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Commit 79a9af26 authored by Nelly Barret's avatar Nelly Barret
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[AM] documentation + improved fusion

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......@@ -49,20 +49,23 @@ To tackle these challenges, social science reasearchers have defined six environ
| Houses | | Countryside | Upper middle | Rural | East |
| Mixed | | | Upper | | ... |
In order to facilitate the comparison of neighbourhoods,
These variables facilitate the comparison of neighbourhoods.
In order to predict the environment of any neighbourhood in France, social science researchers have manually assessed 300 IRIS (note that assessment requires 1 to 2 hours per IRIS). These assessed IRIS are used as training data in supervised algorithms.
# Features
If you want to implement your own algorithm and test it on our dataset, it is possible! You will have to perform the following steps:
1. Create a new class that represents your algorithm. This class must extends the class `Method`, i.e. `class NewAlgorithm(MethodPrediction):`.
2. Then, your algorithm must implemented `fit()` and `predict()` functions.
2. Then, your algorithm must implemented `fit()` and `predict()` functions.
3. You can now use your algorithm on our dataset by create a newx instance of Data and another of Dataset. Do not forget to init them by calling the function (`init_all_in_one()`).
Below, you will find a very simple example to illustrate the aforementionned steps.
```python
# file ./classes/NewAlgorithm.py
class NewAlgorithm(MethodPrediction):
class NewAlgorithm(Method):
def __init__(self, _bar=1):
self.foo = 0
self.bar = _bar
......@@ -89,50 +92,13 @@ if name == "__main__":
Alice is an IT commercial, therefore she often moves across the whole country. She is recruited for a mission in Lyon for 6 months before going back to Paris. Alice would like to find a neighbourhood which is urban, near from shops and, if possible, near from a gym. She knows from her friends that the Part-Dieu neighbourhood is in the CBD (Central Business District) but she prefers to compare it with others before having her final decision. With Predihood, Alice writes the query "Lyon" in the search bar. Then she compare several neighbourhoods using the environment variables and selects a few that she might like. So she compares in detail two neighbourhoods: "Part-Dieu" and "Danton Bir-Akeim". The first one has a lot of shops and services, illustrated by three grouped indicators: "indicateurs service-divers-prive" (i.e. private services such as banks, driving schools or travel agencies), "service-divers-public" (i.e. public services such as post offices or direction of public finances) and "animation-commerce-nonalimentaire" (i.e. shops such as clothing stores, hairdressers or appliance stores). The second one has a gym as shown by the indicators "salles multisports" (i.e. multisport hall) and "salles de remise en forme" (i.e. fitness room). Alice prefers to be near from shops and will go to the gym by bike. Finally, she selects the "Part-Dieu" IRIS for finding an appartment. Moreover, our tool provides a confidence score, which corresponds to the number of lists of relevant indicators which have predict the selected value. For example, all lists have predict the IRIS "Part-Dieu" as a "towers" neighbourhood (score at 7/7).
![Screenshot of Predihood](predihood-predictions.png)
## Bob, an IT professor
Bob is an associate professor in artifical intelligence at the University of Lyon. He works on a new supervised learning algorithm and on improving some existing supervised learning algorithms. First, he would like to test the impact of some parameters on the performances. To achieve this, he adds his proposals in our Predihood tool and test a lot of different configurations. He can easily tune his algorithms with the tuning panel. For example he changes with several values the number of neiighbourhoods for his improved version of the KNN algorithm. Then we would like to run his algorithms on another dataset than his to test his robustess. To achieve this, he runs his new algorithm on ouur daatset composed of assessed IRIS. Finally, Bob teaches a machine learning course and he plans to give a practical course on Scikit-learn algorithms. With Predihood, Bob's students use the interface to learn basic parameters and their influence on results. Moreover, they can export their results as Excel in order to have a detailed experiments section.
# Statement of Need
When studying geographical areas such as neighborhoods, it is necessary to collect various data according to the application domain (e.g., types of neighborhood, statistics such as criminality or unmployment rates, points of interests).
For instance, social science researchers study the relationship between citizens and their living area [@preteceille2009segregation;@authier2008citadins] or how they describe their neighborhood [@airbnb2017]. Computer science researchers are interested in recommending the most relevant neighborhood when buying a house [@RealEstate2013], in predicting price and types of neighborhoods [@tang2015neighborhood] or in detecting similar areas between different cities [@le2015soho].
National institutions (e.g., Open Data initiatives, INSEE in France) may produce data about neighborhoods, but they are usually spread in heterogenous files (databases, spreadsheets). Initiatives such as DataFrance [@datafrance] enable their visualization on a map, but their authors do not share collected data.
Thus, researchers have to manually collect and integrate raw data from national institutions, a challenging issue refered to as `data integration` [@christen2012data]. Although some tools such as OpenRefine or Talend facilitates this integration, they require expert knowledge and programming skills. Besides, spatial queries (e.g., neighborhoods located within a close distance), which are useful in a research context, are usually not directly available.
The French administration provides data about IRIS [@insee-iris], a small division unit of the national territory for statistical purposes (mostly with the same number of residents, thus mainly small-sized in cities and wider in rural areas).
To ease the exploitation of IRIS, we propose the package Mongiris, which includes integrated data about these neighborhoods (IRIS) and an API for manipulating them.
# Summary
The Python package is composed of two modules: integration and API.
The `integration module` is responsible for extracting information from data sources. The module currently supports spreadsheets produced by [INSEE](https://www.insee.fr/).
Since data evolve (e.g., statistics from INSEE are updated every few years), the integration module may be run. Note that new data may be stored in different database or collections so that the evolution can be studied.
For most users, there is no need to use the integration module since a recent dump of the database is provided. It is mainly based on INSEE files from 2014 and 2016.
The current dump contains roughly 37,000 IRIS with 375 indicators and 12,800 IRIS with 640 indicators.
<!-- {362: 36530, 650: 11738, 627: 1057, 385: 79} -->
The `API module` includes common operations such as searching for an IRIS (by IRIS code or according to any field value), inserting, updating or deleting an IRIS.
It also provides geospatial operations: get IRIS from coordinates, get all adjacent or close IRIS from a given IRIS, find all IRIS in a given area, etc.
The Mongiris package is currently used in Mapiris, a tool for visualizing and searching for IRIS.
![Screenshot of Mapiris.](mongiris/data/img/screenshot-mapiris.jpg)
It also powers VizLiris, a prototype for clustering similar IRIS or for recommending relevant neighborhoods according to user needs [@egc19-demo].
<!--
![Screenshot of VizLiris - clustering.](mongiris/data/img/screenshot-vizliris-clustering.png){ width=50%}
![Screenshot of VizLiris - recommendation.](mongiris/data/img/screenshot-vizliris-recommandation.png){ width=50% }
-->
![](mongiris/data/img/screenshot-vizliris-clustering.png){ width=50%} &nbsp;\ ![](mongiris/data/img/screenshot-vizliris-recommandation.png){ width=50% }
\begin{figure}[!h]
\caption{Screenshot of VizLiris - clustering (left) and recommendation (right)}
\end{figure}
![Screenshot of Predihood](predihood-accuracies.png)
# Acknowledgements
......
predihood-accuracies.png

484 KiB

predihood-predictions.png

5.15 MiB

import logging
logging.basicConfig(level="INFO")
\ No newline at end of file
import sys
logging.basicConfig(level=logging.DEBUG)
\ No newline at end of file
......@@ -9,11 +9,10 @@ from area import area
from predihood import model
from predihood.cleaning import clean
from predihood.config import FILE_CLEANED_DATA, FOLDER_DATASETS, ENVIRONMENT_VARIABLES, FILE_GROUPING, NEW_PREFIX, \
OLD_PREFIX
from predihood.config import FILE_CLEANED_DATA, FOLDER_DATASETS, ENVIRONMENT_VARIABLES, FILE_GROUPING, NEW_PREFIX, OLD_PREFIX
from predihood.utility_functions import address_to_code, append_indicator, append_target
logging.basicConfig(level="INFO")
log = logging.getLogger(__name__)
warnings.filterwarnings("ignore", category=RuntimeWarning)
......@@ -21,9 +20,8 @@ class Data:
def __init__(self, normalization="density", filtering=True):
"""
Constructor of the Data class. Initialize attributes.
Args:
normalization: a string to indicate the choice for normalization ("density" for density, "population" for population and None to don't normalize)
normalization: A string to indicate the choice for normalization ("density" for density, "population" for population and None to do not normalize)
filtering: True or False to indicate if useless indicators will be removed or not
"""
self.old_path = FILE_CLEANED_DATA
......@@ -36,13 +34,13 @@ class Data:
# retrieve indicators
self.get_indicators()
logging.debug("Starting with %d indicators", len(self.indicators))
# these indicators are not relevant for the prediction
indicators_to_remove = ["IRIS", "REG", "DEP", "UU2010", 'COM', 'LIBCOM', 'TRIRIS', 'GRD_QUART', 'LIBIRIS', 'TYP_IRIS', 'MODIF_IRIS', 'LAB_IRIS', 'LIB_IRIS', 'LIB_COM', 'CODGEO', 'LIBGEO']
log.debug("Starting with %d indicators", len(self.indicators))
# define indicators that are not relevant for the prediction and remove them
indicators_to_remove = ["IRIS", "REG", "DEP", "UU2010", "COM", "LIBCOM", "TRIRIS", "GRD_QUART", "LIBIRIS", "TYP_IRIS", "MODIF_IRIS", "LAB_IRIS", "LIB_IRIS", "LIB_COM", "CODGEO", "LIBGEO"]
for indicator in indicators_to_remove:
if indicator in self.indicators:
self.indicators.remove(indicator)
logging.debug("Descriptive indicators: %d. It remains %d indicators", len(indicators_to_remove), len(self.indicators))
log.debug("Descriptive indicators: %d. It remains %d indicators", len(indicators_to_remove), len(self.indicators))
def get_indicators(self):
"""
......@@ -50,10 +48,9 @@ class Data:
"""
if self.data is not None:
self.indicators = self.data.columns.tolist()
# self.data.columns gets all columns, so to get indicators, we remove 'CODE', 'AREA', and the 6 environment variables
if 'CODE' in self.indicators: self.indicators.remove('CODE')
# if 'AREA' in self.indicators: self.indicators.remove('AREA')
# if 'DENSITY' in self.indicators: self.indicators.remove('DENSITY')
# self.data.columns gets all columns, so to get indicators, we remove "CODE" (since it is not relevant for prediction).
# We keep "AREA" and "DENSITY" since they are use as features in prediction
if "CODE" in self.indicators: self.indicators.remove("CODE")
for env in ENVIRONMENT_VARIABLES:
if env in self.indicators:
self.indicators.remove(env)
......@@ -62,23 +59,20 @@ class Data:
def init_all_in_one(self):
"""
Main method to create a new dataset. The dataset is created if the corresponding file does not exist, else the dataset is read.
Apply filtering and/or normalization if needed.
Create or read the dataset. It applies normalization and/or filtering if needed.
"""
if not os.path.exists(self.dataset_path):
self.create()
self.do_normalize()
if self.filtering: self.do_filter()
self.apply_normalization()
if self.filtering: self.apply_filtering()
elif not os.path.exists(self.filtered_path):
if self.filtering: self.do_filter()
if self.filtering: self.apply_filtering()
else:
self.read()
logging.debug("Finally, the dataset has %d columns including %d INSEE indicators", len(self.data.columns), len(self.indicators))
logging.debug("The dataset has %d IRIS", len(self.data))
def read(self):
"""
Read the dataset stored in the CSV file (and get indicators form this dataset).
Read the dataset stored in the CSV file (and get indicators from this dataset).
"""
if self.filtering:
self.data = pd.read_csv(self.filtered_path)
......@@ -88,127 +82,158 @@ class Data:
def create(self):
"""
Construct the dataset from HiL data. Ungroup lines, retrieve information about IRIS and construct a CSV file.
Construct the dataset from HomeInLove data, i.e. ungroup addresses, retrieve information about IRIS and construct a CSV file.
"""
logging.info("... construction of dataset is in progress ...")
raw_data = []
append_col = True
null_iris = 0 # IRIS that can't be retrieve from its code
foreigners = 0 # addresses that are not in France
log.info("... construction of dataset is in progress ...")
# 1. read data to transform
if not os.path.exists(self.old_path): clean()
data = pd.read_csv(self.old_path)
# 2. define some variables
raw_data = []
append_col = True
null_iris = 0 # count IRIS that can't be retrieve from its code
foreigners = 0 # count addresses that are not located in France
problems = 0 # count IRIS that encounter problems and that will be not added to the dataset
cols = []
columns = ["address", "country"]
columns.extend(ENVIRONMENT_VARIABLES)
cols_departure = [OLD_PREFIX+env for env in columns]
cols_arrival = [NEW_PREFIX+env for env in columns]
# get data for departure and arrival IRIS
departures = data[cols_departure] # [:4]
arrivals = data[cols_arrival] # [:4]
# 3. get data for departure and arrival IRIS
departures = data[cols_departure]
arrivals = data[cols_arrival]
# remove "old_address", "old_country", "new_address", "new_country"
cols_departure.pop(0)
cols_departure.pop(0)
cols_arrival.pop(0)
cols_arrival.pop(0)
cols.append("CODE")
cols.append("AREA")
cols.append("DENSITY")
# for each departure and arrival IRIS (one line in cleanedData.csv), get the INSEE indicators and construct the
# data from it.
logging.debug("Constructing dataset on %d INSEE indicators", len(self.indicators))
# 4. for each departure and arrival IRIS (one line in cleanedData.csv), get its INSEE indicators and its EV.
# for each IRIS store its code, compute and store its area and its density, store all its INSEE indicators and its 6 EV.
for (index1, row1), (index2, row2) in zip(departures.iterrows(), arrivals.iterrows()):
dep_row = []
arr_row = []
dep_row, arr_row = [], []
# convert address to IRIS code and append it to data only if the address is in France
if row1[OLD_PREFIX + 'country'] == 'France':
dep_code = address_to_code(row1[OLD_PREFIX + 'address'])
if row1[OLD_PREFIX + "country"] == "France":
dep_code = address_to_code(row1[OLD_PREFIX + "address"])
dep_row.append(dep_code)
dep_iris = model.get_iris_from_code(dep_code) # get IRIS information
coords_dep = model.get_coords_from_code(dep_code)
area_dep = area(coords_dep) / 1000000 if coords_dep is not None else None
dep_row.append(area_dep) # compute its area
area_dep = area(coords_dep) / 1000000 if coords_dep is not None else None # convert area from m^2 to km^2
dep_row.append(area_dep) # add area as a feature
density = dep_iris["properties"]["raw_indicators"]["P14_POP"] / area_dep if area_dep is not None and area_dep > 0 else None
dep_row.append(density)
dep_row.append(density) # add density as a feature
if dep_iris is not None:
# 1. append INSEE indicators
# a. append INSEE indicators
for raw_indicator in self.indicators:
dep_row, cols = append_indicator(raw_indicator, dep_iris, dep_row, append_col, cols)
# 2. append targets
# b. append EV
for target in cols_departure:
dep_row = append_target(row1, target, dep_row)
append_col = False
raw_data.append(dep_row) if len(dep_row) > 0 else logging.error("IRIS", dep_code, "have encountered an error. It will not be added to the dataset.")
else:
null_iris += 1
# logging.debug("This IRIS is null. %d IRIS are null.", null_iris)
elif row1[OLD_PREFIX+'country'] == '' or row1[OLD_PREFIX+'country'] is None or row1[OLD_PREFIX+'country'] is np.nan:
null_iris += 1
# logging.debug("This IRIS is null.", null_iris, "IRIS are null.")
else:
foreigners += 1
# logging.debug("This IRIS is a foreigner. %d IRIS are foreigners.", foreigners)
if row2[NEW_PREFIX + 'country'] == 'France':
arr_code = address_to_code(row2[NEW_PREFIX + 'address'])
if len(dep_row) > 0: raw_data.append(dep_row)
else: problems += 1
else: null_iris += 1
elif row1[OLD_PREFIX+"country"] == "" or row1[OLD_PREFIX+"country"] is None or row1[OLD_PREFIX+"country"] is np.nan: null_iris += 1
else: foreigners += 1
if row2[NEW_PREFIX + "country"] == "France":
arr_code = address_to_code(row2[NEW_PREFIX + "address"])
arr_row.append(arr_code)
arr_iris = model.get_iris_from_code(arr_code)
coords_arr = model.get_coords_from_code(arr_code)
area_arr = area(coords_arr) / 1000000 if coords_arr is not None else None
arr_row.append(area_arr) # compute its area
area_arr = area(coords_arr) / 1000000 if coords_arr is not None else None # convert area from m^2 to km^2
arr_row.append(area_arr) # add area as a feature
density = arr_iris["properties"]["raw_indicators"]["P14_POP"] / area_arr if area_arr is not None and area_arr > 0 else None
arr_row.append(density)
arr_row.append(density) # add density as a feature
if arr_iris is not None:
# 1. append INSEE indicators
# a. append INSEE indicators
for raw_indicator in self.indicators:
arr_row, cols = append_indicator(raw_indicator, arr_iris, arr_row, append_col, cols)
# 2. append targets
# b. append targets
for target in cols_arrival:
arr_row = append_target(row2, target, arr_row)
append_col = False
raw_data.append(arr_row) if len(arr_row) > 0 else logging.error("IRIS", arr_code, "have encountered an error. It will not be added to the dataset.")
else:
null_iris += 1
# logging.debug("This IRIS is null. %d IRIS are null.", null_iris)
elif row2[NEW_PREFIX + 'country'] == '' or row2[NEW_PREFIX + 'country'] is None or row2[NEW_PREFIX + 'country'] is np.nan:
null_iris += 1
# logging.debug("This IRIS is null. %d IRIS are null.", null_iris)
else:
foreigners += 1
# logging.debug("This IRIS is a foreigner. %d IRIS are foreigners.", foreigners)
if len(arr_row) > 0: raw_data.append(arr_row)
else: problems += 1
else: null_iris += 1
elif row2[NEW_PREFIX + "country"] == "" or row2[NEW_PREFIX + "country"] is None or row2[NEW_PREFIX + "country"] is np.nan: null_iris += 1
else: foreigners += 1
sys.stdout.write("\r%.2f%%" % ((index1 * 100) / len(departures))) # update progress percentage
sys.stdout.flush()
print()
cols.extend(ENVIRONMENT_VARIABLES)
logging.debug("columns:", cols)
logging.debug("%d null IRIS have been removed from the dataset.", null_iris)
logging.debug("%d addresses are not located in France.", foreigners)
log.info("%d addresses are not located in France.", foreigners)
log.info("%d null IRIS have been removed from the dataset.", null_iris)
log.info("%d IRIS have encountered problems.", problems)
# 5. convert IRIS data into a DataFrame, fill missing values and remove fully empty columns.
self.data = pd.DataFrame(raw_data, columns=cols)
self.data.sort_values("CODE", inplace=True)
self.fill_nan("median")
self.fill_missing_values("median")
nan_columns = self.data.columns[self.data.isna().all()].tolist()
logging.debug("There are %d NaN columns", len(nan_columns))
old_size = len(self.data.columns)
log.debug("There are %d NaN columns", len(nan_columns))
self.data.drop(nan_columns, axis=1, inplace=True)
self.indicators = [indicator for indicator in self.indicators if indicator not in nan_columns] # remove names of NaN columns in the list of indicators
logging.debug("New size without NaN columns: %d (old size: %d)", (len(self.data.columns) - 8), old_size - len(nan_columns))
# self.data.to_csv(self.dataset_path, index=None) # index=None: avoid line numbering
def filter_grouping(self):
def fill_missing_values(self, method="median"):
"""
Remove overly detailed indicators from the dataset. The list of indicators to remove is given by the file `regrouping.csv`.
Fill NaN values given the method.
Args:
method: A string corresponding tto the method for filling NaN values ("zero", "mean" or "median"). Default is median.
"""
assert method in ["zero", "mean", "median"]
cols = ["CODE", "AREA", "DENSITY"]
cols.extend(ENVIRONMENT_VARIABLES)
for col in self.data.iteritems():
value = 0
col_name = col[0]
if col_name not in cols:
# fill missing INSEE indicators
if method == "zero":
value = 0
elif method == "mean":
value = self.data[col_name].mean()
elif method == "median":
value = self.data[col_name].median()
else:
value = np.mean(self.data[col_name])
elif col_name not in ["CODE", "AREA", "DENSITY"]:
# fill missing EV
if method == "zero":
env_values = {"building_type": "Housing estates",
"building_usage": "Other activities",
"landscape": "Urban",
"morphological_position": "Central",
"geographical_position": "Centre",
"social_class": "Lower"}
else: # method == "mean" or method == "median"
env_values = {"building_type": "Mixed",
"building_usage": "Other activities",
"landscape": "Green areas",
"morphological_position": "Urban",
"geographical_position": "Centre",
"social_class": "Middle"}
value = env_values[col_name]
# else: -> column is CODE or AREA or DENSITY -> do nothing
self.data[col_name].replace([np.nan], [value], inplace=True)
def filter_too_detailed_indicators(self):
"""
Remove too detailed indicators from the dataset. The list of indicators is given by the file `regrouping.csv`.
"""
regrouping = pd.read_csv(FILE_GROUPING, sep="\t")
status = regrouping.index[regrouping['STATUS'] == 1].tolist() # indicators that are an element of a subset
status = regrouping.index[regrouping["STATUS"] == 1].tolist() # indicators that are an element of a subset
col_names = [regrouping.iloc[status]["INDICATOR"] for status in status]
# self.filtered_data = self.data[:] # copy by value, not by reference
counter1, counter2 = 0, 0
......@@ -217,31 +242,31 @@ class Data:
del self.data[column]
self.indicators.remove(column)
counter1 += 1
logging.debug("%d indicators have been removed thanks to the regrouping.", counter1)
log.debug("%d indicators have been using regrouping.", counter1)
def do_filter(self):
def apply_filtering(self):
"""
Remove (filter) overly detailed indicators and constant columns from the dataset.
Remove (filter) too detailed indicators and constant columns from the dataset.
"""
if self.data is None:
self.data = pd.read_csv(self.dataset_path)
self.get_indicators()
# 1. remove indicators that have been defined as useless
self.filter_grouping()
self.filter_too_detailed_indicators()
# 2. remove constant columns, i.e. with a null variance
constant_columns = self.data.columns[self.data.nunique() == 1].tolist()
old_size = len(self.data.columns)
self.data.drop(constant_columns, axis=1, inplace=True)
self.indicators = [indicator for indicator in self.indicators if indicator not in constant_columns]
self.data.to_csv(self.filtered_path, index=None) # index=None: avoid line numbering
def do_normalize(self):
def apply_normalization(self):
"""
Normalize the dataset according to the chosen method, i.e. None, population or density. Default is density.
Normalize the dataset with the given method, i.e. None, "population" or "density". Default is "density".
"""
assert self.normalization in [None, "population", "density"]
do_not_normalize = ["CODE", "AREA", "DENSITY", "P14_POP"]
do_not_normalize.extend(ENVIRONMENT_VARIABLES) # extend does not return a list
for index, row in self.data.iterrows():
......@@ -249,123 +274,11 @@ class Data:
col_name = column[0]
if col_name not in do_not_normalize:
if self.normalization == "population":
self.data.at[index, col_name] = row[col_name] / row['P14_POP']
self.data.at[index, col_name] = row[col_name] / row["P14_POP"]
elif self.normalization == "density":
density = row[self.data.columns.get_loc('DENSITY')]
density = row[self.data.columns.get_loc("DENSITY")]
self.data.at[index, col_name] = row[col_name] / density if density > 0 else 0
elif self.normalization is None:
self.data.at[index, col_name] = row[col_name]
else:
raise Exception("You must normalize by 'pop' or 'density'. Apply None to don't normalize.")
self.data.to_csv(self.dataset_path, index=None) # index=None: avoid line numbering
def fill_nan(self, method):
"""
Fill NaN cells according to the chosen method, i.e. zero, mean or median.
Args:
method: a string that indicates the method to fill NaN values. Default is mean.
"""
cols = ["CODE", "AREA", "DENSITY"]
cols.extend(ENVIRONMENT_VARIABLES)
for col in self.data.iteritems():
value = 0
col_name = col[0]
if col_name not in cols:
# INSEE variable
if method == 'zero':
value = 0
elif method == 'mean':
value = self.data[col_name].mean()
elif method == 'median':
value = self.data[col_name].median()
else:
value = np.mean(self.data[col_name])
elif col_name not in ["CODE", "AREA", "DENSITY"]:
# environment variable
if method == 'zero':
env_values = {"building_type": "Housing estates",
"building_usage": "Other activities",
"landscape": "Urban",
"morphological_position": "Central",
"geographical_position": "Centre",
"social_class": "Lower"}
else: # method == 'mean' or method == 'median' or else
env_values = {"building_type": "Mixed",
"building_usage": "Other activities",
"landscape": "Green areas",
"morphological_position": "Urban",
"geographical_position": "Centre",
"social_class": "Middle"}
value = env_values[col_name]
# else: -> column is CODE or AREA or DENSITY -> do nothing
self.data[col_name].replace([np.nan], [value], inplace=True)
def generate_from_cities(self, list_of_iris):
"""
"""
if os.path.exists("small_dataset.csv"):
self.data = pd.read_csv("small_dataset.csv")
else:
raw_data = []
append_indicator = True
cols = []
cols.append("CODE")
cols.append("CITY")
cols.append("AREA")
cols.append("DENSITY")
i = 0
for request, targets in list_of_iris.items():
row = []
postal_code = address_to_code(request)
row.append(postal_code)
row.append(request)
iris = model.get_iris_from_code(postal_code) # get IRIS information
coords = model.get_coords_from_code(postal_code)
area_iris = area(coords) / 1000000 if coords is not None else None # in km^2 instead of m^2
row.append(area_iris) # compute its area
density = iris["properties"]["raw_indicators"]["P14_POP"] / area_iris if area_iris is not None and area_iris > 0 else None
row.append(density)
if iris is not None:
# 1. append INSEE indicators
for raw_indicator in self.indicators:
if raw_indicator in iris["properties"]["raw_indicators"]:
value = iris["properties"]["raw_indicators"][raw_indicator]
row.append(value)
else:
row.append(np.nan)
if append_indicator: cols.append(raw_indicator)
# 2. append targets
row.extend(targets)
append_indicator = False
raw_data.append(row) if len(row) > 0 else logging.error("IRIS", postal_code, "have encountered an error. It will not be added to the dataset.")
else:
logging.debug("This IRIS is null.")
sys.stdout.write("\r%.2f%%" % ((i * 100) / len(list_of_iris))) # update progress percentage
sys.stdout.flush()
i += 1
cols.extend(ENVIRONMENT_VARIABLES)
self.data = pd.DataFrame(raw_data, columns=cols)
self.data.sort_values("CODE", inplace=True)
self.data.to_csv("small_dataset.csv", index=None) # index=None: avoid line numbering
if __name__ == '__main__':
data = Data()
cities = {
# "tassin": ["maisons", "résidentiel", "espaces verts", "périurbain", "ouest lyon", "moyen-sup"],
# "107 rue jean voillot villeurbanne": ["grands ensembles", "autres activités", "urbanisé", "urbain", "est lyon", "popu"],
"saint cyr au mont d'or": ["maisons", "résidentiel", "arboré", "périurbain", "ouest lyon", "sup"],
# "doua villeurbanne": ["immeubles", "autres activités", "urabnisé", "central", "est-lyon", "moyen-inf"],
"part dieu lyon": ["immeubles", "commerçant", "urbanisé", "centrtal", "centre lyon", "moyen"],
# "dompierre sur besbre": ["maisons", "résidentiel", "arboré", "rural", "est moulins", "sup"],
# "rue de la favorite lyon": ["mixte", "commercant", "urbanisé", "urbain", "nord-ouest lyon", "moyen"],
# "lezoux": ["maisons", "résidentiel", "arboré", "rural", "est clermont-ferrand", "moyen-sup"]
}
data.generate_from_cities(cities)
......@@ -5,7 +5,7 @@ from predihood.config import TRAIN_SIZE, TEST_SIZE, ENVIRONMENT_VARIABLES, RANDO
from sklearn.ensemble import IsolationForest
from sklearn.model_selection import train_test_split
logging.basicConfig(level="INFO")
log = logging.getLogger(__name__)
class Dataset:
......@@ -106,7 +106,7 @@ class Dataset:
for i in range(len(predictions)):
if predictions[i] == -1:
code = self.data.loc[i, "CODE"]
logging.debug(code, "has been removed since it is an outlier.")
log.debug(code, "has been removed since it is an outlier.")
self.data = self.data.drop(i, axis=0) # delete rows which are detected as outliers
def remove_rural_iris(self):
......
......@@ -10,7 +10,7 @@ from predihood.classes.Method import Method
from predihood.config import ENVIRONMENT_VARIABLES, FILE_CLEANED_DATA, FOLDER_DISTRIBUTION, OLD_PREFIX, NEW_PREFIX
from predihood.utility_functions import sim, auto_label
logging.basicConfig(level="INFO")
log = logging.getLogger(__name__)
warnings.simplefilter(action='ignore', category=FutureWarning)
......@@ -56,8 +56,7 @@ class MethodCleaning(Method):
"""
Clean data from bad naming conventions.
"""
logging.info(
"The data needs to be cleaned. For each list, write the correct word. For each environment variable, you will get its number of corrections and its error rate.")
log.info("The data needs to be cleaned. For each list, write the correct word. For each environment variable, you will get its number of corrections and its error rate.")
# 1. getting wrong values in a dict ordered by env variable
self.values_by_env = {}
for col_dep, col_arr in zip(self.columns_dep, self.columns_arr):
......@@ -95,18 +94,18 @@ class MethodCleaning(Method):
self.dataset.loc[self.dataset[col_name_new] == label, col_name_new] = chosen_label
size = int(self.dataset.count()[OLD_PREFIX + key]) + int(self.dataset.count()[NEW_PREFIX + key])
mean_error = ((nb_replacement_dep + nb_replacement_arr) / size) * 100
logging.debug("%d IRIS have been corrected for the environment variable %s, corresponding to an error rate of %.0f %%", (nb_replacement_dep + nb_replacement_arr), key, mean_error)
log.debug("%d IRIS have been corrected for the environment variable %s, corresponding to an error rate of %.0f %%", (nb_replacement_dep + nb_replacement_arr), key, mean_error)
# 3. removing outliers from data
count = 0
for outlier in self.outliers:
self.dataset.drop(self.dataset[self.dataset.eq(outlier).any(1)].index, inplace=True)
count += 1
logging.debug("%d outliers removed", count)
log.debug("%d incorrect values removed", count)
# 4. save data
self.dataset.to_csv(FILE_CLEANED_DATA, index=False, encoding='utf-8')
logging.info("Cleaned data is in %s", FILE_CLEANED_DATA)
log.info("Cleaned data is in %s", FILE_CLEANED_DATA)
def create_before_after_labels(self, name_dep, name_arr):
"""
......
......@@ -10,7 +10,7 @@ from sklearn.metrics import auc, roc_curve
from sklearn.model_selection import cross_val_score, GridSearchCV
from sklearn.tree import DecisionTreeClassifier
logging.basicConfig(level="INFO")
log = logging.getLogger(__name__)
class MethodPrediction(Method):
......@@ -67,4 +67,4 @@ class MethodPrediction(Method):
df = pd.DataFrame([iris_indicators_values], columns=iris_indicators_names)
self.prediction = self.classifier.predict(df)[0]
print(self.prediction)
log.debug("%s", self.prediction)
......@@ -62,7 +62,6 @@ class MethodSelection(Method):
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))
self.best_indicators = []
print(upper)
for i in range(len(upper.columns)):
column = upper.columns[i]
for k, value in upper[column].items():
......
CODE,AREA,DENSITY,P14_POP,P14_POP0002,P14_POP0305,P14_POP0610,P14_POP1117,P14_POP1824,P14_POP2539,P14_POP4054,P14_POP5564,P14_POP6579,P14_POP80P,P14_POP0014,P14_POP1529,P14_POP3044,P14_POP4559,P14_POP6074,P14_POP75P,P14_POP0019,P14_POP2064,P14_POP65P,P14_POPH,P14_H0014,P14_H1529,P14_H3044,P14_H4559,P14_H6074,P14_H75P,P14_H0019,P14_H2064,P14_H65P,P14_POPF,P14_F0014,P14_F1529,P14_F3044,P14_F4559,P14_F6074,P14_F75P,P14_F0019,P14_F2064,P14_F65P,C14_POP15P,C14_POP15P_CS1,C14_POP15P_CS2,C14_POP15P_CS3,C14_POP15P_CS4,C14_POP15P_CS5,C14_POP15P_CS6,C14_POP15P_CS7,C14_POP15P_CS8,C14_H15P,C14_H15P_CS1,C14_H15P_CS2,C14_H15P_CS3,C14_H15P_CS4,C14_H15P_CS5,C14_H15P_CS6,C14_H15P_CS7,C14_H15P_CS8,C14_F15P,C14_F15P_CS1,C14_F15P_CS2,C14_F15P_CS3,C14_F15P_CS4,C14_F15P_CS5,C14_F15P_CS6,C14_F15P_CS7,C14_F15P_CS8,P14_POP_FR,P14_POP_ETR,P14_POP_IMM,P14_PMEN,P14_PHORMEN,P14_LOG,P14_RP,P14_RSECOCC,P14_LOGVAC,P14_MAISON,P14_APPART,P14_RP_1P,P14_RP_2P,P14_RP_3P,P14_RP_4P,P14_RP_5PP,P14_NBPI_RP,P14_RPMAISON,P14_NBPI_RPMAISON,P14_RPAPPART,P14_NBPI_RPAPPART,P14_RP_M30M2,P14_RP_3040M2,P14_RP_4060M2,P14_RP_6080M2,P14_RP_80100M2,P14_RP_100120M2,P14_RP_120M2P,P14_RP_ACHTOT,P14_RP_ACH19,P14_RP_ACH45,P14_RP_ACH70,P14_RP_ACH90,P14_RP_ACH05,P14_RP_ACH11,P14_RPMAISON_ACHTOT,P14_RPMAISON_ACH19,P14_RPMAISON_ACH45,P14_RPMAISON_ACH70,P14_RPMAISON_ACH90,P14_RPMAISON_ACH05,P14_RPMAISON_ACH11,P14_RPAPPART_ACHTOT,P14_RPAPPART_ACH19,P14_RPAPPART_ACH45,P14_RPAPPART_ACH70,P14_RPAPPART_ACH90,P14_RPAPPART_ACH05,P14_RPAPPART_ACH11,P14_MEN,P14_MEN_ANEM0002,P14_MEN_ANEM0204,P14_MEN_ANEM0509,P14_MEN_ANEM10P,P14_PMEN_ANEM0002,P14_PMEN_ANEM0204,P14_PMEN_ANEM0509,P14_PMEN_ANEM10P,P14_NBPI_RP_ANEM0002,P14_NBPI_RP_ANEM0204,P14_NBPI_RP_ANEM0509,P14_NBPI_RP_ANEM10P,P14_RP_PROP,P14_RP_LOC,P14_RP_LOCHLMV,P14_RP_GRAT,P14_NPER_RP,P14_NPER_RP_PROP,P14_NPER_RP_LOC,P14_NPER_RP_LOCHLMV,P14_NPER_RP_GRAT,P14_ANEM_RP,P14_ANEM_RP_PROP,P14_ANEM_RP_LOC,P14_ANEM_RP_LOCHLMV,P14_ANEM_RP_GRAT,P14_RP_SDB,P14_RP_CCCOLL,P14_RP_CCIND,P14_RP_CINDELEC,P14_RP_ELEC,P14_RP_EAUCH,P14_RP_BDWC,P14_RP_CHOS,P14_RP_CLIM,P14_RP_TTEGOU,P14_RP_GARL,P14_RP_VOIT1P,P14_RP_VOIT1,P14_RP_VOIT2P,NB_B101,NB_B102,NB_B103,NB_B201,NB_B202,NB_B203,NB_B204,NB_B205,NB_B206,NB_B301,NB_B302,NB_B303,NB_B304,NB_B305,NB_B306,NB_B307,NB_B308,NB_B309,NB_B310,NB_B311,NB_B312,NB_B313,NB_B315,NB_B316,DEC_PIMP14,DEC_TP6014,DEC_Q114,DEC_MED14,DEC_Q314,DEC_EQ14,DEC_D114,DEC_D214,DEC_D314,DEC_D414,DEC_D614,DEC_D714,DEC_D814,DEC_D914,DEC_RD14,DEC_S80S2014,DEC_GI14,DEC_PTSA14,DEC_PCHO14,DEC_PBEN14,DEC_PPEN14,DEC_PAUT14,note,NB_D201,NB_D202,NB_D203,NB_D204,NB_D205,NB_D206,NB_D207,NB_D208,NB_D209,NB_D210,NB_D211,NB_D212,NB_D213,NB_D221,NB_D231,NB_D232,NB_D233,NB_D235,NB_D236,NB_D237,NB_D238,NB_D239,NB_D240,NB_D242,NB_D243,C401,C402,C403,C409,C501,C502,C503,C504,C505,C505_NB_INT,C509,C601,C602,C603,C604,C605,C605_NB_INT,C609,C701,C702,C101,C101_NB_CANT,C101_NB_EP,C101_NB_RPIC,C102,C102_NB_CANT,C102_NB_EP,C104,C104_NB_CANT,C104_NB_PELEM,C104_NB_EP,C104_NB_RPIC,C105,C105_NB_CANT,C105_NB_PELEM,C105_NB_EP,NB_F101,NB_F101_NB_AIREJEU,NB_F101_NB_COU,NB_F101_NB_ECL,NB_F102,NB_F102_NB_AIREJEU,NB_F102_NB_COU,NB_F102_NB_ECL,NB_F103,NB_F103_NB_AIREJEU,NB_F103_NB_COU,NB_F103_NB_ECL,NB_F104,NB_F104_NB_AIREJEU,NB_F104_NB_COU,NB_F104_NB_ECL,NB_F105,NB_F105_NB_AIREJEU,NB_F105_NB_COU,NB_F105_NB_ECL,NB_F106,NB_F106_NB_AIREJEU,NB_F106_NB_COU,NB_F106_NB_ECL,NB_F107,NB_F107_NB_AIREJEU,NB_F107_NB_COU,NB_F107_NB_ECL,NB_F108,NB_F108_NB_AIREJEU,NB_F108_NB_COU,NB_F108_NB_ECL,NB_F109,NB_F109_NB_AIREJEU,NB_F109_NB_COU,NB_F109_NB_ECL,NB_F110,NB_F110_NB_AIREJEU,NB_F110_NB_COU,NB_F110_NB_ECL,NB_F111,NB_F111_NB_AIREJEU,NB_F111_NB_COU,NB_F111_NB_ECL,NB_F112,NB_F112_NB_AIREJEU,NB_F112_NB_COU,NB_F112_NB_ECL,NB_F113,NB_F113_NB_AIREJEU,NB_F113_NB_COU,NB_F113_NB_ECL,NB_F114,NB_F114_NB_AIREJEU,NB_F114_NB_COU,NB_F114_NB_ECL,NB_F116,NB_F116_NB_AIREJEU,NB_F116_NB_COU,NB_F116_NB_ECL,NB_F117,NB_F117_NB_AIREJEU,NB_F117_NB_COU,NB_F117_NB_ECL,NB_F118,NB_F118_NB_AIREJEU,NB_F118_NB_COU,NB_F118_NB_ECL,NB_F119,NB_F119_NB_AIREJEU,NB_F119_NB_COU,NB_F119_NB_ECL,NB_F120,NB_F120_NB_AIREJEU,NB_F120_NB_COU,NB_F120_NB_ECL,NB_F121,NB_F121_NB_AIREJEU,NB_F121_NB_COU,NB_F121_NB_ECL,NB_F201,NB_F201_NB_AIREJEU,NB_F202,NB_F202_NB_AIREJEU,NB_F203,NB_F203_NB_AIREJEU,NB_F302,NB_F302_NB_SALLES,NB_F303,NB_F303_NB_SALLES,NB_F304,NB_F305,NB_A101,NB_A104,NB_A105,NB_A106,NB_A107,NB_A108,NB_A109,NB_A115,NB_A119,NB_A120,NB_A121,NB_A122,NB_A123,NB_A124,NB_A125,NB_A203,NB_A205,NB_A206,NB_A207,NB_A208,NB_A301,NB_A302,NB_A303,NB_A304,NB_A401,NB_A402,NB_A403,NB_A404,NB_A405,NB_A406,NB_A501,NB_A502,NB_A503,NB_A504,NB_A505,NB_A506,NB_A507,P14_POP1564,P14_POP1524,P14_POP2554,P14_H1564,P14_H1524,P14_H2554,P14_H5564,P14_F1564,P14_F1524,P14_F2554,P14_F5564,P14_ACT1564,P14_ACT1524,P14_ACT2554,P14_ACT5564,P14_HACT1564,P14_HACT1524,P14_HACT2554,P14_HACT5564,P14_FACT1564,P14_FACT1524,P14_FACT2554,P14_FACT5564,P14_ACTOCC1564,P14_ACTOCC1524,P14_ACTOCC2554,P14_ACTOCC5564,P14_HACTOCC1564,P14_HACTOCC1524,P14_HACTOCC2554,P14_HACTOCC5564,P14_FACTOCC1564,P14_FACTOCC1524,P14_FACTOCC2554,P14_FACTOCC5564,P14_CHOM1564,P14_CHOM1524,P14_CHOM2554,P14_CHOM5564,P14_HCHOM1564,P14_FCHOM1564,P14_INACT1564,P14_HINACT1564,P14_FINACT1564,P14_ETUD1564,P14_HETUD1564,P14_FETUD1564,P14_RETR1564,P14_HRETR1564,P14_FRETR1564,P14_AINACT1564,P14_HAINACT1564,P14_FAINACT1564,C14_ACT1564,C14_ACT1564_CS1,C14_ACT1564_CS2,C14_ACT1564_CS3,C14_ACT1564_CS4,C14_ACT1564_CS5,C14_ACT1564_CS6,C14_ACTOCC1564,C14_ACTOCC1564_CS1,C14_ACTOCC1564_CS2,C14_ACTOCC1564_CS3,C14_ACTOCC1564_CS4,C14_ACTOCC1564_CS5,C14_ACTOCC1564_CS6,P14_ACTOCC15P,P14_HACTOCC15P,P14_FACTOCC15P,P14_SAL15P,P14_HSAL15P,P14_FSAL15P,P14_NSAL15P,P14_HNSAL15P,P14_FNSAL15P,P14_ACTOCC15P_TP,P14_SAL15P_TP,P14_HSAL15P_TP,P14_FSAL15P_TP,P14_NSAL15P_TP,P14_SAL15P_CDI,P14_SAL15P_CDD,P14_SAL15P_INTERIM,P14_SAL15P_EMPAID,P14_SAL15P_APPR,P14_NSAL15P_INDEP,P14_NSAL15P_EMPLOY,P14_NSAL15P_AIDFAM,P14_ACTOCC15P_ILT1,P14_ACTOCC15P_ILT2P,P14_ACTOCC15P_ILT2,P14_ACTOCC15P_ILT3,P14_ACTOCC15P_ILT4,P14_ACTOCC15P_ILT5,C14_ACTOCC15P,C14_ACTOCC15P_PAS,C14_ACTOCC15P_MAR,C14_ACTOCC15P_DROU,C14_ACTOCC15P_VOIT,C14_ACTOCC15P_TCOM,C201,C201_NB_CANT,C201_NB_EP,C201_NB_INT,C301,C301_NB_CANT,C301_NB_PGE,C301_NB_EP,C301_NB_INT,C302,C302_NB_CANT,C302_NB_PGE,C302_NB_EP,C302_NB_INT,C303,C303_NB_PGE,C303_NB_INT,C304,C304_NB_CANT,C304_NB_INT,C305,C305_NB_CANT,C305_NB_INT,NB_D401,NB_D402,NB_D403,NB_D404,NB_D405,NB_D502,NB_D601,NB_D602,NB_D603,NB_D604,NB_D605,NB_D606,NB_D701,NB_D702,NB_D703,NB_D704,NB_D705,NB_D709,NB_E101,NB_E102,NB_E103,NB_E106,NB_G101,NB_G102,NB_G103,NB_G104,C14_MEN,C14_MENPSEUL,C14_MENHSEUL,C14_MENFSEUL,C14_MENSFAM,C14_MENFAM,C14_MENCOUPSENF,C14_MENCOUPAENF,C14_MENFAMMONO,C14_PMEN,C14_PMEN_MENPSEUL,C14_PMEN_MENHSEUL,C14_PMEN_MENFSEUL,C14_PMEN_MENSFAM,C14_PMEN_MENFAM,C14_PMEN_MENCOUPSENF,C14_PMEN_MENCOUPAENF,C14_PMEN_MENFAMMONO,P14_POP15P,P14_POP5579,P14_POPMEN15P,P14_POPMEN1524,P14_POPMEN2554,P14_POPMEN5579,P14_POPMEN80P,P14_POP15P_PSEUL,P14_POP1524_PSEUL,P14_POP2554_PSEUL,P14_POP5579_PSEUL,P14_POP80P_PSEUL,P14_POP15P_MARIEE,P14_POP15P_NONMARIEE,C14_MEN_CS1,C14_MEN_CS2,C14_MEN_CS3,C14_MEN_CS4,C14_MEN_CS5,C14_MEN_CS6,C14_MEN_CS7,C14_MEN_CS8,C14_PMEN_CS1,C14_PMEN_CS2,C14_PMEN_CS3,C14_PMEN_CS4,C14_PMEN_CS5,C14_PMEN_CS6,C14_PMEN_CS7,C14_PMEN_CS8,C14_FAM,C14_COUPAENF,C14_FAMMONO,C14_COUPSENF,C14_NE24F0,C14_NE24F1,C14_NE24F2,C14_NE24F3,C14_NE24F4P,P14_POP0205,P14_POP1114,P14_POP1517,P14_POP2529,P14_POP30P,P14_SCOL0205,P14_SCOL0610,P14_SCOL1114,P14_SCOL1517,P14_SCOL1824,P14_SCOL2529,P14_SCOL30P,P14_NSCOL15P,P14_NSCOL15P_DIPLMIN,P14_NSCOL15P_CAPBEP,P14_NSCOL15P_BAC,P14_NSCOL15P_SUP,P14_HNSCOL15P,P14_HNSCOL15P_DIPLMIN,P14_HNSCOL15P_CAPBEP,P14_HNSCOL15P_BAC,P14_HNSCOL15P_SUP,P14_FNSCOL15P,P14_FNSCOL15P_DIPLMIN,P14_FNSCOL15P_CAPBEP,P14_FNSCOL15P_BAC,P14_FNSCOL15P_SUP,C15_POP01P_IRAN012,C15_POP01P_IRANAUT,C15_POP01P,building_type,building_usage,landscape,morphological_position,geographical_position,social_class
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CODE,CITY,AREA,DENSITY,P14_POP,P14_POP0002,P14_POP0305,P14_POP0610,P14_POP1117,P14_POP1824,P14_POP2539,P14_POP4054,P14_POP5564,P14_POP6579,P14_POP80P,P14_POP0014,P14_POP1529,P14_POP3044,P14_POP4559,P14_POP6074,P14_POP75P,P14_POP0019,P14_POP2064,P14_POP65P,P14_POPH,P14_H0014,P14_H1529,P14_H3044,P14_H4559,P14_H6074,P14_H75P,P14_H0019,P14_H2064,P14_H65P,P14_POPF,P14_F0014,P14_F1529,P14_F3044,P14_F4559,P14_F6074,P14_F75P,P14_F0019,P14_F2064,P14_F65P,C14_POP15P,C14_POP15P_CS1,C14_POP15P_CS2,C14_POP15P_CS3,C14_POP15P_CS4,C14_POP15P_CS5,C14_POP15P_CS6,C14_POP15P_CS7,C14_POP15P_CS8,C14_H15P,C14_H15P_CS1,C14_H15P_CS2,C14_H15P_CS3,C14_H15P_CS4,C14_H15P_CS5,C14_H15P_CS6,C14_H15P_CS7,C14_H15P_CS8,C14_F15P,C14_F15P_CS1,C14_F15P_CS2,C14_F15P_CS3,C14_F15P_CS4,C14_F15P_CS5,C14_F15P_CS6,C14_F15P_CS7,C14_F15P_CS8,P14_POP_FR,P14_POP_ETR,P14_POP_IMM,P14_PMEN,P14_PHORMEN,P14_LOG,P14_RP,P14_RSECOCC,P14_LOGVAC,P14_MAISON,P14_APPART,P14_RP_1P,P14_RP_2P,P14_RP_3P,P14_RP_4P,P14_RP_5PP,P14_NBPI_RP,P14_RPMAISON,P14_NBPI_RPMAISON,P14_RPAPPART,P14_NBPI_RPAPPART,P14_RP_M30M2,P14_RP_3040M2,P14_RP_4060M2,P14_RP_6080M2,P14_RP_80100M2,P14_RP_100120M2,P14_RP_120M2P,P14_RP_ACHTOT,P14_RP_ACH19,P14_RP_ACH45,P14_RP_ACH70,P14_RP_ACH90,P14_RP_ACH05,P14_RP_ACH11,P14_RPMAISON_ACHTOT,P14_RPMAISON_ACH19,P14_RPMAISON_ACH45,P14_RPMAISON_ACH70,P14_RPMAISON_ACH90,P14_RPMAISON_ACH05,P14_RPMAISON_ACH11,P14_RPAPPART_ACHTOT,P14_RPAPPART_ACH19,P14_RPAPPART_ACH45,P14_RPAPPART_ACH70,P14_RPAPPART_ACH90,P14_RPAPPART_ACH05,P14_RPAPPART_ACH11,P14_MEN,P14_MEN_ANEM0002,P14_MEN_ANEM0204,P14_MEN_ANEM0509,P14_MEN_ANEM10P,P14_PMEN_ANEM0002,P14_PMEN_ANEM0204,P14_PMEN_ANEM0509,P14_PMEN_ANEM10P,P14_NBPI_RP_ANEM0002,P14_NBPI_RP_ANEM0204,P14_NBPI_RP_ANEM0509,P14_NBPI_RP_ANEM10P,P14_RP_PROP,P14_RP_LOC,P14_RP_LOCHLMV,P14_RP_GRAT,P14_NPER_RP,P14_NPER_RP_PROP,P14_NPER_RP_LOC,P14_NPER_RP_LOCHLMV,P14_NPER_RP_GRAT,P14_ANEM_RP,P14_ANEM_RP_PROP,P14_ANEM_RP_LOC,P14_ANEM_RP_LOCHLMV,P14_ANEM_RP_GRAT,P14_RP_SDB,P14_RP_CCCOLL,P14_RP_CCIND,P14_RP_CINDELEC,P14_RP_ELEC,P14_RP_EAUCH,P14_RP_BDWC,P14_RP_CHOS,P14_RP_CLIM,P14_RP_TTEGOU,P14_RP_GARL,P14_RP_VOIT1P,P14_RP_VOIT1,P14_RP_VOIT2P,NB_B101,NB_B102,NB_B103,NB_B201,NB_B202,NB_B203,NB_B204,NB_B205,NB_B206,NB_B301,NB_B302,NB_B303,NB_B304,NB_B305,NB_B306,NB_B307,NB_B308,NB_B309,NB_B310,NB_B311,NB_B312,NB_B313,NB_B315,NB_B316,DEC_PIMP14,DEC_TP6014,DEC_Q114,DEC_MED14,DEC_Q314,DEC_EQ14,DEC_D114,DEC_D214,DEC_D314,DEC_D414,DEC_D614,DEC_D714,DEC_D814,DEC_D914,DEC_RD14,DEC_S80S2014,DEC_GI14,DEC_PTSA14,DEC_PCHO14,DEC_PBEN14,DEC_PPEN14,DEC_PAUT14,note,NB_D201,NB_D202,NB_D203,NB_D204,NB_D205,NB_D206,NB_D207,NB_D208,NB_D209,NB_D210,NB_D211,NB_D212,NB_D213,NB_D221,NB_D231,NB_D232,NB_D233,NB_D235,NB_D236,NB_D237,NB_D238,NB_D239,NB_D240,NB_D242,NB_D243,C401,C402,C403,C409,C501,C502,C503,C504,C505,C505_NB_INT,C509,C601,C602,C603,C604,C605,C605_NB_INT,C609,C701,C702,C101,C101_NB_CANT,C101_NB_EP,C101_NB_RPIC,C102,C102_NB_CANT,C102_NB_EP,C104,C104_NB_CANT,C104_NB_PELEM,C104_NB_EP,C104_NB_RPIC,C105,C105_NB_CANT,C105_NB_PELEM,C105_NB_EP,NB_F101,NB_F101_NB_AIREJEU,NB_F101_NB_COU,NB_F101_NB_ECL,NB_F102,NB_F102_NB_AIREJEU,NB_F102_NB_COU,NB_F102_NB_ECL,NB_F103,NB_F103_NB_AIREJEU,NB_F103_NB_COU,NB_F103_NB_ECL,NB_F104,NB_F104_NB_AIREJEU,NB_F104_NB_COU,NB_F104_NB_ECL,NB_F105,NB_F105_NB_AIREJEU,NB_F105_NB_COU,NB_F105_NB_ECL,NB_F106,NB_F106_NB_AIREJEU,NB_F106_NB_COU,NB_F106_NB_ECL,NB_F107,NB_F107_NB_AIREJEU,NB_F107_NB_COU,NB_F107_NB_ECL,NB_F108,NB_F108_NB_AIREJEU,NB_F108_NB_COU,NB_F108_NB_ECL,NB_F109,NB_F109_NB_AIREJEU,NB_F109_NB_COU,NB_F109_NB_ECL,NB_F110,NB_F110_NB_AIREJEU,NB_F110_NB_COU,NB_F110_NB_ECL,NB_F111,NB_F111_NB_AIREJEU,NB_F111_NB_COU,NB_F111_NB_ECL,NB_F112,NB_F112_NB_AIREJEU,NB_F112_NB_COU,NB_F112_NB_ECL,NB_F113,NB_F113_NB_AIREJEU,NB_F113_NB_COU,NB_F113_NB_ECL,NB_F114,NB_F114_NB_AIREJEU,NB_F114_NB_COU,NB_F114_NB_ECL,NB_F116,NB_F116_NB_AIREJEU,NB_F116_NB_COU,NB_F116_NB_ECL,NB_F117,NB_F117_NB_AIREJEU,NB_F117_NB_COU,NB_F117_NB_ECL,NB_F118,NB_F118_NB_AIREJEU,NB_F118_NB_COU,NB_F118_NB_ECL,NB_F119,NB_F119_NB_AIREJEU,NB_F119_NB_COU,NB_F119_NB_ECL,NB_F120,NB_F120_NB_AIREJEU,NB_F120_NB_COU,NB_F120_NB_ECL,NB_F121,NB_F121_NB_AIREJEU,NB_F121_NB_COU,NB_F121_NB_ECL,NB_F201,NB_F201_NB_AIREJEU,NB_F202,NB_F202_NB_AIREJEU,NB_F203,NB_F203_NB_AIREJEU,NB_F302,NB_F302_NB_SALLES,NB_F303,NB_F303_NB_SALLES,NB_F304,NB_F305,NB_A101,NB_A104,NB_A105,NB_A106,NB_A107,NB_A108,NB_A109,NB_A115,NB_A119,NB_A120,NB_A121,NB_A122,NB_A123,NB_A124,NB_A125,NB_A203,NB_A205,NB_A206,NB_A207,NB_A208,NB_A301,NB_A302,NB_A303,NB_A304,NB_A401,NB_A402,NB_A403,NB_A404,NB_A405,NB_A406,NB_A501,NB_A502,NB_A503,NB_A504,NB_A505,NB_A506,NB_A507,P14_POP1564,P14_POP1524,P14_POP2554,P14_H1564,P14_H1524,P14_H2554,P14_H5564,P14_F1564,P14_F1524,P14_F2554,P14_F5564,P14_ACT1564,P14_ACT1524,P14_ACT2554,P14_ACT5564,P14_HACT1564,P14_HACT1524,P14_HACT2554,P14_HACT5564,P14_FACT1564,P14_FACT1524,P14_FACT2554,P14_FACT5564,P14_ACTOCC1564,P14_ACTOCC1524,P14_ACTOCC2554,P14_ACTOCC5564,P14_HACTOCC1564,P14_HACTOCC1524,P14_HACTOCC2554,P14_HACTOCC5564,P14_FACTOCC1564,P14_FACTOCC1524,P14_FACTOCC2554,P14_FACTOCC5564,P14_CHOM1564,P14_CHOM1524,P14_CHOM2554,P14_CHOM5564,P14_HCHOM1564,P14_FCHOM1564,P14_INACT1564,P14_HINACT1564,P14_FINACT1564,P14_ETUD1564,P14_HETUD1564,P14_FETUD1564,P14_RETR1564,P14_HRETR1564,P14_FRETR1564,P14_AINACT1564,P14_HAINACT1564,P14_FAINACT1564,C14_ACT1564,C14_ACT1564_CS1,C14_ACT1564_CS2,C14_ACT1564_CS3,C14_ACT1564_CS4,C14_ACT1564_CS5,C14_ACT1564_CS6,C14_ACTOCC1564,C14_ACTOCC1564_CS1,C14_ACTOCC1564_CS2,C14_ACTOCC1564_CS3,C14_ACTOCC1564_CS4,C14_ACTOCC1564_CS5,C14_ACTOCC1564_CS6,P14_ACTOCC15P,P14_HACTOCC15P,P14_FACTOCC15P,P14_SAL15P,P14_HSAL15P,P14_FSAL15P,P14_NSAL15P,P14_HNSAL15P,P14_FNSAL15P,P14_ACTOCC15P_TP,P14_SAL15P_TP,P14_HSAL15P_TP,P14_FSAL15P_TP,P14_NSAL15P_TP,P14_SAL15P_CDI,P14_SAL15P_CDD,P14_SAL15P_INTERIM,P14_SAL15P_EMPAID,P14_SAL15P_APPR,P14_NSAL15P_INDEP,P14_NSAL15P_EMPLOY,P14_NSAL15P_AIDFAM,P14_ACTOCC15P_ILT1,P14_ACTOCC15P_ILT2P,P14_ACTOCC15P_ILT2,P14_ACTOCC15P_ILT3,P14_ACTOCC15P_ILT4,P14_ACTOCC15P_ILT5,C14_ACTOCC15P,C14_ACTOCC15P_PAS,C14_ACTOCC15P_MAR,C14_ACTOCC15P_DROU,C14_ACTOCC15P_VOIT,C14_ACTOCC15P_TCOM,C201,C201_NB_CANT,C201_NB_EP,C201_NB_INT,C301,C301_NB_CANT,C301_NB_PGE,C301_NB_EP,C301_NB_INT,C302,C302_NB_CANT,C302_NB_PGE,C302_NB_EP,C302_NB_INT,C303,C303_NB_PGE,C303_NB_INT,C304,C304_NB_CANT,C304_NB_INT,C305,C305_NB_CANT,C305_NB_INT,NB_D401,NB_D402,NB_D403,NB_D404,NB_D405,NB_D502,NB_D601,NB_D602,NB_D603,NB_D604,NB_D605,NB_D606,NB_D701,NB_D702,NB_D703,NB_D704,NB_D705,NB_D709,NB_E101,NB_E102,NB_E103,NB_E106,NB_G101,NB_G102,NB_G103,NB_G104,C14_MEN,C14_MENPSEUL,C14_MENHSEUL,C14_MENFSEUL,C14_MENSFAM,C14_MENFAM,C14_MENCOUPSENF,C14_MENCOUPAENF,C14_MENFAMMONO,C14_PMEN,C14_PMEN_MENPSEUL,C14_PMEN_MENHSEUL,C14_PMEN_MENFSEUL,C14_PMEN_MENSFAM,C14_PMEN_MENFAM,C14_PMEN_MENCOUPSENF,C14_PMEN_MENCOUPAENF,C14_PMEN_MENFAMMONO,P14_POP15P,P14_POP5579,P14_POPMEN15P,P14_POPMEN1524,P14_POPMEN2554,P14_POPMEN5579,P14_POPMEN80P,P14_POP15P_PSEUL,P14_POP1524_PSEUL,P14_POP2554_PSEUL,P14_POP5579_PSEUL,P14_POP80P_PSEUL,P14_POP15P_MARIEE,P14_POP15P_NONMARIEE,C14_MEN_CS1,C14_MEN_CS2,C14_MEN_CS3,C14_MEN_CS4,C14_MEN_CS5,C14_MEN_CS6,C14_MEN_CS7,C14_MEN_CS8,C14_PMEN_CS1,C14_PMEN_CS2,C14_PMEN_CS3,C14_PMEN_CS4,C14_PMEN_CS5,C14_PMEN_CS6,C14_PMEN_CS7,C14_PMEN_CS8,C14_FAM,C14_COUPAENF,C14_FAMMONO,C14_COUPSENF,C14_NE24F0,C14_NE24F1,C14_NE24F2,C14_NE24F3,C14_NE24F4P,P14_POP0205,P14_POP1114,P14_POP1517,P14_POP2529,P14_POP30P,P14_SCOL0205,P14_SCOL0610,P14_SCOL1114,P14_SCOL1517,P14_SCOL1824,P14_SCOL2529,P14_SCOL30P,P14_NSCOL15P,P14_NSCOL15P_DIPLMIN,P14_NSCOL15P_CAPBEP,P14_NSCOL15P_BAC,P14_NSCOL15P_SUP,P14_HNSCOL15P,P14_HNSCOL15P_DIPLMIN,P14_HNSCOL15P_CAPBEP,P14_HNSCOL15P_BAC,P14_HNSCOL15P_SUP,P14_FNSCOL15P,P14_FNSCOL15P_DIPLMIN,P14_FNSCOL15P_CAPBEP,P14_FNSCOL15P_BAC,P14_FNSCOL15P_SUP,C15_POP01P_IRAN012,C15_POP01P_IRANAUT,C15_POP01P,batiment,usage,paysage,morpho,geo,social
691910102,saint cyr au mont d'or,4.06781786426077,818.9902708867584,3331.5032545689232,76.98330410345714,94.0121782522999,192.7938487419909,354.41397156414564,245.1770212756093,357.00381297661073,713.263797944255,507.3596377952264,497.0909370642007,293.4047448511275,562.1319794820487,499.2701541681201,467.0941808457472,752.1863552978557,638.7426370622669,412.07794771288457,808.0815067275795,1732.9260659260156,790.4956819153282,1593.073723621543,295.5112345336855,249.24531697002104,219.3935353372964,377.5181682383789,320.47361010216594,130.9318584399953,418.3425590722418,847.6085405071798,327.12262404212146,1738.42953094738,266.6207449483631,250.02483719809905,247.7006455084508,374.6681870594769,318.269026960101,281.1460892728893,389.73894765533765,885.3175254188357,463.37305787320673,2751.9418128591647,0.0,139.5119473040038,553.9657665633968,336.0556070672835,217.6735164466612,111.4752082247064,888.6807028520788,504.5790644010343,1287.4786697711336,0.0,103.59851615645248,354.5056486758734,140.6771280359918,40.71317015062986,92.19952469712165,379.2069234208927,176.57775863417177,1464.463143088031,0.0,35.91343114755132,199.46011788752332,195.3784790312917,176.96034629603136,19.27568352758476,509.473779431186,328.0013057668625,3177.9843409897644,153.51891357915878,239.20781977552735,3068.50325456905,262.99999999987295,1313.090376619586,1212.1398512255405,19.37434325744307,81.5761821366024,1077.946836522431,234.0799284358077,23.28282977624894,59.95765855944054,111.84748661472337,171.54066534701406,845.5112109281137,6595.721542353153,993.311547555706,5892.932616530212,217.7646920084872,700.6617025002478,14.83224987216634,25.29422559543401,82.58020916620792,99.89523008082888,107.97894369417416,164.18520658539072,717.3737862313386,1204.0864061112472,237.27628124378148,70.60248504157653,186.578220562609,418.3299383010319,221.1081550408162,70.1913259214321,988.3906240387588,183.19849629375489,39.44346108013154,145.09591986664918,347.80804060751484,208.83332928325555,64.01137690745288,214.63217041114103,54.0777849500266,30.0954123000977,41.482300695959815,70.52189769351706,12.274825757560649,6.179949013979219,1212.1398512255405,97.02487203199485,168.12529216530135,178.97046359161854,768.0192234366258,272.522787772369,495.6932428716353,535.1985558801227,1765.088668044923,448.85403427246166,791.8970367260811,1005.1783675213128,4349.792103833298,963.2040186994706,231.53842742777792,70.25978588738072,17.39740509829207,3068.50325456905,2490.275358410852,537.1147184570765,177.60831423912063,41.11317770112238,22162.13110306449,19559.05051847977,2241.200093088324,728.1030695947321,361.8804914963963,1187.3845694475408,51.6345243204203,748.389111246978,347.84173361648743,,,,,,,1049.4308380640457,1141.2219301995124,416.0097081562998,725.2122220432126,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1978.8755931715464,401.24834445545423,1070.2676109208658,970.439865045736,202.13522014539183,519.4546354839516,248.8500094163926,1008.4357281258102,199.1131243100624,550.812975436914,258.50962837883384,1333.4407263806077,97.81195155710104,944.9837555582292,290.6450192652774,713.2029379190811,55.297493825990415,490.4481902041478,167.45725388894292,620.2377884615266,42.51445773111062,454.5355653540815,123.1877653763345,1229.442903312818,78.49024090075537,874.536350606717,276.41631180534546,672.637634458645,42.54616231433884,468.6723582416975,161.41911390260864,556.8052688541728,35.94407858641652,405.8639923650194,114.99719790273684,103.99782306778992,19.321710656345665,70.44740495151231,14.228707459931927,40.56530346043612,63.43251960735378,645.4348667909386,257.2369271266549,388.19793966428364,293.3406931551036,138.72817501076304,154.6125181443405,151.66096776884763,76.33235566218295,75.32861210666466,200.4332058669874,42.17639645370893,158.25680941327846,1332.987280432584,0.0,135.37412743200608,541.4356801740122,323.63895037176263,209.53569657466545,111.4752082247064,1205.9966171747835,0.0,135.37412743200608,533.3837903794777,287.84534307713045,173.41726215101582,75.97609413515352,1267.7755259498167,696.4340731617262,571.3414527880905,879.9296917333102,429.8023597805172,450.127331952793,387.8458342165065,266.63171338120907,121.21412083529746,244.4131258731119,191.95238849187783,38.05504710327213,153.89734138860572,52.46073738123404,789.1147448658825,61.59545080321692,5.988211183394492,1.06361166134729,22.16767321946903,144.10810001650475,237.6420800968537,6.095654103148068,246.3281136408,1021.4474123090166,928.5161497388538,51.33804878872739,35.69223937441055,5.900974407024968,1243.2190000036824,44.06933467988882,48.72512017319361,75.19720720978589,934.6336538372904,140.5936841035237,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1205.9076367475232,259.52124536873754,93.59782619856108,165.92341917017646,24.06047246218702,922.3259189165984,429.8826573505746,427.9612394733864,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lyon,sup
693830105,part dieu lyon,0.1820333661663812,15759.391669306415,2868.735114298272,89.37744289683285,65.00699410511523,109.62493164848075,122.07945082526108,735.8326244731194,810.9626712227148,424.0727553309982,260.4090809465158,168.39066145916664,82.9785013900671,334.28848339567253,1150.9618330736075,612.2084187812508,360.5117433522781,290.5456874999086,120.21894819555433,576.9422168501824,2040.423734598856,251.36916284923373,1394.3254883457255,202.00157054606476,511.48486032394726,348.6057000587006,163.6968232188202,138.37672460428192,30.159809593910737,296.5920313360928,1001.3665002391252,96.3669567705074,1474.4096259525463,132.28691284960777,639.4769727496604,263.6027187225501,196.81492013345792,152.16896289562663,90.05913860164358,280.3501855140896,1039.0572343597305,155.00220607872637,2539.446647504612,0.0,83.05034145413771,704.6283321387307,367.9730180325022,301.5039050583068,98.29824375952228,280.9214476164014,703.0713594450111,1190.3239111588557,0.0,66.79925356984343,429.32811797789793,155.34266625722324,86.32380403170538,88.58748225003595,100.65025773255093,263.2923293395987,1349.1227363457565,0.0,16.25108788429428,275.3002141608327,212.6303517752789,215.18010102660145,9.71076150948633,180.2711898838505,439.7790301054124,2626.512218439074,242.22289585919785,289.78110452173945,2868.735114298272,0.0,2033.0057366276155,1727.6961236904133,72.51386285485307,232.7957500823489,12.99558709500999,1997.3270298885109,455.3031007775552,452.9081339919927,420.1044999056224,210.65345002830784,188.7269389869352,4536.366166871877,3.27921785928091,19.67530715568546,1701.733786187038,4484.250333559404,366.4608570658795,252.65845929839023,328.6350073608472,289.3083232227793,221.9387581994257,160.98074356446452,107.71397497862677,1726.6961203700107,711.7676503833012,248.84570759798265,342.0029261707826,138.47530080033238,238.50857808471028,47.09595733290157,3.27921785928091,3.27921785928091,0.0,0.0,0.0,0.0,0.0,1700.7337828666357,705.1541626604536,248.84570759798265,342.0029261707826,134.09659920418693,232.53845978395128,38.09592744927835,1727.6961236904133,587.7833091919883,468.9477339313985,248.4127165579665,422.5523640090599,863.5758953233284,792.8286982166152,443.1827935498193,769.147727208509,1232.2039909922064,1153.130015050683,671.0987893472782,1479.9333714817094,497.90300616018993,1200.4002848689925,35.980204073780584,29.392832661230837,2868.735114298272,1102.7384329386257,1723.431925257006,60.51774450229238,42.56475610264027,13884.26496100124,7588.178204669629,6003.503267147241,285.0317593185571,292.5834891843726,1671.907929380486,212.44066033881904,706.0195833544321,777.9259719739744,,,,,,,223.21536959241448,825.7687175096696,676.4558695050845,149.31284800458508,0.0,0.0,0.0,2.0,1.0,4.0,2.0,0.0,0.0,2.0,8.0,4.0,0.0,4.0,23.0,9.0,1.0,1.0,0.0,0.0,1.0,2.0,2.0,0.0,71.96261682242991,12.001533742331288,18828.0,28173.0,41694.0,0.8116281546161218,10830.0,16470.0,21246.0,24884.0,33140.0,37952.0,45216.0,59378.0,5.482733148661127,7.747861598465689,0.3730561919,62.0,2.9,8.4,18.9,7.8,0.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,3.0,0.0,0.0,0.0,0.0,0.0,9.0,1.0,8.0,5.0,1.0,0.0,2.0,0.0,0.0,0.0,0.0,5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,2.0,0.0,2.0,1.0,1.0,14.0,0.0,8.0,37.0,8.0,3.0,2.0,2283.077468053365,787.6329605531367,1235.035426553713,1095.9569610291533,344.6983155609278,636.9973828423216,114.26126262590398,1187.1205070242122,442.934644992209,598.0380437113914,146.14781832061186,1558.169007507554,244.4077543264409,1159.3234843966927,154.4377687844203,827.4088491521577,114.2007023105375,621.5038263584864,91.7043204831338,730.7601583553961,130.2070520159034,537.8196580382063,62.73344830128651,1365.820059414279,185.70677338100185,1051.452402876774,128.6608831565034,719.7509042837581,82.15966475419236,564.56807781224,73.02316171732575,646.069155130521,103.54710862680946,486.8843250645338,55.637721439177646,192.3489480932748,58.70098094543907,107.8710815199188,25.77688562791692,107.65794486839962,84.69100322487517,724.9084605458119,268.5481118769957,456.36034866881613,567.9395385943797,236.6618635518527,331.27767504252694,71.00509629187721,22.55694214277017,48.44815414910704,85.96382565955487,9.329306182372768,76.6345194771821,1567.169037391177,0.0,83.05034145413771,695.0175973374423,367.9730180325022,298.1202231789545,94.91456188017,1365.820059414279,0.0,66.8261189300018,673.4690655161534,311.48921548351336,250.79667305088915,63.23898643372136,1382.198157974272,732.7453209643986,649.4528370098733,1189.944178700894,604.6719658819795,585.2722128189143,192.25397927337812,128.07335508241917,64.18062419095898,261.28212181538515,220.806279100421,59.90833834137228,160.89794075904874,40.47584271496414,946.4513977439236,154.84727326786,5.324309297153036,6.368741029731784,76.95245736222516,125.95320661084472,65.30575294574027,0.995019716793168,850.7106854468644,531.4874725274075,412.0701172408793,63.55855233570655,36.37911189965891,19.47969105116274,1382.198157974272,50.70868711012296,239.43800818482876,158.08859251167772,366.9227093250352,567.0401608426073,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,7.0,2.0,0.0,1.0,1732.696140292426,1035.8733713346055,456.1517101502505,579.721661184355,143.15984111691432,553.6629278409063,274.3232693312335,212.31103281765792,67.02862569201488,2881.7351574635054,1035.8733713346055,456.1517101502505,579.721661184355,345.5049202812469,1500.356865847653,548.646538662467,798.4541528542068,153.25617433097915,2534.4466309025997,428.7997424056824,2534.4466309025997,787.6329605531369,1235.035426553713,428.7997424056824,82.9785013900671,1034.873368014203,407.778054914005,413.7232067473779,156.11966095623248,57.2524453965876,652.2472127747718,1882.199418127828,0.0,69.9784447209263,507.3180935551661,252.8943610579161,204.480654531788,81.97399452718834,203.568046322732,412.4825455767093,0.0,182.7379529128382,1057.4133976874537,398.7674831379562,261.0021524608823,133.94471327565503,300.616909372834,547.2525486158859,553.6629278409063,212.31103281765792,67.02862569201488,274.3232693312335,296.92962348978926,124.86350133611116,96.19155212323872,32.39903303248624,3.27921785928091,95.30875667136532,70.27911474524369,51.8003360800174,363.3288725204709,1383.4847978289918,68.34126396868191,109.62493164848075,70.27911474524369,47.471046499657525,627.8022039728917,106.34992461173955,7.557892747228328,1745.2655630710822,253.7424506954132,101.59803997966644,214.80155480756025,1175.1235175884424,863.286967270473,100.59808308856296,51.071784797215656,117.98888886200712,593.6282105226874,881.9785958006091,153.14436760685018,50.52625518245078,96.81266594555314,581.495307065755,,,,immeubles,commerçant,urbanisé,centrtal,centre lyon,moyen
......@@ -6,7 +6,7 @@ import pandas as pd
from predihood.classes.MethodCleaning import MethodCleaning
from predihood.config import FILE_DATA_HIL, OLD_PREFIX, NEW_PREFIX, FOLDER_DISTRIBUTION
logging.basicConfig(level="INFO")
log = logging.getLogger(__name__)
def clean():
......@@ -26,10 +26,10 @@ def clean():
# 3. clean data
cleaning = MethodCleaning("cleaning", data)
cleaning.clean()
logging.info("Many plots have be generated in " + FOLDER_DISTRIBUTION)
log.info("Many plots have be generated in " + FOLDER_DISTRIBUTION)
# 4. distribution between women and men
women_men = data.iloc[:, ["sex"]].value_counts()
women_men = data["sex"].value_counts()
labels = "Women", "Men"
sizes = [women_men["Femme"], women_men["Homme"]] # getting number of women and number of men
colors = ["salmon", "lightblue"]
......@@ -43,13 +43,12 @@ def clean():
data_temp = data_temp.dropna()
ages_plot = []
total_plot = []
min_age = int(min(data_temp.iloc[:, ["age"]]))
max_age = int(max(data_temp.iloc[:, ["age"]]))
min_age = int(min(data_temp["age"]))
max_age = int(max(data_temp["age"]))
for counter in range(min_age, max_age + 1):
total_plot.append(data_temp.loc[data_temp.age == float(counter), "age"].count())
ages_plot.append(counter)
mean = np.average(ages_plot, weights=total_plot)
logging.info("Mean age: %.1f", mean)
plt.bar(ages_plot, total_plot)
plt.axvline(x=mean, color="red") # draw median age as a line
......@@ -58,7 +57,7 @@ def clean():
plt.title("Distribution of age")
plt.show()
ages = data_temp.iloc[:, ["age"]]
ages = data_temp["age"]
plt.hist(ages, facecolor="gray", align="mid")
plt.xlabel("Age (in years)")
plt.ylabel("Number of people")
......@@ -66,7 +65,7 @@ def clean():
plt.show()
# 6. distribution between incomes
incomes = data.iloc[:, ["income"]]
incomes = data["income"]
plt.hist(incomes, facecolor="gray", align="mid")
plt.title("Distribution of monthly income")
plt.xlabel("Monthly income (in euros)")
......@@ -74,7 +73,7 @@ def clean():
plt.show()
# 7. distribution between reasons of transfer
transfers = data.iloc[:, ["reason"]].value_counts()
transfers = data["reason"].value_counts()
labels = transfers.index.tolist()
sizes = [transfers[i] for i in range(len(transfers))]
plt.pie(sizes, labels=labels, autopct="%.2f", shadow=True, startangle=90)
......@@ -83,7 +82,7 @@ def clean():
plt.show()
# 8. distribution between geographic positions
geo = pd.concat([data.iloc[:, [OLD_PREFIX + "morphological_position"]], data.iloc[:, [NEW_PREFIX + "morphological_position"]]], ignore_index=True)
geo = pd.concat([data[OLD_PREFIX + "morphological_position"], data[NEW_PREFIX + "morphological_position"]], ignore_index=True)
split_geo = [geo[i].split()[0] if not isinstance(geo[i], float) else "" for i in range(len(geo))]
set_geo = set(split_geo)
uniques = [split_geo.count(elem) for elem in set_geo]
......
......@@ -8,15 +8,15 @@ id,name,HiL_id,sex,age,nb_child,income,monthly_charges,tax_revenue,old_address,o
7,Boiron,593abc1f5d86cf000f237640,Femme,45.0,1.0,7219.0,2100.0,,9 allée des Lilas 37230 Fondettes,France,Hébergé,,4 chemin du Calvaire 42580 La Tour en Jarez,France,Logé par l’employeur,Mutation,"Zone résidentielle maison avec piscine et jardin, lotissement récent, petite commune à l’ouest de Tours. Le lotissement est assez verdoyant et situé proche de vastes parcelles agricoles.",Individuel ,Lotissement,Résidentiel,Agricole,Péri-urbain,Ouest Tours,Moyen-sup,"Petite commune au nord de Saint-Etienne. Grosse maison mitoyenne, lotissement classe moy-sup, zone résidentielle. assez arborée, proche de vastes terrain agricoles.",Lotissement,Résidentiel,Espaces verts,Péri-urbain,Nord Saint-Étienne,Moyen-sup
8,Prouff,594281ba431637000f1912e0,Homme,36.0,0.0,4620.0,1090.0,52271.0,27 rue Gauthey 75017 Paris,France,Locataire,1650.0,22 rue Jérôme Dulaar 69004 Lyon,France,Locataire,Embauche,"17ème arrondissement de Paris, quartier très densifié et peu verdoyant, dans un petit immeuble bourgeois du début de 20ème, avec 4 étages, situé proche d’un petit espace vert, le seul des environs, la cité des fleurs. ",Collectif,Immeubles,Commerçant,Urbanisé,Urbain,Nord Paris,Moyen,"Son nouveau logement, dans le 4ème arrondissement de Lyon est situé dans une zone un peu moins densifié que son logement parisien, avec plus de verdure à proximité, là aussi dans un petit immeuble bourgeois (3étages) du début du 20ème",Immeubles,Résidentiel,Espaces verts,Urbain,Ouest Lyon,Moyen-sup
9,Basson,59495c6028fa18000f2428f4,Homme,33.0,0.0,2835.0,,24395.0,Le Bourg 15 rue de la Courbette 43200 Saint-Jeures,France,Propriétaire,,7 rue de la Présentation 75011 Paris,Paris,Locataire,Mutation,"Petit lieux-dit très peu densifié entouré de terres agricoles et de forêts, zone résidentielle ",Individuel,Maisons,Résidentiel,Agricole,Rural,Nord-Est Puy-en-Velay,Moyen,Appartement dans un immeuble hausmanien de 5 étages proche du parc des Buttes-Chaumond. 11ème arrondissement de Paris. Zone animée. très densifiées. nord-est paris.Peu arboré. ,Immeubles,Commerçant,Urbanisé,Central,Nord-Est Paris,Moyen-sup
10,Pirodeau,594ccee328fa18000f242944,Femme,17.0,0.0,635.0,645.0,19552.0,4 rue Clos des Vignes 86130 Jauney Clan ,France,Locataire,,19 rue de la Gare 33320 Eysines,France,Colocataire,Alternance,"Appart dans grande maison avec parking en terre et grands jardins, qui doit accueillir plusieurs studios. Zone résidentielle assez peu densifiée, assez arborée, à la frontière ouest de la commune. Cette maison est située dans un lotissement des années 1990-2000, plutôt classe moyenne/petites classes moyennes, proximité d’une autoroute (A10) derrière laquelle s’étendent de vastes parcelles agricoles",Collectif,Lotissement,Résidentiel,Espaces verts,Péri-urbain,Nord Poitiers,Petit-moyen,"Appartement commune périphérie nord-ouest de Bordeaux, trois immeubles récents situés en zone résidentielle, immeubles situés proche de maisons avec jardin (dont certaines avec piscine), classe moy-sup, proximité immédiate de grandes parcelles agricoles, zone assez arborée. ",Mixte,Résidentiel,Espaces verts,Péri-urbain,Nord-Ouest Bordeaux,Moyen
10,Pirodeau,594ccee328fa18000f242944,Femme,17.0,0.0,635.0,645.0,19552.0,4 rue Clos des Vignes 86130 Jauney Clan ,France,Locataire,,19 rue de la Gare 33320 Eysines,France,Colocataire,Alternance,"Appart dans grande maison avec parking en terre et grands jardins, qui doit accueillir plusieurs studios. Zone résidentielle assez peu densifiée, assez arborée, à la frontière ouest de la commune. Cette maison est située dans un lotissement des années 1990-2000, plutôt classe moyenne/petites classes moyennes, proximité d’une autoroute (A10) derrière laquelle s’étendent de vastes parcelles agricoles",Collectif,Lotissement,Résidentiel,Espaces verts,Péri-urbain,Nord Poitiers,Moyen-inf,"Appartement commune périphérie nord-ouest de Bordeaux, trois immeubles récents situés en zone résidentielle, immeubles situés proche de maisons avec jardin (dont certaines avec piscine), classe moy-sup, proximité immédiate de grandes parcelles agricoles, zone assez arborée. ",Mixte,Résidentiel,Espaces verts,Péri-urbain,Nord-Ouest Bordeaux,Moyen
11,Flandrin,596380766500d3000f0d6945,Homme,25.0,0.0,711.0,1175.0,28578.0,436 avenue du Petit Barthélémy13090 Aix-en-Provence,France,Hébergé,,10 Villa des Gobelins 75013 Paris,France,Colocataire,Mutation,"Maison au nord-ouest du centre d’Aix-en-Provence située dans une zone mixte principalement composées d’immeubles, grands ensembles à proximité, grands axes routiers à proximité, peut-être aussi activité commerciales type ZAC, un peu arboré à proximité de la maison.",Individuel,Mixte,Résidentiel,Espaces verts,Urbain,Nord-Ouest Aix-en-Provence ,Moyen,"10 Villa des Gobelins, 75013 Immeuble bourgeois fin 19ème de 4 étages proche de nombreux commerces, au sud du centre de Paris. ",Immeubles,Commerçant,Urbanisé,Central,Sud Paris,Moyen-sup
13,Thomas (pas de numéro de dossier),,Homme,23.0,0.0,1348.0,1025.0,8070.0,29 rue Louis Aragon 85000 La Roche-sur-Yon,France,Hébergé,,10 rue de Citeaux 75012 Paris,France,Locataire,Alternance,"Maison avec jardin, proche d’un terrain arboré, située dans un lotissement assez vert, classe moy-sup, sud-est de La-Roche -sur-Yon ",Individuel,Lotissement,Résidentiel,Espaces verts,Urbain,Sud-Est La-Roche-sur-Yon,Moyen-sup,"Immeuble dans une rue qui semble assez calme, plutôt résidentielle, quartier commerçant, centre nord de PAris, proche de la la Place Aligre et de l’hôpital Saint-Antoine",Immeubles,Commerçant,Urbanisé,Central,Nord Paris,Moyen-sup
14,Rouchut,595680b428fa18000f242a14,Homme,32.0,0.0,2823.0,450.0,49379.0,37 allée de la Rue Basse 87350 Panazol,France,Propriétaire,,14 rue André Moinier 63000 Clermont-Ferrand,France,Locataire,Mutation,"Maison situé dans une zone très verdoyante (hameau), dans un corps de ferme avec grands jardins potager. A proximité on trouve un étang et parcelle de terres agricoles (ou de prés) et lotissements résidentiels",Individuel,Maisons,Résidentiel,Arboré,Péri-urbain,Est Limoges,Petit-moyen,"Appartement zone densifiée, centre historique de Clermond-Ferrand, immeuble de 4 étages, proximité commerces",Immeubles,Commerçant,Urbanisé,Central,Centre Clermont-Ferrand,Moyen
14,Rouchut,595680b428fa18000f242a14,Homme,32.0,0.0,2823.0,450.0,49379.0,37 allée de la Rue Basse 87350 Panazol,France,Propriétaire,,14 rue André Moinier 63000 Clermont-Ferrand,France,Locataire,Mutation,"Maison situé dans une zone très verdoyante (hameau), dans un corps de ferme avec grands jardins potager. A proximité on trouve un étang et parcelle de terres agricoles (ou de prés) et lotissements résidentiels",Individuel,Maisons,Résidentiel,Arboré,Péri-urbain,Est Limoges,Moyen-inf,"Appartement zone densifiée, centre historique de Clermond-Ferrand, immeuble de 4 étages, proximité commerces",Immeubles,Commerçant,Urbanisé,Central,Centre Clermont-Ferrand,Moyen
15,Gabriel,596f7bdb329bbd000fcec69a,Homme,21.0,0.0,991.0,634.0,108317.0,150 chemin de la Tuilerie 13320 Bouc-Bel-Air,France,Hébergé,,91 rue Hippolyte Kahn 69100 Villeurbanne,France,Locataire,Alternance,"Grande maison parentale, clôturée par mur et haies d’arbres, avec grand terrain et piscine, est située dans un lotissement classe sup (grandes maisons piscine, etc.), arboré et proche d’une petite forêt. ",Individuel,Maisons,Résidentiel,Espaces verts,Péri-urbain,Nord Marseille,Sup,"Immeubles années 60, dalle béton avec supérette au milieu, style Presqu’île 2, dense, beaucoup d’immeubles assez hauts, proche quartier grattes-ciel. ",Grand ensemble,Résidentiel,Urbanisé,Central,Centre Villeurbanne,Moyen
16,Boissière,595df5dc34e718000fdb9b0f,Femme,56.0,0.0,5775.0,845.0,60097.0,8 rue Edouard Vaillant 87000 Limoges,France,Propriétaire,,60 rue Philippe Castan 34090 Montpellier,France,Locataire,Mutation,"Immeuble années 50 dans le centre historique de Limoges, à proximité de plusieurs commerces",Collectif,Immeubles,Commerçant,Urbanisé,Central,Centre Limoges,Moyen,"Appartement zone mixte, limite périurbain, résidentielle proche lotissements plutôt classe moy/sup proche axes routiers, nord Montpellier",Mixte,Autres activités,Espaces verts,Péri-urbain,Nord Montpellier,Moyen-sup
17,Beye,59653dec6500d3000f0d6991,Femme,22.0,0.0,1156.0,490.0,56693.0,10 avenue Paul Santy 69008 Lyon ,France,Locataire,450.0,39 rue de la République 73200 Albertville ,France,Locataire,Alternance,"immeuble années 50 proche commerces de l’avenue, transport en commun et autres structures (maison de la danse, etc.), proche zone lotissement et zone immeubles. ",Collectif,Mixte,Commerçant,Urbanisé,Urbain,Est Lyon,Moyen,Petit immeuble rue commerçante du centre historique d’Albertville. ,Immeubles,Commerçant,Urbanisé,Central,Centre Albertville,Moyen
18,Chastel ,59672f1d6500d3000f0d6a76,Homme,30.0,0.0,3208.0,,48506.0,7 rue des Layottes 21000 Dijon ,France,Locataire,320.0,26 rue Désirée Clary 13002 Marseille,France,Logé par employeur conjoint,Embauche,"Maison avec terrasse et jardin dans un lotissement assez arboré, classe moy/sup, situé à la périphérie de Dijon et proche de vastes terrains agricoles",Individuel,Lotissement,Résidentiel,Arboré,Péri-urbain,Nord Dijon,Moyen,"Immeuble récent de 6 étages, dans une zone plutôt résidentielle, tout proche des docks et friches industriels de Marseille.",Grand ensemble,Autres activités,Urbanisé,Central,Ouest Marseille,Moyen
19,Metayer,596cbe88887324000f911a2c,Homme,22.0,0.0,1327.0,530.0,,4 rue du Professeur Villemin 33170 Gradignan,France,Locataire,495.0,18 rue de Naudet 33170 Gradignan ,France,Locataire,Alternance,"Barre Immeubles de 5 étages en U, construction récente, avec pelouse en front, dans zone résidentielle proche lotissements maisons, espaces verts et petite forêt.",Collectif,Mixte,Résidentiel,Espaces verts,Péri-urbain,Sud Bordeaux,Moyen,"Nouveau logement situé tout proche de l’ancien dans résidence universitaire, dans une tour, grand ensemble avec pelouse en bas des tours, proche lotissements, espèces verts et petite forêt, contre le périphérique.",Grand ensemble,Résidentiel,Espaces verts,Péri-urbain,Sud Bordeaux,Petit-moyen
19,Metayer,596cbe88887324000f911a2c,Homme,22.0,0.0,1327.0,530.0,,4 rue du Professeur Villemin 33170 Gradignan,France,Locataire,495.0,18 rue de Naudet 33170 Gradignan ,France,Locataire,Alternance,"Barre Immeubles de 5 étages en U, construction récente, avec pelouse en front, dans zone résidentielle proche lotissements maisons, espaces verts et petite forêt.",Collectif,Mixte,Résidentiel,Espaces verts,Péri-urbain,Sud Bordeaux,Moyen,"Nouveau logement situé tout proche de l’ancien dans résidence universitaire, dans une tour, grand ensemble avec pelouse en bas des tours, proche lotissements, espèces verts et petite forêt, contre le périphérique.",Grand ensemble,Résidentiel,Espaces verts,Péri-urbain,Sud Bordeaux,Moyen-inf
20,Flauder,5954e12728fa18000f242a11,Homme,22.0,0.0,1165.0,480.0,14406.0,35 rue du Foix 41000 Blois ,France,Locataire,380.0,7 rue Ropra 59800 Lille,France,Locataire,Alternance,"Petit immeuble bourgeois début 20ème, quartier historique, plutôt résidentiel, proche de la Loire. ",Collectif,Immeubles,Résidentiel,Espaces verts,Central,Centre Blois,Moyen,"Petit immeuble de 2 étages, avec place devant, zone plutôt résidentielles, abords centre historique de Lille",Immeubles,Résidentiel,Urbanisé,Central,Centre Lille,Moyen-sup
21,Romand,5972242146e7ee000f296b21,Homme,48.0,2.0,5287.0,1435.0,,07502 Tipparary Dr Baton Rouge Louisiane 70808 États-Unis,USA,Locataire,1472.0,37 rue Saint Nestor 69008 Lyon ,France,Locataire,Mutation,"USA, maison avec pelouse située dans immense lotissement résidentiel (pas de commerce)(typiquement américain) avec nombreuses maisons type classe moy/sup, beaucoup d’espaces vert et bois à proximité.",Individuel,Lotissement,Résidentiel,Espaces verts,Péri-urbain,Sud Baton Rouge,Moyen-sup,"Quartier résidentiel, barre d’immeubles sécurisés de 9 étages, non loin du métro Monplaisir-Lumière. ",Immeubles,Résidentiel,Espaces verts,Urbain,Est Lyon,Moyen
22,Damboise,597b20b446e7ee000f296f83,Femme,23.0,0.0,1168.0,330.0,60968.0,24 chemin de Charrière Blanche 69130 Ecully ,France,Hébergé,,2 rue Gambetta 73200 Albertville,France,Locataire,Alternance,"Immeuble de 7 étages assez récent en zone résidentielle immeuble et maisons classe moy, zone assez verte et arborée. ",Collectif,Mixte,Résidentiel,Espaces verts,Urbain,Ouest Lyon,Moyen-sup,"Petit immeuble de trois étages sur axe assez commerçant, vue sur les montagnes, à quelques dizaines de mètres de la rivière de l’Arly.",Immeubles,Commerçant,Urbanisé,Central,Centre Albertville,Moyen
......@@ -25,13 +25,13 @@ id,name,HiL_id,sex,age,nb_child,income,monthly_charges,tax_revenue,old_address,o
25,Martin (pas de numéro de dossier),,Homme,21.0,0.0,563.0,295.0,7404.0,336 rue du Moulin 76940 Arelaune-en-Seine,France,Hébergé,,7 rue Henri Simon 76111 Yport,France,Locataire,Alternance,"Maison lotissement classe moyenne récent, peu dense et très verdoyant, forte proximité de terres agricoles.",Individuel,Maisons,Résidentiel,Arboré,Péri-urbain,Nord Rouen,Moyen,"Zone résidentielle, mixte maisons bourgeoises et classe moy, zone avec espaces verts proche de vastes terrains agricoles et de la mer",Maisons,Résidentiel,Espaces verts,Péri-urbain,Sud Yport,Moyen-sup
26,"Lapeyrie (étudiante en architecture, coup de pouce nelly, pas de mobili-pass)",5995888020ee1e000fe4c0fe,Femme,20.0,0.0,,718.84,149523.0,127 avenue du Maréchal de Saxe 69003 Lyon ,France,Hébergé,,153 avenue Jean Jaurès 75019 Paris,France,Locataire,Etude,Immeuble bourgeois du centre de Lyon,Collectif,Immeubles,Commerçant,Urbanisé,Central,Centre Lyon,Sup,"Immeuble années 30, le long de l’avenue Jean Jaurès, à proximité de nombreux commerces",Immeubles,Commerçant,Urbanisé,Central,Nord Paris,Moyen-sup
27,Tanguy,599b05803b1e7d000f147ffb,Homme,37.0,0.0,3452.0,750.0,39197.0,2 bis rue Saint-Michel 59300 Valenciennes,France,Locataire,750.0,16 boulevard Commandant Mandine 13500 Martigues,France,Locataire,Embauche,"Petit immeuble briques rouge zone résidentielle proche université Valenciennes, zone résidentiel, classe moyenne",Collectif,Mixte,Autres activités,Espaces verts,Urbain,Sud Valenciennes,Moyen," Immeuble, dans la résidence Le Chambord, dans un quartier résidentielle constitué de maisons et d’immeuble, classe petit moyen-moyen..",Mixte,Résidentiel,Espaces verts,Urbain,Sud Martigues,Moyen
28,Mahaosa,59a492cd4515ee000f0eafa8,Femme,21.0,0.0,1226.0,540.0,23069.0,45 rue des Marronniers 77550 Moissy-Cramayel,France,Hébergé,,38 rue du Ménil 92600 Asnières-sur-Seine,France,Locataire,Alternance,"Petit immeuble de 2 étages assez récent mais déjà défraîchi , zone résidentielle, style classe moyenne/populaire face à un grand terrain arboré et non loin de terrains agricoles.",Collectif,Immeubles,Résidentiel,Arboré,Péri-urbain,Sud-Est Paris,Petit-moyen,"Zone résidentielle assez dense constituée de maisons et d’immeubles, classe moyenne, populaire",Mixte,Résidentiel,Urbanisé,Urbain,Nord Paris,Moyen
28,Mahaosa,59a492cd4515ee000f0eafa8,Femme,21.0,0.0,1226.0,540.0,23069.0,45 rue des Marronniers 77550 Moissy-Cramayel,France,Hébergé,,38 rue du Ménil 92600 Asnières-sur-Seine,France,Locataire,Alternance,"Petit immeuble de 2 étages assez récent mais déjà défraîchi , zone résidentielle, style classe moyenne/populaire face à un grand terrain arboré et non loin de terrains agricoles.",Collectif,Immeubles,Résidentiel,Arboré,Péri-urbain,Sud-Est Paris,Moyen-inf,"Zone résidentielle assez dense constituée de maisons et d’immeubles, classe moyenne, populaire",Mixte,Résidentiel,Urbanisé,Urbain,Nord Paris,Moyen
29,Laï,59b794a526163d000f744980,Homme,32.0,1.0,3211.0,750.0,0.0,199 Mingcheng Lu 201103 Shangaï,Chine ,Locataire,,24 bis avenue de la Libération 33610 Canéjan ,France,Locataire,Mutation,"Première couronne de Shanghai, Zone assez densifiée, îlots avec pleins immeubles similaires classes moy-sup, avec proximité de quelques zones arborées..plutôt résidentiel mais différentes activités autour",Collectif,Immeubles,Résidentiel,Espaces verts,Urbain,Sud-Ouest Shanghai,Moyen-sup,"Maison zone résidentielle classe moyenne/supérieure à la périphérie de Pessac, constituée principalement de maisons, dont certaines avec piscine, dans un cadre assez vert et arboré, à proximité de zones de forêt et d’espaces agricoles. ",Mixte,Autres activités,Espaces verts,Péri-urbain,Sud Bordeaux,Moyen-sup
30,Lebrun ,59b8096826163d000f7449c9,Homme,36.0,0.0,3558.0,620.0,46022.0,6 rue des Tiocs 33130 Bègles,France,Propriétaire,,68 rue du Vieux Port 69540 Irigny,France,Locataire,Mutation,"Petit immeuble assez récent de 3 étages, résidence de 3 immeubles identiques, fermée par un petit grillage, grand parking extérieur, style plutôt classes moyennes, zone résidentielle proche voie de chemin de fer et lotissements/zones résidentielles constituées plutôt maisons. ",Collectif,Mixte,Résidentiel,Espaces verts,Urbain,Sud Bordeaux,Moyen,"Appartement dans grande maison bourgeoise avec parc, au bord du Rhône, au bord du chemin de fer, dans une zone très verdoyante et arborée. ",Maisons,Résidentiel,Arboré,Péri-urbain,Sud Lyon,Sup
32,Auque,59cd098977bf9b000fa29735,Homme,38.0,0.0,5569.0,,96064.0,16 rue Camille Saint-Saëns 87350 Panazol,France,Propriétaire,,16 rue Antoine d’Auvergne 63000 Clermont-Ferrand,France,Locataire,Mutation,Maison avec jardin en zone résidentielle avec espaces verts constituée de maisons classe moy/moy-sup à l’Est de Limoges,Individuel,Lotissement,Résidentiel,Espaces verts,Péri-urbain,Est Limoges,Moyen-sup,Immeuble ancien dans le centre historique zone plutôt résidentielle et classe moyenne de Clermont-Ferrand,Immeubles,Résidentiel,Urbanisé,Central,Centre Clermont-Ferrand,Moyen-sup
33,Chacun,597b20b446e7ee000f296f83,Homme,29.0,0.0,4191.0,1500.0,62288.0,52 rue Burdeau 69001 Lyon ,France,Locataire,1500.0,27 rue Jacques Hillairet 75012 Paris ,France,Locataire,Mutation,"Immeuble d’un quartier ancien ,animé et gentrifié du centre historique de Lyon",Collectif,Immeubles,Commerçant,Espaces verts,Central,Centre Lyon,Moyen-sup,Immeuble assez récent d’un quartier un peu commerçant de l’est parisien.,Immeubles,Commerçant,Urbanisé,Central,Est Paris,Moyen-sup
34,Dupré,59d87c9ad80403000f95b2ec,Homme,25.0,0.0,1580.0,515.0,24569.0,9 rue Charles Faroux 60200 Compiègne,France,Locataire,,311 cours Émile Zola 69100 Villeurbanne,France,Locataire,Alternance,Immeuble zone résidentielle classe moyenne Compiègne ,Collectif,Immeubles,Résidentiel,Espaces verts,Urbain,Nord Paris,Moyen,Immeuble quartier un peu commerçant classe moyenne à l’est nord Villeurbanne,Immeubles,Commerçant,Urbanisé,Urbain,Est Lyon,Moyen
35,Boughani,5a008155947fa2000f7da93b,Femme,24.0,0.0,1181.0,568.0,22519.0,7 rue Jules Barard 42300 Roanne,France,Hébergé,,121 route de Genas 69100 Villeurbanne,France,Locataire,Alternance,Maison mitoyenne lotissement/immeuble quartier résidentiel/Zac petit moyen/popu nord est Roanne,Individuel,Mixte,Autres activités,Espaces verts,Urbain,Nord Roanne,Petit-moyen,"Immeuble récent quartier résidentiel immeubles (et qq maisons), sud Villeurbanne, proche tram",Immeubles,Résidentiel,Urbanisé,Urbain,Est Lyon,Moyen-sup
35,Boughani,5a008155947fa2000f7da93b,Femme,24.0,0.0,1181.0,568.0,22519.0,7 rue Jules Barard 42300 Roanne,France,Hébergé,,121 route de Genas 69100 Villeurbanne,France,Locataire,Alternance,Maison mitoyenne lotissement/immeuble quartier résidentiel/Zac petit moyen/popu nord est Roanne,Individuel,Mixte,Autres activités,Espaces verts,Urbain,Nord Roanne,Moyen-inf,"Immeuble récent quartier résidentiel immeubles (et qq maisons), sud Villeurbanne, proche tram",Immeubles,Résidentiel,Urbanisé,Urbain,Est Lyon,Moyen-sup
36,Robert,5a02e436947fa2000f7daa64,Homme,28.0,0.0,2035.0,841.0,19260.0,18 rue Saint-Thomas 27000 Evreux,France,Locataire,547.0,109 rue Francis de Préssensé 69100 Villeurbanne ,France,Locataire,Embauche,"Immeuble dans le centre ville, quartier commerçant, plutôt classe moyenne",Collectif,Mixte,Commerçant,Urbanisé,Central,Centre Evreux,Moyen,"Immeuble quartier résidentiel de Villeurbanne, avec qq espaces verts, classe moyenne.",Mixte,Résidentiel,Espaces verts,Urbain,Est Lyon,Moyen
37,Richardot ,59df7eb5631061000f0e1c6d,Homme,23.0,0.0,1453.0,650.0,10608.0,13 chemin de l’Abreuvoir 21380 Asnière-Les-Dijon ,France,Hébergé,,5 montée du Change 69005 Lyon ,France,Locataire,Alternance,"Maison zone résidentielle, proximité immédiate de terrains agricoles, classe moyenne sup ",Individuel,Maisons,Résidentiel,Agricole,Péri-urbain,Nord Dijon,Moyen,"Immeuble ancien du Vieux-Lyon, commerçant, classe moi-sup.",Immeubles,Commerçant,Urbanisé,Central,Centre Lyon,Moyen-sup
38,Morelle,5a1d5647aa796e000fd01e5f,Homme,22.0,0.0,629.0,400.0,94241.0,9 rue Casimir Brenier 38120 Saint-Égrève,France,Hébergé,,23 rue Claudius Penet 69003 Lyon ,France,Locataire,Alternance,"Maison dans périurbain au nord de Grenoble, quartier résidentiel mixte avec maison classe moiy:moy-sup et immeubles plutôt classe populaire ",Individuel,Mixte,Autres activités,Espaces verts,Péri-urbain,Nord Grenoble,Moyen,"Bloc maisons de ville dans le 3ème de Lyon, zone mixte immeubles et maisons, classe moy-sup/classe mon, plutôt résidentielle avec qq entreprises/usines, qq espaces verts, proximité immédiate du tram",Mixte,Autres activités,Espaces verts,Central,Est Lyon,Moyen-sup
......@@ -50,32 +50,32 @@ id,name,HiL_id,sex,age,nb_child,income,monthly_charges,tax_revenue,old_address,o
50,Saïez (pas de numéro de dossier) (ABANDON),,Femme,,,,,,,,,,,,,Mutation,,,,,,,,,,,,,,,
51,Geffroy,5a312331fdc6d3000f4e49cb,Femme,48.0,0.0,3529.0,750.0,36314.0,69 avenue du Maréchal Foch 78400 Chatou ,France,Locataire,1055.0,2 bis rue Jean Mermoz 74940 Annecy-Le-Vieux,France,Locataire,Embauche,"Bloc maisons de ville années 60 nord-est Paris, quartier mixte immeuble et maisons, moy-sup, commerçant, qq espaces verts",Individuel,Mixte,Commerçant,Urbanisé,Urbain,Nord-Ouest Paris,Moyen-sup,"Petit immeuble récent avec jardin, zone résidentielle maisons et immeubles, périurbaine, à l’Est d’Annecy, plutôt classe moy-sup/sup, grandes maisons avec terrain et piscine à proximité..",Mixte,Commerçant,Espaces verts,Péri-urbain,Nord-Est Annecy,Sup
52,Gabriel ,59e61471631061000f0e1e34,Femme,25.0,0.0,2695.0,,108317.0,150 chemin de la Tuilerie 13320 Bouc-Bel-Air,France,Hébergé,,12 rue Fourcade 75015 Paris ,France,Locataire,Embauche,"Maison, zone résidentielle périurbaine, lotissement classe moy-sup au sud d’Aix-en-Provence, présence arbres et espaces verts. ",Individuel,Lotissement,Résidentiel,Espaces verts,Péri-urbain,Sud Aix-en-Provence,Sup,"Immeuble bourgeois quartier commerçant, centre paris (15ème),urbanisé",Immeubles,Commerçant,Urbanisé,Central,Centre Paris,Sup
53,Dramé,59fb8e49947fa2000f7da794,Homme,32.0,1.0,2189.0,437.0,24403.0,2 Allée Boris Vian 93380 Pierrefitte-sur-Seine,France,Hébergé,,3 rue Henri Chevalier 69004 Lyon ,France,Locataire,Embauche,"Immeuble récent, style grand ensemble classe moyenne (avec autour grands ensemble plutôt popu) résidentiel, au sud de Sarcelles, peu verdoyant.",Collectif,Grand ensemble,Résidentiel,Urbanisé,Urbain,Nord Paris,Petit-moyen,"Petit immeuble années 70 au nord du 4ème à Lyon( limite Cuire), zone résidentielle, mixte immeubles, gd ensemble, et maisons, plutôt classe moy, assez vert. ",Mixte,Résidentiel,Espaces verts,Urbain,Nord Lyon,Moyen
53,Dramé,59fb8e49947fa2000f7da794,Homme,32.0,1.0,2189.0,437.0,24403.0,2 Allée Boris Vian 93380 Pierrefitte-sur-Seine,France,Hébergé,,3 rue Henri Chevalier 69004 Lyon ,France,Locataire,Embauche,"Immeuble récent, style grand ensemble classe moyenne (avec autour grands ensemble plutôt popu) résidentiel, au sud de Sarcelles, peu verdoyant.",Collectif,Grand ensemble,Résidentiel,Urbanisé,Urbain,Nord Paris,Moyen-inf,"Petit immeuble années 70 au nord du 4ème à Lyon( limite Cuire), zone résidentielle, mixte immeubles, gd ensemble, et maisons, plutôt classe moy, assez vert. ",Mixte,Résidentiel,Espaces verts,Urbain,Nord Lyon,Moyen
54,Laniray (ABANDON),59df29f0631061000f0e1c56,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
55,Joly (pas de numéro de dossier),,Homme,46.0,0.0,5005.0,1100.0,47649.0,89 rue Faraday 59700 Marcq-en-Bareuil,France,Propriétaire,,Mas du Pin 13930 Aureille,France,Locataire,Mutation,"Maison de ville brique rouge (maisons collées les unes aux autres) , avec petit jardin, résidentiel, classe moy, nord-est de Lille",Individuel,Mixte,Résidentiel,Espaces verts,Urbain,Nord-Est Lille,Moyen,Adresse introuvable sur google View,,,,,,
56,Paul (pas de numéro de dossier pas de données sur nombre de biens biens proposés et visités) ,,Homme,28.0,0.0,1604.0,550.0,106301.0,490 avenue Louis Bozzo 83000 Toulon,France,Hébergé,,336 rue Paul Bert 69003 Lyon,France,Colocataire,Embauche,Maison avec piscine dans quartier résidentiel classe sup au nord-est de Toulon ,Individuel,Maisons,Résidentiel,Espaces verts,Urbain,Nord-Est Toulon,Sup,"Immeuble années 70 dans quartier commerçant, fin du 3ème à l’est de Lyon, classe moy",Immeubles,Commerçant,Urbanisé,Central,Est Lyon,Moyen
57,Fauquet,5a2ea7a0a05fae000fd4e3d2,Homme,53.0,1.0,7965.0,660.0,73422.0,31 rue Decoster Virnot 59700 Marcq-en-Bareuil,France,Locataire,,29 rue Chocatelle 51200 Epernay,France,Locataire,Mutation,"Immeuble années 90, quartier résidentiel avec quelques entreprises, plutôt classe moy-sup commune nord-est de Lille sur ceinture urbaine avec Tourcoing",Collectif,Mixte,Résidentiel,Espaces verts,Urbain,Nord-Est Lille,Moyen-sup,"Petit immeuble années 90 quartier central, commerçant classe moy-sup, qq espaces-verts, ",Immeubles,Commerçant,Urbanisé,Central,Centre Epernay,Moyen-sup
58,Longin,5a3c061534fd41000f4bcd28,Femme,46.0,2.0,2446.0,720.0,14916.0,62 avenue Mont Jovis 87100 Limoges,France,Locataire,630.0,9 rue Victor Hugo 69800 Saint-Priest,France,Locataire,Embauche,"Petit immeuble 2 étages, zone mixte, centre Limoge, quartier un peu commerçant, plutôt classe moy-sup",Collectif,Immeubles,Commerçant,Urbanisé,Central,Centre Limoges,Petit-moyen,"Barre immeuble 5 étages grand ensemble, quartier urbanisé, mixtes immeubles, barres, maisons, plutôt résidentiel, moyen/popu ",Grand ensemble,Résidentiel,Espaces verts,Urbain,Sud-Est Lyon,Petit-moyen
58,Longin,5a3c061534fd41000f4bcd28,Femme,46.0,2.0,2446.0,720.0,14916.0,62 avenue Mont Jovis 87100 Limoges,France,Locataire,630.0,9 rue Victor Hugo 69800 Saint-Priest,France,Locataire,Embauche,"Petit immeuble 2 étages, zone mixte, centre Limoge, quartier un peu commerçant, plutôt classe moy-sup",Collectif,Immeubles,Commerçant,Urbanisé,Central,Centre Limoges,Moyen-inf,"Barre immeuble 5 étages grand ensemble, quartier urbanisé, mixtes immeubles, barres, maisons, plutôt résidentiel, moyen/popu ",Grand ensemble,Résidentiel,Espaces verts,Urbain,Sud-Est Lyon,Moyen-inf
59,Rouille,5a2bf54fa05fae000fd4e3a7,Femme,25.0,0.0,1560.0,955.39,,2542 Route de Guinot 31330 Merville,France,Locataire,880.0,146 allée du domaine des acacias 01480 Ars- sur-Formans,France,Locataire,Mutation,"Maison isolée, zone rurale, entourée de terrains agricoles, proche lotissement et petite commune nord ouest de Toulouse, maison classe moy/petit moyen",Individuel,Maisons,Résidentiel,Agricole,Rural,Nord-Ouest Toulouse,Moyen,"Maison lotissement neuf classe moyenne/petit moyen, proximité immédiate terrains agricole, petite commune à l’Est de Villefranche-sur-Saône.",Lotissement,Résidentiel,Agricole,Péri-urbain,Est Villefranche-sur-Saône,Moyen
60,Coulibaly ,5a3fdfc534fd41000f4bce68,Femme,28.0,0.0,2374.0,570.0,6872.0,4 rue Charles Emile Paris 93350 Le Bourget,France,Locataire,854.0,9 rue Hanappier 33200 Bordeaux,France,Locataire,Embauche,"Petite maison quartier résidentiel mixte immeubles et maisons, périphérie Nord-Est de Paris, classe moyenne/moyenne sup",Individuel,Mixte,Résidentiel,Espaces verts,Urbain,Nord-Est Paris,Moyen,"Immeuble récent quartier résidentiel, mixte, limite ouest du centre de Bordeaux, classe moyenne ",Mixte,Résidentiel,Urbanisé,Urbain,Ouest Bordeaux,Moyen
61,Baillot ,5a314707832f4e000f7731b7,Homme,27.0,0.0,1970.0,434.0,21971.0,36 rue des Lads 69500 Bron,France,Locataire,516.0,1 rue Ernest Renan 35136 Saint-Jacques-de-la-Lande,France,Locataire,Mutation,"Petit immeuble 2 étages zone résidentielle mixte maison/immeubles, classe moyenne/petit moyen, Sud- Est de Lyon, proche axe routier, Uni Lyon 2 Broin et parc",Collectif,Mixte,Résidentiel,Espaces verts,Urbain,Sud-Est Lyon,Moyen,"Petit immeuble 2 étages assez récent dans groupe immeubles similaires, zone résidentielle, périurbain sud ouest de Rennes, classe moyenne, qq espaces verts ",Mixte,Résidentiel,Espaces verts,Péri-urbain,Sud-Ouest Rennes,Moyen
62,Boulbaroud,5a5ce84ffaa5a0000f8565b4,Homme,24.0,0.0,1026.0,380.0,,27 avenue de Schwargenbeck 07200 Aubenas,France,Hébergé,,"65 rue Jules Guesde
69200 Venissieux",France,Locataire,Alternance,"Barre immeuble quartier résidentiel popu ouest de Aubenas, mixte avec maisons ",Collectif,Mixte,Résidentiel,Urbanisé,Urbain,Ouest Aubenas,Popu,"Immeuble récent 4 étage, quartier résidentiel classe moyenne/popu, (mosquée , usine à proximité, ..)",Immeubles,Autres activités,Urbanisé,Urbain,Sud-Est Lyon,Petit-moyen
69200 Venissieux",France,Locataire,Alternance,"Barre immeuble quartier résidentiel popu ouest de Aubenas, mixte avec maisons ",Collectif,Mixte,Résidentiel,Urbanisé,Urbain,Ouest Aubenas,Popu,"Immeuble récent 4 étage, quartier résidentiel classe moyenne/popu, (mosquée , usine à proximité, ..)",Immeubles,Autres activités,Urbanisé,Urbain,Sud-Est Lyon,Moyen-inf
63,Ambarki ,5a562171faa5a0000f856329,Homme,32.0,0.0,1534.0,830.0,19435.0,9 chemin du Monarie 38240 Meylan,France,Locataire,460.0,4 rue Marcel Sambat 69150 Decines,France,Locataire,Mutation,"Résidence petits immeubles périurbain, résidentiel, avec qq entreprises et lotissement à proxilité, proche autoroute, verdoyant et arboré, classe moy-sup, Nord-Est Grenoble",Collectif,Mixte,Autres activités,Arboré,Péri-urbain,Nord-Est Grenoble,Moyen-sup,"Immeuble récent quartier un peu commerçant, mixte avec maisons, est de Lyon, urbain, espace verts, classe moyennne, proche lotissements",Mixte,Commerçant,Espaces verts,Péri-urbain,Est Lyon,Moyen
64,BIzig ,5a5e5ae7faa5a0000f8566ae,Homme,19.0,0.0,922.0,483.0,25326.0,246 route de Bourg 01360 Jayat,France,Hébergé,,"81 avenue Marius Herliet, 69008 Lyon",France,Locataire,Alternance,"Annexe à l’arrière d’un bar-restau en bord de route. Village au nord de Bourg en Bresse., terres agricoles à proximité. Zone classe moy/popu",Individuel,Maisons,Commerçant,Agricole,Rural,Nord Bourg en Bresse,Moyen,"Immeuble résidence années 70-80, zone commerçante/résidentielle Bron classe moyenne,moyenne, un peu d’espaces verts",Immeubles,Commerçant,Urbanisé,Central,Est Lyon,Moyen
65,Harduin ,5a37f7817f1530000feef370,Femme,25.0,0.0,1178.0,588.0,10075.0,14 b Faubourg Reclus 73000 Chambéry,France,Locataire,621.0,3 rue Claude Berri 69800 Saint-Priest,France,Locataire,Mutation,"Immeuble années 60 avec grande tour derrière dans la même architecture, dans zone commerçant limite centre historique Chambéry, quartier classe moyenne, peu verdoyant",Collectif,Immeubles,Commerçant,Urbanisé,Central,Centre Chambéry,Moyen,"Petit immeuble dans résidence de 5 immeubles similaires, récent, avec espaces verts , classe moyenne , dans style ZAC, commune sud-est Lyon",Immeubles,Autres activités,Espaces verts,Urbain,Sud-Est Lyon,Moyen
66,Talbaoui ,5a72cc51920f43000fa336ba,Homme,22.0,0.0,1181.0,447.0,0.0,12 rue Bitola 88000 Epinal,France,Hébergé,,46 cours Docteur Long 69003 Lyon,France,Locataire,Alternance,"Immeuble grand ensemble quartier popu au sud d’Epinal, qq espaces verts ",Collectif,Grand ensemble,Résidentiel,Espaces verts,Urbain,Sud Épinal,Popu,"Petit immeuble quartier commerçant plutôt moyen- moyen-sup, est du centre de Lyon, urbanisé",Mixte,Commerçant,Urbanisé,Central,Est Lyon,Moyen-sup
67,Petitjean ,5a8eafed3e0001000f5466c3,Homme,24.0,0.0,1775.0,489.0,57545.0,20 rue d’Eguesteim 68110 Illzach,France,Hébergé,,290 rue Jean-Paul Sartre 38090 Villefontaine,France,Locataire,Embauche,"Logement maison mitoyenne lotissement résidentiel, moyen-sup/sup, proche terrains agricoles",Individuel,Lotissement,Résidentiel,Espaces verts,Péri-urbain,Nord Mulhouse,Moyen-sup,"Immeuble assez récent, zone résidentielle petits immeubles, classe popu/moyenne, arboré, sud-est Lyon ",Immeubles,Résidentiel,Arboré,Péri-urbain,Sud-Est Lyon,Petit-moyen
67,Petitjean ,5a8eafed3e0001000f5466c3,Homme,24.0,0.0,1775.0,489.0,57545.0,20 rue d’Eguesteim 68110 Illzach,France,Hébergé,,290 rue Jean-Paul Sartre 38090 Villefontaine,France,Locataire,Embauche,"Logement maison mitoyenne lotissement résidentiel, moyen-sup/sup, proche terrains agricoles",Individuel,Lotissement,Résidentiel,Espaces verts,Péri-urbain,Nord Mulhouse,Moyen-sup,"Immeuble assez récent, zone résidentielle petits immeubles, classe popu/moyenne, arboré, sud-est Lyon ",Immeubles,Résidentiel,Arboré,Péri-urbain,Sud-Est Lyon,Moyen-inf
68,Jacob,5a42657634fd41000f4bce6d,Femme,30.0,0.0,1796.0,450.0,1960.0,6 allée du Jardin Public 47250 Samazan,France,Hébergé,,38 rue Chalopin 69007 Lyon,France,Locataire,Embauche,"Maison lotissement classe moyen/petit-moyen, espaces verts, résidentiel, sud de Marmande, proche terrains agricoles et petit bois",Individuel,Lotissement,Résidentiel,Agricole,Péri-urbain,Sud Marmande,Moyen,"Immeuble dans le centre de Lyon (7ème) quartier commerçant, plutôt classe popu et moyenne-moyenne-sup, en gentrification",Immeubles,Commerçant,Urbanisé,Central,Centre Lyon,Moyen-sup
69,Mambuene,5a5d2c6cfaa5a0000f8565ba,Femme,26.0,0.0,1796.0,1210.0,11101.0,14 rue Charles Quint 59800 Lille,France,Locataire,740.0,3 rue Danton 94270 Le Kremlin Bicêtre,France,Locataire,Embauche,"Petit immeuble en brique rouge dans le centre de Lille, urbanisé, quartier commerçant, classe moyen-sup, ",Collectif,Mixte,Commerçant,Urbanisé,Central,Centre Lille,Moyen-sup,"Immeuble 70’s quartier commerçant classe popu/moyenne, juste derrière périphérique, sud de Paris",Immeubles,Commerçant,Urbanisé,Urbain,Sud Paris,Moyen
70,Menouer,5a816a5f1ca503000ffaf2bc,Homme,33.0,0.0,1463.0,500.0,,93 lotissement 275 Hai Khemisti 31000 Oran,Algérie,Hébergé,,3 rue des Charrières 69320 Feyzin,France,Locataire,Embauche,"Logement (pas possible de voir si maison ou immeuble) dans un quartier résidentiel à l’est de Oran, plutôt classe moyenne, très urbanisé",Individuel,Mixte,Résidentiel,Urbanisé,Urbain,Est Oran,Moyen,"Immeuble dans un quartier commerçant de Villeurbanne, urbanisé, classe moyenne- moyenne-sup, proche Gratte-Ciel",Immeubles,Commerçant,Urbanisé,Central,Est Lyon,Moyen
71,Schneider ,5ab12eb03e0001000f547e79,Femme,38.0,0.0,2079.0,750.0,34901.0,20 route de Briey 57160 Châtel-Saint-Germain,France,Propriétaire,,30 rue du 19 Novembre 68870 Bartenheim,France,Locataire,Mutation,"Maison bord de route, proche lotissements, zone périurbaine à l’ouest de Metz, classe moyenne/popu, arboré et vert",Individuel,Maisons,Résidentiel,Arboré,Péri-urbain,Ouest Metz,Moyen,"Petit immeuble, zone résidentielle classe moyenne, espaces verts, commune au sud-est de Mulhouse. ",Mixte,Résidentiel,Espaces verts,Urbain,Sud-Est Mulhouse,Moyen
72,Carlier ,5a78899e920f43000fa338fe,Homme,51.0,0.0,1961.0,860.0,1170.0,37 rue du Général Gillant 57100 Thionville ,France,Locataire,,16 avenue Félix Gouin 13800 Istres,France,Locataire,Embauche,"Maison en zone résidentielle, périurbain, nord-ouest de Thionville, classe moyenne, proche terrains agricoles",Invididuel,Maisons,Résidentiel,Agricole,Rural,Nord-Ouest Thionville,Moyen,"Immeuble dans grand ensemble, zone maisons/immeuble, classe moyenne/popu, zone résidentielle, proche du centre",Mixte,Résidentiel,Urbanisé,Urbain,Centre Istres,Petit-moyen
72,Carlier ,5a78899e920f43000fa338fe,Homme,51.0,0.0,1961.0,860.0,1170.0,37 rue du Général Gillant 57100 Thionville ,France,Locataire,,16 avenue Félix Gouin 13800 Istres,France,Locataire,Embauche,"Maison en zone résidentielle, périurbain, nord-ouest de Thionville, classe moyenne, proche terrains agricoles",Invididuel,Maisons,Résidentiel,Agricole,Rural,Nord-Ouest Thionville,Moyen,"Immeuble dans grand ensemble, zone maisons/immeuble, classe moyenne/popu, zone résidentielle, proche du centre",Mixte,Résidentiel,Urbanisé,Urbain,Centre Istres,Moyen-inf
73,Lienhart,5a8e9be33e0001000f5466bc,Homme,25.0,0.0,2034.0,372.36,37886.0,15 rue Gay Lambert 13005 Marseille,France,Hébergé,,39 rue Jules Brunard 69007 Lyon ,France,Locataire,Embauche,"Maison mitoyenne dans quartier résidentiel du centre de Marseille , classe moyenne/moyenne-sup, urbanisé",Individuel,Mixte,Résidentiel,Urbanisé,Central,Est Marseille,Moyen-sup,"Immeuble 70’s quartier central de Lyon, quelques commerces, un peu de verdure, moyen/moyen-sup",Immeubles,Résidentiel,Espaces verts,Central,Centre Lyon,Moyen-sup
74,Mande,5a799985920f43000fa33a44,Homme,48.0,6.0,4051.0,1163.0,31763.0,2 rue des Cavaliers 97310 Kourou,France,Locataire,1120.0,1806 Chemin du Carreyrat 82000 Montauban,France,Locataire,Mutation,"Maison lotissement résidentiel du centre de Kourou, classe moy-sup/sup urbanisé",Individuel,Lotissement,Résidentiel,Urbanisé,Central,Centre Kourou,Moyen-sup,"Maison lotissement périurbain classe moy-sup, espaces verts",Lotissement,Résidentiel,Espaces verts,Péri-urbain,Sud-Est Montauban,Moyen-sup
75,Sery,5a8f358f3e0001000f5467b1,Homme,27.0,0.0,2245.0,669.0,12876.0,6 rue de la Ouatose 38420 Domène,France,Locataire,615.0,Allée Nord-Sud 69100 Villeurbanne,France,Locataire,Embauche,"Lot Immeubles récents, poche lotissement maisons, quartier résidentiel zone périurbaine, vue sur les montagnes, classe moyenne/moy-sup",Collectif,Mixte,Résidentiel,Espaces verts,Péri-urbain,Nord-Est Grenoble,Moyen-sup,"Immeuble 90’s proche centre Villeurbanne, quartier résidentiel, classe moyenne/moy-sup",Immeubles,Résidentiel,Urbanisé,Central,Est Lyon,Moyen
76,Er Rifki ABANDON… A MODIFIER DANS DOCU MODALISA (bail saisi est son bail précédent…logement de transition ?).. Je laisse les info saisie pour le moment.. a voir avec Loïc..,5a79f672920f43000fa33ba3,Femme,24.0,0.0,1025.0,495.0,534.0,22 rue du Brabant 25000 Besançon,France,Hébergé,,2 rue Général Dayau 69100 Villeurbanne,France,Locataire,Alternance,,,,,,,,,,,,,,,
77,Benhaddad,5a75a845920f43000fa33817,Femme,23.0,0.0,1422.0,1148.0,,33 rue Théophraste Renaudot 42000 Saint-Etienne,France,Hébergé,,77 boulevard National 92250 La Garenne-Colombe,France,Locataire,Alternance,Maison classe moy-popu quartier résidentiel périurbain au NOrd-Ouest de Saint-Étienne. ,Individuel,Maisons,Résidentiel,Espaces verts,Péri-urbain,Nord-Ouest Saint-Étienne,Petit-moyen,"Immeuble années 80 classe moyenne, dans zone commerçante,, proche tram, urbanisée, nord-ouest de Paris",Immeubles,Commerçant,Urbanisé,Urbain,Nord-Ouest Paris,Moyen
77,Benhaddad,5a75a845920f43000fa33817,Femme,23.0,0.0,1422.0,1148.0,,33 rue Théophraste Renaudot 42000 Saint-Etienne,France,Hébergé,,77 boulevard National 92250 La Garenne-Colombe,France,Locataire,Alternance,Maison classe moy-popu quartier résidentiel périurbain au NOrd-Ouest de Saint-Étienne. ,Individuel,Maisons,Résidentiel,Espaces verts,Péri-urbain,Nord-Ouest Saint-Étienne,Moyen-inf,"Immeuble années 80 classe moyenne, dans zone commerçante,, proche tram, urbanisée, nord-ouest de Paris",Immeubles,Commerçant,Urbanisé,Urbain,Nord-Ouest Paris,Moyen
78,Suptil,5a57d6befaa5a0000f856452,Femme,25.0,0.0,1348.0,630.0,13791.0,430 rue de la République 73000 Chambéry,France,Hébergé,,10 boulevard des Collèges 38230 Pont de Cheruy,France,Locataire,Embauche,"Immeuble récent quartier un peu commerçant, classe moy/moy-sup, proche centre Chambéry, quelques espaces-verts",Collectif,Immeubles,Commerçant,Espaces verts,Central,Centre Chambéry,Moyen-sup,"Maison commune est de Lyon, petit lotissement, zone résidentielle, proche écoles..",Mixte,Autres activités,Espaces verts,Péri-urbain,Est Lyon,Moyen
79,Petit ,5aa401733e0001000f5472ca,Femme,52.0,1.0,2078.0,651.0,24941.0,26 avenue de la Gare 73700 Bourg- Saint-Maurice,France,Locataire,423.0,4 rue de Thou 69001 Lyon,France,Locataire,Mutation,"Petit immeuble années 50 dans le centre de Bourg-Saint-Maurice. Zone commerçante, proche gare, moyen/moyen-sup",Collectif,Mixte,Commerçant,Urbanisé,Central,Centre Bourg-Saint-Maurice,Moyen-sup,"Immeuble ancien centre de Lyon, quartier commerçant, moyen-sup/sup",Immeubles,Commerçant,Urbanisé,Central,Centre Lyon,Moyen-sup
80,Jourdain ,5a2ff97aa05fae000fd4e54a,Homme,55.0,0.0,3800.0,300.0,66875.0,16 rue des Redons 13280 Raphèle-les-Arles,France,Propriétaire,,1 rue Montjézieux 48100 Bourg-sur-Cologne,France,Locataire,Mutation,"Maison lotissement zone résidentielle périurbaine, classe moy-sup/sup, proche terres agricoles",Individuel,Lotissement,Résidentiel,Espaces verts,Péri-urbain,Sud-Est Arles,Moyen-sup,Maison (adresse introuvable sur Google view),,,,,,
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0,P14_RPMAISON_ACHTOT,P14_NBPI_RPMAISON,P14_RP_120M2P,P14_ANEM_RP_PROP,P14_RPMAISON,C14_MEN_CS7,P14_NPER_RP_PROP,DEC_D114,C14_COUPAENF,C14_PMEN_MENCOUPSENF,P14_MAISON,P14_RPMAISON_ACH90,P14_RPMAISON_ACH70,P14_RP_100120M2,P14_RP_5PP,P14_RPMAISON_ACH45,C14_PMEN_CS1,,,
1,DENSITY,P14_PHORMEN,P14_NBPI_RPMAISON,DEC_EQ14,P14_POP_ETR,P14_RP_LOCHLMV,P14_ANEM_RP_GRAT,C14_ACTOCC15P_MAR,P14_POP0205,NB_A504,NB_F121,P14_RP_ACH90,P14_RP_ACH19,P14_NPER_RP_LOCHLMV,DEC_D614,P14_RPAPPART_ACH19,P14_PMEN_ANEM10P,,,
2,P14_RP_ACH90,P14_RPMAISON_ACH90,C14_NE24F4P,DEC_D314,P14_RP_LOCHLMV,P14_RPAPPART_ACH90,P14_RETR1564,DEC_EQ14,P14_RSECOCC,NB_F103,DEC_PTSA14,C14_MENSFAM,NB_F116,NB_F111,P14_NPER_RP_GRAT,C14_PMEN_CS6,NB_D240,,,
3,C14_COUPAENF,P14_POP_IMM,P14_RPAPPART_ACH19,P14_SCOL1114,C14_MEN_CS3,P14_SCOL1517,C14_ACTOCC15P_MAR,P14_POP1524_PSEUL,P14_POP0205,P14_RP_ACH19,P14_RPMAISON_ACH90,DEC_PTSA14,P14_ACTOCC15P_ILT2P,C14_POP15P_CS5,P14_RP_ACH90,DENSITY,P14_POP0610,NB_F111,P14_RSECOCC,P14_RP_80100M2
4,P14_POP1524_PSEUL,P14_SCOL2529,C14_ACTOCC15P_MAR,P14_ETUD1564,P14_RPAPPART_ACH19,P14_NSAL15P_EMPLOY,C14_ACTOCC15P_TCOM,P14_CHOM5564,P14_RP_CINDELEC,P14_POP_IMM,C14_PMEN_MENSFAM,P14_RP_ACH19,P14_RPAPPART_ACH70,C14_MENSFAM,C14_MEN_CS8,P14_RSECOCC,P14_SAL15P_EMPAID,P14_RP_3040M2,,
5,P14_RP_ACH19,P14_RPAPPART_ACH19,C14_PMEN_CS6,NB_A402,P14_SCOL2529,C14_POP15P_CS3,C14_ACT1564_CS2,NB_A301,P14_RPMAISON_ACH19,C14_MEN_CS6,P14_ANEM_RP_LOCHLMV,P14_RP_ACH70,C14_ACTOCC1564_CS3,P14_POP_ETR,P14_ACTOCC15P_ILT3,C14_ACT1564_CS3,P14_SAL15P_APPR,P14_SAL15P_CDD,,
0,P14_RPMAISON_ACHTOT,P14_NBPI_RPMAISON,P14_RP_120M2P,P14_ANEM_RP_PROP,P14_RPMAISON,C14_MEN_CS7,P14_NPER_RP_PROP,DEC_D114,C14_COUPAENF,C14_PMEN_MENCOUPSENF,C14_ACTOCC15P_MAR,P14_RPMAISON_ACH90,P14_RPMAISON_ACH70,P14_RPMAISON_ACH19,P14_MAISON,P14_POP0014,C14_MENCOUPAENF,C14_MENCOUPSENF,P14_NBPI_RP_ANEM10P,NB_B202,P14_NBPI_RPAPPART,P14_RP_5PP,P14_RP_ACH19,P14_RPAPPART,P14_ACTOCC1564,P14_RP_LOC,DEC_D314,NB_B304,P14_SCOL0610,P14_SCOL1114,P14_POP0205,NB_D213,P14_POPMEN80P,C14_PMEN_MENCOUPAENF,P14_SCOL1517,P14_APPART,NB_A406,P14_POP0610,P14_NPER_RP_LOC,NB_B201,C14_PMEN_CS2,NB_B305,P14_NBPI_RP_ANEM0509,P14_NSAL15P_TP,C14_ACTOCC1564_CS4,C14_ACTOCC1564_CS2,P14_RP_PROP,P14_NPER_RP_GRAT,P14_POP,DEC_PTSA14,P14_RP_ACH45,P14_RP_ACH90,P14_MEN_ANEM0509,NB_B203,C14_PMEN_CS4,NB_A403,P14_RPMAISON_ACH45,P14_CHOM2554,C14_PMEN_CS7,NB_B204,P14_RP_GRAT,C509,P14_RSECOCC,P14_RETR1564,C14_PMEN_MENSFAM,DENSITY,C14_NE24F4P,NB_F120,P14_RP,DEC_S80S2014,P14_SAL15P_CDI,NB_B310,NB_A505,C14_COUPSENF,P14_RP_3P,NB_D233,P14_LOGVAC,P14_NSCOL15P_DIPLMIN,P14_PMEN_ANEM0509,C14_PMEN_CS6,P14_ANEM_RP_LOC,C14_PMEN_CS5,C14_MEN,P14_AINACT1564,NB_B315,P14_RP_4P,P14_NSAL15P_EMPLOY,NB_B101,C14_POP15P_CS2,NB_B303,P14_F3044,P14_ANEM_RP,P14_RP_CCIND,NB_A402,P14_ACTOCC15P_ILT5,DEC_EQ14,NB_D403,C14_PMEN_CS1,P14_POP4054,NB_F103,P14_RP_100120M2,NB_D240,NB_E101,DEC_PCHO14,P14_RP_CCCOLL,DEC_D714,P14_RPAPPART_ACH05,DEC_D814,DEC_Q114,NB_A502,NB_A504,NB_B102,P14_POP0002,NB_D239,DEC_D214,NB_B309,NB_D404,C14_ACTOCC1564_CS1,P14_RPAPPART_ACH11,C14_ACT1564_CS3,C14_ACTOCC15P_TCOM,P14_ANEM_RP_GRAT,NB_D602,P14_RPAPPART_ACHTOT,P14_RP_2P,P14_RPAPPART_ACH70,P14_ACTOCC15P_ILT4,P14_RPAPPART_ACH45,DEC_D414,C14_PMEN_CS8,C14_MEN_CS1,P14_RP_1P,P14_ANEM_RP_LOCHLMV,P14_RP_4060M2,NB_A507,C14_ACTOCC15P_DROU,NB_B302,P14_SCOL2529,P14_POP80P,P14_POP0305,P14_RPAPPART_ACH90,NB_A404,NB_F111,C14_PMEN_MENFAMMONO,C14_MEN_CS8,P14_ACTOCC15P_ILT1,DEC_PBEN14,P14_RP_LOCHLMV,DEC_D914,P14_RP_VOIT2P,P14_RP_M30M2,C14_ACTOCC15P_PAS,NB_D212,P14_POP75P,P14_NPER_RP_LOCHLMV,P14_ACTOCC15P_ILT3,P14_POP5564,P14_RPAPPART_ACH19,,,,,
1,DENSITY,P14_PHORMEN,P14_NBPI_RPMAISON,DEC_EQ14,P14_POP_ETR,P14_RP_LOCHLMV,P14_ANEM_RP_GRAT,C14_ACTOCC15P_MAR,P14_POP0205,NB_A504,NB_E101,P14_NPER_RP_PROP,C14_COUPAENF,P14_RP_CCIND,C14_MENCOUPAENF,P14_NPER_RP_LOCHLMV,NB_A301,P14_ACTOCC15P_ILT2,P14_SCOL1517,P14_NBPI_RP_ANEM10P,C14_MEN_CS7,P14_POP_IMM,P14_SCOL0610,P14_POP0610,C14_ACTOCC15P_VOIT,P14_POP0305,P14_CHOM5564,P14_CHOM1524,P14_POP0014,P14_SCOL1114,NB_B308,C509,NB_D233,C14_NE24F4P,DEC_D114,C14_ACTOCC1564_CS2,C101,NB_D601,P14_NBPI_RP_ANEM0509,P14_RPMAISON_ACH70,C14_MENCOUPSENF,NB_A501,P14_POP4559,P14_NSCOL15P,NB_D213,P14_AINACT1564,P14_RP_CCCOLL,NB_D201,NB_A401,C14_ACTOCC1564_CS3,P14_NSAL15P_TP,NB_A303,P14_ANEM_RP_LOCHLMV,P14_RP_120M2P,P14_RPMAISON_ACH11,C14_MENPSEUL,P14_RPAPPART_ACH45,P14_MAISON,P14_SCOL2529,P14_ACT1524,NB_A506,P14_RPMAISON_ACHTOT,C14_MENFSEUL,NB_B203,C14_PMEN_CS6,C14_MEN_CS6,P14_ACTOCC15P_ILT4,P14_POP1529,NB_A304,NB_F114,C14_ACTOCC1564_CS5,DEC_PCHO14,P14_LOGVAC,C14_PMEN_CS5,P14_ACTOCC1564,P14_NSCOL15P_SUP,P14_RP,P14_RP_80100M2,NB_A404,DEC_D314,P14_RSECOCC,P14_MEN_ANEM0509,P14_NSCOL15P_DIPLMIN,P14_CHOM2554,DEC_PTSA14,P14_ACTOCC15P_ILT2P,NB_B315,C14_ACT1564_CS5,C301,P14_POP15P_MARIEE,P14_NPER_RP_GRAT,P14_POP65P,C14_MEN,P14_RP_VOIT1P,P14_MEN_ANEM0002,NB_B101,P14_RPAPPART_ACH19,C14_MEN_CS3,P14_RP_3040M2,C14_PMEN_CS3,NB_F121,P14_RP_ACH90,P14_RP_ACH19,DEC_D614,P14_PMEN_ANEM10P,C14_MEN_CS8,P14_POP1524_PSEUL,P14_RP_1P,DEC_TP6014,NB_D202,NB_A203,P14_POP0002,C14_PMEN_CS4,P14_RP_100120M2,NB_D604,P14_SCOL30P,NB_A502,NB_B103,C702,P14_RPMAISON,P14_RPAPPART_ACH11,P14_RPMAISON_ACH19,C14_ACT1564_CS1,P14_RPAPPART_ACH90,NB_A405,P14_RPAPPART_ACH70,DEC_D214,C14_NE24F3,NB_D240,P14_RPMAISON_ACH90,DEC_PIMP14,DEC_PPEN14,DEC_RD14,NB_F116,C601,P14_RP_ACH11,C503,NB_A115,P14_RP_GRAT,P14_APPART,C14_ACTOCC1564_CS6,P14_NSAL15P_EMPLOY,P14_POP,C14_POP15P_CS3,P14_SAL15P_EMPAID,P14_RPMAISON_ACH45,C14_ACTOCC15P_PAS,NB_B313,P14_ACT5564,P14_PMEN_ANEM0002,NB_B206,NB_D238,DEC_D714,NB_A406,C14_FAMMONO,C14_PMEN_MENFAMMONO,P14_POP1117,C14_PMEN_CS1,NB_D209,P14_RPAPPART_ACH05,P14_RP_M30M2,C14_PMEN_CS8,DEC_MED14
2,P14_RP_ACH90,P14_RPMAISON_ACH90,C14_NE24F4P,DEC_D314,P14_RP_LOCHLMV,P14_RPAPPART_ACH90,P14_RETR1564,DEC_EQ14,P14_RSECOCC,NB_F103,NB_B201,C14_MEN_CS7,C14_ACTOCC15P_MAR,C14_MEN,P14_SCOL1114,DEC_PCHO14,P14_NSCOL15P,P14_NSCOL15P_DIPLMIN,P14_RP_ACH70,P14_POP0610,P14_POP1524_PSEUL,C14_MEN_CS3,P14_NSAL15P_TP,NB_B204,NB_B101,P14_CHOM5564,P14_POP_IMM,NB_D240,C509,P14_NSAL15P_EMPLOY,P14_RP_100120M2,C14_PMEN_CS5,DEC_D114,C14_MEN_CS4,P14_ANEM_RP_GRAT,P14_POP15P_MARIEE,P14_SCOL1517,NB_G104,P14_RP_2P,P14_RP_ACH45,C14_PMEN_MENSFAM,NB_B203,P14_SCOL0610,DEC_TP6014,P14_POP1114,P14_NPER_RP_GRAT,NB_B303,P14_NPER_RP_LOCHLMV,P14_NSCOL15P_CAPBEP,C14_POP15P_CS5,P14_RP_120M2P,C14_COUPAENF,NB_D213,P14_NBPI_RP_ANEM0509,P14_APPART,P14_CHOM1524,P14_NBPI_RP_ANEM10P,NB_A504,DEC_PTSA14,P14_RPMAISON_ACH11,NB_D233,C14_ACT1564_CS4,C14_FAMMONO,DEC_PBEN14,NB_A506,NB_A406,C14_MEN_CS6,NB_F116,P14_ACTOCC15P_ILT1,NB_A404,P14_ACTOCC15P_ILT4,C14_PMEN_MENCOUPAENF,P14_MEN_ANEM0509,C302,P14_POP0305,C14_PMEN_CS4,C14_POP15P_CS2,P14_RP_SDB,C14_MENCOUPSENF,C14_PMEN_CS6,NB_B202,C14_ACTOCC1564_CS3,NB_B308,P14_F3044,P14_RP_VOIT2P,P14_RP_3040M2,NB_F102,P14_RPAPPART_ACH70,NB_D237,P14_PMEN_ANEM10P,P14_POP4559,C14_PMEN_CS2,P14_RPMAISON_ACH70,P14_RP_GRAT,P14_RP_VOIT1P,NB_F111,NB_D209,P14_POP0014,C101,C701,C14_MENSFAM,NB_B103,P14_ANEM_RP_LOCHLMV,NB_D231,NB_D605,DEC_PIMP14,NB_F107,NB_B302,P14_PHORMEN,C14_MEN_CS1,NB_B310,P14_RP_ACH19,P14_SCOL2529,P14_ACTOCC15P_ILT3,C14_ACTOCC15P_DROU,NB_F304,C14_ACTOCC15P_TCOM,C14_ACTOCC15P_PAS,P14_POP,P14_RPAPPART_ACH11,NB_D602,P14_RP_CCCOLL,P14_LOGVAC,NB_D221,NB_F120,NB_F112,P14_NSCOL15P_SUP,P14_CHOM2554,NB_F114,P14_SAL15P_INTERIM,C104,C14_ACT1564_CS3,P14_RP_1P,NB_D243,P14_RPMAISON_ACH05,NB_A301,NB_B307,C14_PMEN_CS3,C14_NE24F3,NB_D210,P14_RP_ACH11,DEC_PPEN14,C14_ACTOCC1564_CS2,P14_RP_6080M2,P14_RPAPPART_ACH45,NB_A501,P14_RPAPPART_ACHTOT,C14_MENCOUPAENF,DENSITY,P14_RPAPPART_ACH05,NB_A121,P14_ACTOCC5564,C601,DEC_PAUT14,C201,NB_D238,P14_POP_ETR,NB_A401,DEC_Q114,NB_D404,P14_NSAL15P_AIDFAM,,
3,C14_COUPAENF,P14_POP_IMM,P14_RPAPPART_ACH19,P14_SCOL1114,C14_MEN_CS3,P14_SCOL1517,C14_ACTOCC15P_MAR,P14_POP1524_PSEUL,P14_POP0205,P14_RP_ACH19,DEC_PTSA14,P14_SCOL0610,P14_NSCOL15P_CAPBEP,P14_RSECOCC,C14_ACTOCC15P_VOIT,P14_POP0610,P14_RP_100120M2,P14_RP_ACH90,NB_D213,C14_PMEN_CS6,P14_CHOM2554,NB_G104,P14_RPMAISON_ACH70,P14_NSCOL15P,P14_POP2539,P14_POP4559,P14_POPMEN2554,P14_POP0305,P14_SCOL0205,P14_NBPI_RP_ANEM0509,P14_RPMAISON_ACH90,NB_D601,NB_F116,P14_MEN_ANEM0509,C14_MENCOUPSENF,P14_NSAL15P_TP,P14_NSCOL15P_DIPLMIN,P14_RPMAISON_ACHTOT,NB_A504,P14_ACTOCC1564,DENSITY,DEC_EQ14,P14_POP15P_MARIEE,C14_PMEN_MENFAM,P14_RPMAISON_ACH19,P14_ACTOCC15P_ILT2,C14_ACTOCC1564_CS6,P14_POP_ETR,P14_AINACT1564,C14_ACTOCC1564_CS5,P14_RPMAISON,P14_RP_5PP,DEC_D314,P14_RP_SDB,C14_POP15P_CS2,C509,DEC_PCHO14,C14_PMEN_CS5,P14_RETR1564,P14_RP_ACH70,P14_NBPI_RP_ANEM10P,P14_CHOM1524,P14_CHOM5564,P14_F3044,C14_ACTOCC1564_CS3,P14_NPER_RP_LOCHLMV,C14_ACT1564_CS5,C14_ACTOCC15P_PAS,P14_RP_GRAT,P14_RP_120M2P,C14_NE24F4P,C301,C14_MENSFAM,P14_NBPI_RPMAISON,NB_A121,C14_PMEN_MENCOUPAENF,NB_B203,P14_RP_VOIT2P,C502,NB_E101,C14_MENHSEUL,DEC_D214,P14_ANEM_RP_LOC,C14_MEN,P14_POPMEN15P,P14_PMEN_ANEM0509,NB_A402,P14_NPER_RP_PROP,C14_FAM,C14_ACT1564_CS4,NB_B303,P14_POP4054,P14_ACTOCC15P_ILT2P,NB_A301,C14_MEN_CS7,NB_B308,NB_A501,P14_SAL15P_CDI,P14_RP_80100M2,DEC_PBEN14,C14_POP15P_CS5,NB_F111,C14_PMEN_CS1,C14_ACT1564_CS1,NB_B302,C14_MEN_CS1,DEC_D114,NB_D243,C409,P14_NSAL15P_AIDFAM,C14_ACT1564_CS3,DEC_D714,P14_APPART,P14_SCOL2529,DEC_PIMP14,P14_MEN_ANEM0002,P14_POP0014,P14_PMEN_ANEM10P,P14_RPAPPART_ACH05,NB_D201,NB_A401,P14_NSAL15P,P14_POP,P14_RP_4060M2,P14_RP_ACH45,NB_F114,P14_NBPI_RPAPPART,P14_RP_M30M2,NB_D709,NB_D605,P14_RPAPPART_ACH45,P14_RP_ACH11,NB_A406,P14_RPAPPART,P14_RPAPPART_ACH70,DEC_TP6014,C701,P14_RP_4P,P14_PHORMEN,P14_RPMAISON_ACH11,C14_MEN_CS4,C14_ACTOCC15P_DROU,P14_SAL15P_APPR,DEC_D614,P14_RP_2P,DEC_RD14,P14_RP_GARL,NB_A115,C14_PMEN_MENSFAM,P14_RP_ACH05,NB_D603,P14_RP_LOCHLMV,P14_ACT1524,P14_POP2554,P14_RP_VOIT1P,,,,,,,,
4,P14_POP1524_PSEUL,P14_SCOL2529,C14_ACTOCC15P_MAR,P14_ETUD1564,P14_RPAPPART_ACH19,P14_NSAL15P_EMPLOY,C14_ACTOCC15P_TCOM,P14_CHOM5564,P14_RP_CINDELEC,P14_POP_IMM,P14_POP_ETR,P14_RP_120M2P,P14_CHOM1524,NB_A504,P14_RP_CCCOLL,C14_PMEN_CS8,P14_RPAPPART_ACH70,C14_PMEN_MENSFAM,P14_NSAL15P_TP,C14_MEN_CS3,P14_RPAPPART_ACHTOT,P14_RP_3040M2,P14_RPMAISON,P14_RP_ACH45,P14_MEN_ANEM0002,C14_MENSFAM,P14_RP_ACH19,P14_RP_ACH70,P14_RP_CCIND,P14_RP_LOCHLMV,DEC_PBEN14,P14_ACTOCC15P_ILT3,P14_CHOM2554,C14_PMEN_CS3,P14_RPAPPART_ACH45,C14_MENHSEUL,P14_RPMAISON_ACH45,P14_SCOL1824,P14_POP0205,C14_ACTOCC1564_CS3,P14_SCOL0610,P14_RP_GRAT,P14_RPMAISON_ACH90,C14_ACT1564_CS3,P14_RP_ACH11,C14_POP15P_CS3,P14_ACTOCC15P_ILT1,P14_RPAPPART_ACH11,C14_NE24F2,C14_COUPAENF,P14_SAL15P_INTERIM,P14_POP1114,C14_MENPSEUL,P14_NPER_RP_LOCHLMV,P14_RPAPPART_ACH90,C14_NE24F3,P14_NSCOL15P_DIPLMIN,P14_POP75P,P14_SCOL1114,NB_A402,NB_A403,NB_D201,P14_RP_M30M2,P14_ANEM_RP_LOCHLMV,P14_NPER_RP_GRAT,P14_AINACT1564,C14_ACTOCC1564_CS2,P14_RPMAISON_ACH70,C14_MEN,P14_LOGVAC,P14_ACTOCC5564,C14_NE24F1,P14_ANEM_RP_GRAT,P14_SCOL0205,P14_PHORMEN,C14_MEN_CS7,C14_MENFSEUL,P14_NSAL15P,P14_NSCOL15P_SUP,P14_NBPI_RPMAISON,P14_POP80P,P14_CHOM1564,P14_PMEN_ANEM0509,P14_NBPI_RPAPPART,P14_SAL15P_APPR,P14_PMEN_ANEM0002,P14_RPMAISON_ACH05,C14_PMEN_MENCOUPAENF,P14_F1529,P14_ACT5564,P14_SAL15P_EMPAID,P14_MEN_ANEM0509,C14_MEN_CS8,P14_POP,C14_NE24F4P,P14_POPMEN80P,C14_ACTOCC1564_CS6,NB_A507,P14_F3044,C14_MENCOUPAENF,P14_RSECOCC,P14_APPART,P14_RPAPPART_ACH05,P14_RP_1P,NB_A501,P14_RPAPPART,DEC_PAUT14,DEC_EQ14,NB_A203,P14_POP2529,P14_ANEM_RP_LOC,P14_MAISON,C14_ACTOCC15P_DROU,NB_E101,P14_ACTOCC15P_ILT5,P14_ACTOCC15P_ILT2,P14_NBPI_RP_ANEM0002,P14_RP_6080M2,P14_ACTOCC15P_ILT4,P14_POP0305,NB_B204,NB_F111,P14_RPMAISON_ACH19,C14_MEN_CS6,C14_PMEN_CS6,NB_F116,P14_ACTOCC15P_ILT2P,C14_PMEN_MENFAMMONO,C601,DENSITY,P14_SCOL30P,P14_RP_100120M2,P14_NSAL15P_INDEP,DEC_D114,NB_D221,P14_RP_2P,C14_PMEN_CS4,,,,,,,,,,,,,,,,,,,,,,,,,,
5,P14_RP_ACH19,P14_RPAPPART_ACH19,C14_PMEN_CS6,NB_A402,P14_SCOL2529,C14_POP15P_CS3,C14_ACT1564_CS2,NB_A301,P14_RPMAISON_ACH19,C14_MEN_CS6,NB_A504,P14_SAL15P_INTERIM,NB_B302,P14_NSAL15P_EMPLOY,P14_INACT1564,P14_RPMAISON_ACH45,C14_ACTOCC1564_CS3,P14_CHOM1564,P14_ACTOCC15P_ILT2,NB_A403,P14_NPER_RP_LOC,P14_SCOL1824,P14_SAL15P_EMPAID,P14_POP1117,P14_POP_ETR,DEC_PAUT14,NB_A506,C14_MEN_CS3,P14_RSECOCC,NB_D201,P14_RPMAISON_ACH11,NB_A505,P14_ANEM_RP_LOCHLMV,P14_RP_100120M2,C14_ACT1564_CS6,P14_RP_LOCHLMV,P14_ACTOCC15P_ILT1,C14_ACTOCC1564_CS2,P14_RP_4060M2,P14_ACTOCC15P_ILT5,C14_POP15P_CS6,C14_ACTOCC15P_TCOM,P14_ANEM_RP_GRAT,P14_RP_2P,P14_RPAPPART_ACH11,P14_NPER_RP_LOCHLMV,P14_POPMEN1524,NB_B202,P14_ETUD1564,P14_ACTOCC15P_ILT3,P14_POP0002,P14_POP_IMM,NB_A501,P14_RP_3040M2,P14_POP2529,P14_NSCOL15P_SUP,NB_A406,P14_SCOL0610,C14_MENSFAM,P14_NBPI_RP_ANEM0002,C14_NE24F4P,NB_A401,C14_ACTOCC15P_DROU,DEC_D814,C14_MEN_CS2,NB_B303,P14_NBPI_RP_ANEM10P,P14_POP1524_PSEUL,NB_D243,P14_RETR1564,C14_MENFAMMONO,P14_MEN_ANEM0002,P14_RP_CCCOLL,P14_NSAL15P_INDEP,P14_RP_CINDELEC,P14_RPMAISON_ACH05,P14_SAL15P_APPR,P14_RP_ACH90,P14_PHORMEN,P14_RP_GRAT,P14_NPER_RP_GRAT,P14_ACT1524,P14_POP2539,P14_ACTOCC5564,P14_POPMEN80P,P14_POP5564,P14_POP0305,C14_PMEN_CS3,C14_ACT1564_CS5,P14_PMEN_ANEM10P,C14_MENCOUPAENF,P14_ACT5564,P14_LOG,P14_POP0205,NB_D602,C14_PMEN_MENSFAM,C14_ACT1564_CS3,P14_CHOM1524,P14_RP_CCIND,NB_G102,P14_RP_ACH70,P14_SAL15P_CDD,P14_RPMAISON_ACH70,NB_A404,P14_POP,P14_RP_M30M2,C14_MEN_CS8,P14_RP_120M2P,P14_RP_ACH45,C14_ACTOCC15P_PAS,P14_RPAPPART,C14_PMEN_MENCOUPSENF,P14_PMEN_ANEM0002,P14_RP_4P,P14_ACTOCC15P_ILT2P,P14_SCOL30P,P14_MEN_ANEM0204,C14_NE24F2,P14_RP_ACH11,P14_NSAL15P_TP,DEC_EQ14,NB_A507,P14_AINACT1564,C14_ACTOCC1564_CS6,P14_ANEM_RP_LOC,P14_RPAPPART_ACH05,P14_NBPI_RPAPPART,P14_POP4054,P14_RPAPPART_ACH70,NB_D235,P14_POP0610,C14_MENFSEUL,C14_PMEN_MENFAMMONO,P14_PMEN_ANEM0509,C14_PMEN_CS2,C14_MEN_CS5,P14_RPAPPART_ACH90,P14_POP1529,P14_ACTOCC15P_TP,P14_RP_5PP,DEC_PBEN14,P14_RP_1P,,,,,,,,,,,,,,,,,,,,,
0,P14_RPMAISON_ACHTOT,P14_NBPI_RPMAISON,P14_RP_120M2P,P14_ANEM_RP_PROP,P14_RPMAISON,C14_MEN_CS7,P14_NPER_RP_PROP,DEC_D114,C14_COUPAENF,C14_PMEN_MENCOUPSENF,C14_ACTOCC15P_MAR,P14_RPMAISON_ACH90,P14_RPMAISON_ACH70,P14_RPMAISON_ACH19,P14_MAISON,P14_POP0014,C14_MENCOUPAENF,C14_MENCOUPSENF,P14_NBPI_RP_ANEM10P,NB_B202,P14_RP_100120M2,P14_RP_5PP,P14_RPMAISON_ACH45,C14_PMEN_CS1,P14_RP_PROP,NB_D240,P14_RP_ACH45,P14_NBPI_RPAPPART,NB_E101,DEC_PCHO14,,,,,,
1,DENSITY,P14_PHORMEN,P14_NBPI_RPMAISON,DEC_EQ14,P14_POP_ETR,P14_RP_LOCHLMV,P14_ANEM_RP_GRAT,C14_ACTOCC15P_MAR,P14_POP0205,NB_A504,NB_E101,P14_NPER_RP_PROP,C14_COUPAENF,P14_RP_CCIND,C14_MENCOUPAENF,P14_NPER_RP_LOCHLMV,NB_A301,P14_ACTOCC15P_ILT2,P14_SCOL1517,P14_NBPI_RP_ANEM10P,NB_F121,P14_RP_ACH90,P14_RP_ACH19,DEC_D614,P14_RPAPPART_ACH19,P14_PMEN_ANEM10P,P14_NPER_RP_GRAT,C14_MEN_CS8,P14_POP1524_PSEUL,P14_RP_120M2P,DEC_PTSA14,P14_RP_1P,DEC_TP6014,NB_D202,P14_RSECOCC,
2,P14_RP_ACH90,P14_RPMAISON_ACH90,C14_NE24F4P,DEC_D314,P14_RP_LOCHLMV,P14_RPAPPART_ACH90,P14_RETR1564,DEC_EQ14,P14_RSECOCC,NB_F103,NB_B201,C14_MEN_CS7,C14_ACTOCC15P_MAR,C14_MEN,P14_SCOL1114,DEC_PCHO14,P14_NSCOL15P,P14_NSCOL15P_DIPLMIN,P14_RP_ACH70,P14_POP0610,DEC_PTSA14,C14_MENSFAM,NB_F116,NB_F111,P14_NPER_RP_GRAT,C14_PMEN_CS6,NB_D240,NB_G104,NB_B103,P14_RPAPPART_ACH70,P14_ANEM_RP_LOCHLMV,NB_D231,NB_D605,NB_A504,DEC_PIMP14,NB_F107
3,C14_COUPAENF,P14_POP_IMM,P14_RPAPPART_ACH19,P14_SCOL1114,C14_MEN_CS3,P14_SCOL1517,C14_ACTOCC15P_MAR,P14_POP1524_PSEUL,P14_POP0205,P14_RP_ACH19,DEC_PTSA14,P14_SCOL0610,P14_NSCOL15P_CAPBEP,P14_RSECOCC,C14_ACTOCC15P_VOIT,P14_POP0610,P14_RP_100120M2,P14_RP_ACH90,NB_D213,C14_PMEN_CS6,P14_RPMAISON_ACH90,P14_ACTOCC15P_ILT2P,C14_POP15P_CS5,DENSITY,NB_F111,P14_RP_80100M2,C14_PMEN_CS1,C14_MENSFAM,C14_ACT1564_CS1,NB_B302,NB_A504,C14_ACTOCC15P_PAS,C14_MEN_CS1,NB_F116,,
4,P14_POP1524_PSEUL,P14_SCOL2529,C14_ACTOCC15P_MAR,P14_ETUD1564,P14_RPAPPART_ACH19,P14_NSAL15P_EMPLOY,C14_ACTOCC15P_TCOM,P14_CHOM5564,P14_RP_CINDELEC,P14_POP_IMM,P14_POP_ETR,P14_RP_120M2P,P14_CHOM1524,NB_A504,P14_RP_CCCOLL,C14_PMEN_CS8,P14_RPAPPART_ACH70,C14_PMEN_MENSFAM,P14_NSAL15P_TP,C14_MEN_CS3,P14_RP_ACH19,C14_MENSFAM,C14_MEN_CS8,P14_RSECOCC,P14_SAL15P_EMPAID,P14_RP_3040M2,P14_RP_GRAT,C14_NE24F3,P14_LOGVAC,C14_NE24F4P,P14_POP,P14_APPART,,,,
5,P14_RP_ACH19,P14_RPAPPART_ACH19,C14_PMEN_CS6,NB_A402,P14_SCOL2529,C14_POP15P_CS3,C14_ACT1564_CS2,NB_A301,P14_RPMAISON_ACH19,C14_MEN_CS6,NB_A504,P14_SAL15P_INTERIM,NB_B302,P14_NSAL15P_EMPLOY,P14_INACT1564,P14_RPMAISON_ACH45,C14_ACTOCC1564_CS3,P14_CHOM1564,P14_ACTOCC15P_ILT2,NB_A403,P14_ANEM_RP_LOCHLMV,P14_RP_ACH70,P14_POP_ETR,P14_ACTOCC15P_ILT3,C14_ACT1564_CS3,P14_SAL15P_APPR,P14_SAL15P_CDD,P14_ANEM_RP_GRAT,P14_RP_CINDELEC,C14_MEN_CS3,P14_RPMAISON_ACH70,P14_NPER_RP_GRAT,NB_A404,P14_RP_CCCOLL,P14_RSECOCC,
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