# default_exp geoms

⚠️ The writing is a work in progress. The functions work. ⚠️

Please read everything found on the mainpage before continuing; disclaimer and all.

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About this Tutorial:

Whats Inside?

The Tutorial

In this notebook, the basics of working with geographic data are introduced.

  • Reading in data (points/ geoms) -- Convert lat/lng columns to point coordinates -- Geocoding address to coordinates -- Changing coordinate reference systems -- Connecting to PostGisDB's
  • Basic Operations
  • Saving shape data
  • Get Polygon Centroids
  • Working with Points and Polygons -- Map Points and Polygons -- Get Points in Polygons -- Create Choropleths -- Create Heatmaps (KDE?)

Objectives

By the end of this tutorial users should have an understanding of:

  • How to read in and process geo-data asa geo-dataframe.
  • The Coordinate Reference System and Coordinate Encoding
  • Basic geo-visualization strategies

Background

An expansice discursive on programming and cartography can be found here

Datatypes and Geo-data

Geographic data must be encoded properly order to attain the full potential of the spatial nature of your geographic data.

If you have read in a dataset using pandas it's data type will be a Dataframe.

It may be converted into a Geo-Dataframe using Geopandas as demonstrated in the sections below.

You can check a variables at any time using the [dtype]((https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dtypes.html) command:

yourGeoDataframe.dtype

Coordinate Reference Systems (CRS)

Make sure the appropriate spatial Coordinate Reference System (CRS) is used when reading in your data!

ala wiki:

A spatial reference system (SRS) or coordinate reference system (CRS) is a coordinate-based local, regional or global system used to locate geographical entities

CRS 4326 is the CRS most people are familar with when refering to latiude and longitudes.

Baltimore's 4326 CRS should be at (39.2, -76.6)

BNIA uses CRS 2248 internally

Additional Information: https://docs.qgis.org/testing/en/docs/gentle_gis_introduction/coordinate_reference_systems.html

Ensure your geodataframes' coordinates are using the same CRS using the geopandas command:

yourGeoDataframe.CRS

Coordinate Encoding

When first recieving a spatial dataset, the spatial column may need to be encoded to convert its 'text' data type values into understood 'coordinate' data types before it can be understood/processed accordingly.

Namely, there are two ways to encode text into coordinates:

  • df[geom] = df[geom].apply(lambda x: loads( str(x) ))
  • df[geom] = [Point(xy) for xy in zip(df.x, df.y)]

The first approach can be used for text taking the form "Point(-76, 39)" and will encode the text too coordinates. The second approach is useful when creating a point from two columns containing lat/lng information and will create Point coordinates from the two columns.

More on this later

Raster Vs Vector Data

There exists two types of Geospatial Data, Raster and Vector. Both have different file formats.

This lab will only cover vector data.

Vector Data

Vector Data: Individual points stored as (x,y) coordinates pairs. These points can be joined to create lines or polygons.

Format of Vector data

Esri Shapefile — .shp, .dbf, .shx Description - Industry standard, most widely used. The three files listed above are needed to make a shapefile. Additional file formats may be included.

Geographic JavaScript Object Notation — .geojson, .json Description — Second most popular, Geojson is typically used in web-based mapping used by storing the coordinates as JSON.

Geography Markup Language — .gml Description — Similar to Geojson, GML has more data for the same amount of information.

Google Keyhole Markup Language — .kml, .kmz Description — XML-based and predominantly used for google earth. KMZ is a the newer, zipped version of KML.

Raster Data

Raster Data: Cell-based data where each cell represent geographic information. An Aerial photograph is one such example where each pixel has a color value

Raster Data Files: GeoTIFF — .tif, .tiff, .ovr ERDAS Imagine — .img IDRISI Raster — .rst, .rdc

Information Sourced From: https://towardsdatascience.com/getting-started-with-geospatial-works-1f7b47955438

Vector Data: Census Geographic Data:

  • Geographic Coordinate Data is provided by the census and compliments their census geographies
  • https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2010.html
  • https://www.census.gov/programs-surveys/acs/geography-acs/geography-boundaries-by-year.html
  • Bnia created and provides for free geographic boundary data that compliment these CSA's

Guided Walkthrough

SETUP:

Import Modules

!pip install geopandas !pip install VitalSigns!apt install libspatialindex-dev !pip install rtree# @title Run: Install Modules ! pip install geopy ! pip install geoplot #hide t = """ !pip install nbdev from google.colab import drive drive.mount('/content/drive') %cd /content/drive/My Drive/'Software Development Documents'/dataplay/ """ # !pip install dataplayMounted at /content/drive /content/drive/My Drive/Software Development Documents/dataplay # export # @title Run: Import Modules # These imports will handle everything import os import sys import csv import numpy as np import pandas as pd import pyproj from pyproj import Proj, transform # conda install -c conda-forge proj4 from shapely.geometry import LineString # from shapely import wkb # https://pypi.org/project/geopy/ import folium # In case file is KML, enable support import fiona fiona.drvsupport.supported_drivers['kml'] = 'rw' fiona.drvsupport.supported_drivers['KML'] = 'rw' import psycopg2import matplotlib.pyplot as plt import IPython from IPython.core.display import HTML import os from branca.colormap import linear#export import pandas as pd import geopandas as gpd from geopandas import GeoDataFrame from shapely.geometry import Point from shapely.wkt import loads from geopy.geocoders import Nominatim from IPython.display import clear_output from folium import plugins from folium.plugins import TimeSliderChoropleth from folium.plugins import MarkerCluster from dataplay import merge from dataplay import intaker from VitalSigns import acsDownload

Configure Enviornment

# This will just beautify the output pd.set_option('display.expand_frame_repr', False) pd.set_option('display.precision', 2) from IPython.core.interactiveshell import InteractiveShell InteractiveShell.ast_node_interactivity = "all" # pd.set_option('display.expand_frame_repr', False) # pd.set_option('display.precision', 2) # pd.reset_option('max_colwidth') pd.set_option('max_colwidth', 50) # pd.reset_option('max_colwidth')#hide # %matplotlib inline # !jupyter nbextension enable --py widgetsnbextension

Retrieve GIS Data

As mentioned earlier:

When you use a pandas function to 'read-in' a dataset, the returned value is of a datatype called a 'Dataframe'.

We need a 'Geo-Dataframe', however, to effectively work with spatial data.

While Pandas does not support Geo-Dataframes; Geo-pandas does.

Geopandas has everything you love about pandas, but with added support for geo-spatial data.

Principle benefits of using Geopandas over Pandas when working with spatial data:

  • The geopandas plot function will now render a map by default using your 'spatial-geometries' column.
  • Libraries exist spatial-operations and interactive map usage.

There are many ways to have our spatial-data be read-in using geo-pandas into a geo-dataframe.

Namely, it means reading in Geo-Spatial-data from a:

  1. (.geojson or .shp) file directly using Geo-pandas
  2. (.csv, .json) file using Pandas and convert it to Geo-Pandas
  • using a prepared 'geometry' column
  • by transformting latitude and longitude columns into a 'geometry' column.
  • acquiring coordinates from an address
  • mapping your non-spatial-data to data-with-space
  1. Connecting to a DB

We will review each one below

Approach 1: Reading in Data Directly

If you are using Geopandas, direct imports only work with geojson and shape files.

spatial coordinate data is properly encoded with these types of files soas to make them particularly easy to use.

You can perform this using geopandas' read_file() function.

# This dataset is taken from the public database provided by BNIAJFI hosted by Esri / ArcGIS # BNIA ArcGIS Homepage: https://data-bniajfi.opendata.arcgis.com/ csa_gdf = intaker.Intake.getData("https://services1.arcgis.com/mVFRs7NF4iFitgbY/ArcGIS/rest/services/Hhchpov/FeatureServer/0/query?where=1%3D1&outFields=*&returnGeometry=true&f=pgeojson")

As you can see, the resultant variable is of type GeoDataFrame.

type(csa_gdf)

GeoDataFrames are only possible when one of the columns are of a 'Geometry' Datatype

csa_gdf.dtypes

Awesome. So that means, now you can plot maps all prety like:

csa_gdf.plot(column='hhchpov15')

And now lets take a peak at the raw data:

csa_gdf.head(1)

I'll show you more ways to save the data later, but for our example in the next section to work, we need a csv.

We can make one by saving the geo-dataframe avove using the to_gdf function.

The spatial data will be stored in an encoded form that will make it easy to re-open up in the future.

csa_gdf.to_csv('example.csv')

Approach 2: Converting Pandas into Geopandas

Approach 2: Method 1: Convert using a pre-formatted 'geometry' column

This approach loads a map using a geometry column

In our previous example, we saved a geo-dataframe as a csv.

Now lets re-open it up using pandas!

# A url to a public Dataset. url = "example.csv" geom = 'geometry' # An example of loading in an internal BNIA file crs = {'init' :'epsg:2248'} # Read in the dataframe csa_gdf = intaker.Intake.getData(url)

Great!

But now what?

Well, for starters, regardless of the project you are working on: It's always a good idea to inspect your data.

This is particularly important if you don't know what you're working with.

csa_gdf.head(1)

Take notice of how the geometry column has a special.. foramatting.

All spatial data must take on a similar form encoding for it to be properly interpretted as a spatial data-type.

As far as I can tell, This is near-identical to the table I printed out in our last example.

BUT WAIT!

You'll notice, that if I run the plot function a pretty map will not de-facto appear

csa_gdf.plot()

Why is this? Because you're not working with a geo-dataframe but just a dataframe!

Take a look:

type(csa_gdf)

Okay... So thats not right..

What can we do about this?

Well for one, our spatial data (in the geometry-column) is not of the right data-type even though it takes on the right form.

csa_gdf.dtypes

Ok. So how do we change it? Well, since it's already been properly encoded...

You can convert a columns data-type from an object (or whatver else) to a 'geometry' using the loads function.

In the example below, we convert the datatypes for all records in the 'geometry' column

# Convert the geometry column datatype from a string of text into a coordinate datatype csa_gdf[geom] = csa_gdf[geom].apply(lambda x: loads( str(x) ))

Thats all! Now lets see the geometry columns data-type and the entire tables's data-type

csa_gdf.dtypestype(csa_gdf)

As you can see, we have a geometry column of the right datatype, but our table is still only just a dataframe.

But now, you are ready to convert your entire pandas dataframe into a geo-dataframe.

You can do that by running the following function:

# Process the dataframe as a geodataframe with a known CRS and geom column csa_gdf = GeoDataFrame(csa_gdf, crs=crs, geometry=geom)

Aaaand BOOM.

csa_gdf.plot(column='hhchpov18')

goes the dy-no-mite

type(csa_gdf)

Approach 2: Method 2: Convert Column(s) to Coordinate

Approach 2: Method 2: Example: A Generic Outline

This is the generic example but it will not work since no URL is given.

# More Information: https://geopandas.readthedocs.io/en/latest/gallery/create_geopandas_from_pandas.html#from-longitudes-and-latitudes # If your data has coordinates in two columns run this cell # It will create a geometry column from the two. # A public dataset is not provided for this example and will not run. # Load DF HERE. Accidently deleted the link. Need to refind. # Just rely on example 2 for now. """ exe_df['x'] = pd.to_numeric(exe_df['x'], errors='coerce') exe_df['y'] = pd.to_numeric(exe_df['y'], errors='coerce') # exe_df = exe_df.replace(np.nan, 0, regex=True) # An example of loading in an internal BNIA file geometry=[Point(xy) for xy in zip(exe_df.x, exe_df.y)] exe_gdf = gpd.GeoDataFrame( exe_df.drop(['x', 'y'], axis=1), crs=crs, geometry=geometry) """
Approach 2: Method 2: Example: Geoloom

Since I do not readily have a dataset with lat and long's I will have to make one.

We can split the coordinates from a geodataframe like so...

# Alternate Primary Table # Table: Geoloom, # Columns: # In this example, we are going to read in a shapefile geoloom_gdf = gpd.read_file("https://services1.arcgis.com/mVFRs7NF4iFitgbY/ArcGIS/rest/services/Geoloom_Crowd/FeatureServer/0/query?where=1%3D1&outFields=*&returnGeometry=true&f=pgeojson"); # then create columns for its x and y coords geoloom_gdf['POINT_X'] = geoloom_gdf['geometry'].centroid.x geoloom_gdf['POINT_Y'] = geoloom_gdf['geometry'].centroid.y # Now lets just drop the geometry column and save it to have our example dataset. geoloom_gdf = geoloom_gdf.dropna(subset=['geometry']) geoloom_gdf.to_csv('example.csv')

The first thing you will want to do when given a dataset with a coordinates column is ensure its datatype.

geoloom_df = pd.read_csv('example.csv') # We already know the x and y columns because we just saved them as such. geoloom_df['POINT_X'] = pd.to_numeric(geoloom_df['POINT_X'], errors='coerce') geoloom_df['POINT_Y'] = pd.to_numeric(geoloom_df['POINT_Y'], errors='coerce') # df = df.replace(np.nan, 0, regex=True) # And filter out for points only in Baltimore City. geoloom_df = geoloom_df[ geoloom_df['POINT_Y'] > 39.3 ] geoloom_df = geoloom_df[ geoloom_df['POINT_Y'] < 39.5 ]# An example of loading in an internal BNIA file crs = {'init' :'epsg:2248'} geometry=[Point(xy) for xy in zip(geoloom_df['POINT_X'], geoloom_df['POINT_Y'])] geoloom_gdf = gpd.GeoDataFrame( geoloom_df.drop(['POINT_X', 'POINT_Y'], axis=1), crs=crs, geometry=geometry) # 39.2904° N, 76.6122°geoloom_gdf.head(1)

Heres a neat trick to make it more presentable, because those points mean nothing to me.

# Create our base layer. ax = csa_gdf.plot(column='hhchpov18', edgecolor='black') # now plot our points over it. geoloom_gdf.plot(ax=ax, color='red') plt.show()
Approach 2: Method 3: Using a Crosswalk (Need Crosswalk on Esri)

When you want to merge two datasets that do not share a common column, it is often useful to create a 'crosswalk' file that 'maps' records between two datasets. We can do this to append spatial data when a direct merge is not readily evident.

Check out this next example where we pull ACS Census data and use its 'tract' column and map it to a community. We can then aggregate the points along a the communities they belong to and map it on a choropleth!

We will set up our ACS query variables right here for easy changing

# Our download function will use Baltimore City's tract, county and state as internal paramters # Change these values in the cell below using different geographic reference codes will change those parameters tract = '*' county = '510' # '059' # 153 '510' state = '24' #51 # Specify the download parameters the function will receieve here tableId = 'B19049' # 'B19001' year = '17' saveAcs = True

And now we will call the function with those variables and check out the result

retrieve_acs_data = acsDownload.retrieve_acs_data IPython.core.display.HTML("") # state, county, tract, tableId, year, saveOriginal, save df = retrieve_acs_data(state, county, tract, tableId, year, saveAcs) df.head(1) df.to_csv('tracts_data.csv')

This contains the CSA labels we will map our tracts to. This terminal command will download it

!wget https://raw.githubusercontent.com/bniajfi/bniajfi/main/CSA-to-Tract-2010.csv

Here

crosswalk = pd.read_csv('CSA-to-Tract-2010.csv') crosswalk.tail(1)from dataplay import merge mergeDatasets = merge.mergeDatasets merged_df_geom = mergeDatasets(left_ds=df, right_ds=crosswalk, crosswalk_ds=False, left_col='tract', right_col='TRACTCE10', crosswalk_left_col = False, crosswalk_right_col = False, merge_how='outer', # left right or columnname to retrieve interactive=False) merged_df_geom.head(1)import geopandas as gpd Hhchpov = gpd.read_file("https://services1.arcgis.com/mVFRs7NF4iFitgbY/ArcGIS/rest/services/Hhchpov/FeatureServer/0/query?where=1%3D1&outFields=*&returnGeometry=true&f=pgeojson") Hhchpov = Hhchpov[['CSA2010', 'hhchpov15', 'hhchpov16', 'hhchpov17', 'hhchpov18', 'geometry']] Hhchpov.to_file("Hhchpov.geojson", driver='GeoJSON') Hhchpov.to_csv('Hhchpov.csv') gpd.read_file("Hhchpov.geojson").head(1)# A simple merge # df.merge(crosswalk, left_on='tract', right_on='TRACTCE10')

A simple example of how this would work

# A simple merge merged_df = mergeDatasets(left_ds=merged_df_geom, right_ds=Hhchpov, crosswalk_ds=False, left_col='CSA2010', right_col='CSA2010', crosswalk_left_col = False, crosswalk_right_col = False, merge_how='outer', # left right or columnname to retrieve interactive=False)# geoms.readInGeometryData(url='Hhchpov.geojson').head(0) # The attributes are what we will use. in_crs = 2248 # The CRS we recieve our data out_crs = 4326 # The CRS we would like to have our data represented as geom = 'geometry' # The column where our spatial information lives. # To create this dataset I had to commit a full outer join. # In this way geometries will be included even if there merge does not have a direct match. # What this will do is that it means at least one (near) empty record for each community will exist that includes (at minimum) the geographic information and name of a Community. # That way if no point level information existed in the community, that during the merge the geoboundaries are still carried over. # Primary Table # Description: I created a public dataset from a google xlsx sheet 'Bank Addresses and Census Tract'. # Table: FDIC Baltimore Banks # Columns: Bank Name, Address(es), Census Tract left_ds = 'tracts_data.csv' left_col = 'tract' # Crosswalk Table # Table: Crosswalk Census Communities # 'TRACT2010', 'GEOID2010', 'CSA2010' crosswalk_ds = 'CSA-to-Tract-2010.csv' use_crosswalk = True crosswalk_left_col = 'TRACTCE10' crosswalk_right_col = 'CSA2010' # Secondary Table # Table: Baltimore Boundaries => HHCHPOV # 'TRACTCE10', 'GEOID10', 'CSA', 'NAME10', 'Tract', 'geometry' right_ds = 'Hhchpov.geojson' right_col ='CSA2010' interactive = True merge_how = 'outer' # reutrns a pandas dataframe mergedf = merge.mergeDatasets( left_ds=left_ds, left_col=left_col, crosswalk_ds=crosswalk_ds, crosswalk_left_col = crosswalk_left_col, crosswalk_right_col = crosswalk_right_col, right_ds=right_ds, right_col=right_col, merge_how=merge_how, interactive = interactive )mergedf.dtypes# Convert the geometry column datatype from a string of text into a coordinate datatype # mergedf[geom] = mergedf[geom].apply(lambda x: loads( str(x) ) ) # Process the dataframe as a geodataframe with a known CRS and geom column mergedGdf = GeoDataFrame(mergedf, crs=in_crs, geometry=geom) mergedGdf.plot()
Approach 2: Method 4: Geocoding Addresses and Landmarks to Coordinates

Sometimes (usually) we just don't have the coordinates of a place, but we do know it's address or that it is an established landmark.

In such cases we attempt 'geo-coding' these points in an automated manner.

While convenient, this process is error prone, so be sure to check it's work!

For this next example to take place, we need a dataset that has a bunch of addresses.

We can use the geoloom dataset from before in this example. We'll just drop geo'spatial data.

geoloom = gpd.read_file("https://services1.arcgis.com/mVFRs7NF4iFitgbY/ArcGIS/rest/services/Geoloom_Crowd/FeatureServer/0/query?where=1%3D1&outFields=*&returnGeometry=true&f=pgeojson"); geoloom = geoloom.dropna(subset=['geometry']) geoloom = geoloom.drop(columns=['geometry','GlobalID', 'POINT_X', 'POINT_Y']) geoloom.head(1)

But if for whatever reason the link is down, you can use this example dataframe mapping just some of the many malls in baltimore.

address_df = pd.DataFrame({ 'Location' : pd.Series([ '100 N. Holliday St, Baltimore, MD 21202', '200 E Pratt St, Baltimore, MD', '2401 Liberty Heights Ave, Baltimore, MD', '201 E Pratt St, Baltimore, MD', '3501 Boston St, Baltimore, MD', '857 E Fort Ave, Baltimore, MD', '2413 Frederick Ave, Baltimore, MD' ]), 'Address' : pd.Series([ 'Baltimore City Council', 'The Gallery at Harborplace', 'Mondawmin Mall', 'Harborplace', 'The Shops at Canton Crossing', 'Southside Marketplace', 'Westside Shopping Center' ]) }) address_df.head()

You can use either the Location or Address column to perform the geo-coding on.

address_df = geoloom.copy() addrCol = 'Location'

This function takes a while. The less columns/data/records the faster it executes.

# More information vist: https://geopy.readthedocs.io/en/stable/#module-geopy.geocoders # In this example we retrieve and map a dataset with no lat/lng but containing an address # In this example our data is stored in the 'STREET' attribute geometry = [] geolocator = Nominatim(user_agent="my-application") for index, row in address_df.iterrows(): # We will try and return an address for each Street Name try: # retrieve the geocoded information of our street address geol = geolocator.geocode(row[addrCol], timeout=None) # create a mappable coordinate point from the response object's lat/lang values. pnt = Point(geol.longitude, geol.latitude) # Append this value to the list of geometries geometry.append(pnt) except: # If no street name was found decide what to do here. # df.loc[index]['geom'] = Point(0,0) # Alternate method geometry.append(Point(0,0)) # Finally, we stuff the geometry data we created back into the dataframe address_df['geometry'] = geometryaddress_df.head(1)

Awesome! Now convert the dataframe into a geodataframe and map it!

gdf = gpd.GeoDataFrame( address_df, geometry=geometry) gdf = gdf[ gdf.centroid.y > 39.3 ] gdf = gdf[ gdf.centroid.y < 39.5 ]# Create our base layer. ax = csa_gdf.plot(column='hhchpov18', edgecolor='black') # now plot our points over it. geoloom_gdf.plot(ax=ax, color='red')

A litte later down, we'll see how to make this even-more interactive.

Approach 3: Connecting to a PostGIS database

In the following example pulls point geodata from a Postgres database.

We will pull the postgres point data in two manners.

  • SQL query where an SQL query uses ST_Transform(the_geom,4326) to transform the_geom's CRS from a DATABASE Binary encoding into standard Lat Long's
  • Using a plan SQL query and performing the conversion using gpd.io.sql.read_postgis() to pull the data in as 2248 and convert the CRS using .to_crs(epsg=4326)
  • These examples will not work in colabs as their is no local database to connect to and has been commented out for that reason
# This Notebook can be downloaded to connect to a database ''' conn = psycopg2.connect(host='', dbname='', user='', password='', port='') # DB Import Method One sql1 = 'SELECT the_geom, gid, geogcode, ooi, address, addrtyp, city, block, lot, desclu, existing FROM housing.mdprop_2017v2 limit 100;' pointData = gpd.io.sql.read_postgis(sql1, conn, geom_col='the_geom', crs=2248) pointData = pointData.to_crs(epsg=4326) # DB Import Method Two sql2 = 'SELECT ST_Transform(the_geom,4326) as the_geom, ooi, desclu, address FROM housing.mdprop_2017v2;' pointData = gpd.GeoDataFrame.from_postgis(sql2, conn, geom_col='the_geom', crs=4326) pointData.head() pointData.plot() '''

Basics Operations

Inspection

def geomSummary(gdf): return type(gdf), gdf.crs, gdf.columns; # for p in df['Tract'].sort_values(): print(p) geomSummary(csa_gdf)

Converting CRS

# Convert the CRS of the dataset into one you desire # The gdf must be loaded with a known crs in order for the to_crs conversion to work # We use this often to converting BNIAs custom CRS to the common type out_crs = 4326 csa_gdf = csa_gdf.to_crs(epsg=out_crs)

Saving

# Here is code to comit a simple save filename = 'TEST_FILE_NAME' csa_gdf.to_file(f"{filename}.geojson", driver='GeoJSON')# Here is code to save this new projection as a geojson file and read it back in csa_gdf = csa_gdf.to_crs(epsg=2248) #just making sure csa_gdf.to_file(filename+'.shp', driver='ESRI Shapefile') csa_gdf = gpd.read_file(filename+'.shp')

Draw Tool

import folium from folium.plugins import Draw # Draw tool. Create and export your own boundaries m = folium.Map() draw = Draw() draw.add_to(m) m = folium.Map(location=[39.28759453969165, -76.61278931706487], zoom_start=12) draw = Draw(export=True) draw.add_to(m) # m.save(os.path.join('results', 'Draw1.html')) m

Geometric Manipulations

Boundary

newcsa = csa_gdf.copy() newcsa['geometry'] = csa_gdf.boundary newcsa.plot(column='CSA2010' )

envelope

newcsa = csa_gdf.copy() newcsa['geometry'] = csa_gdf.envelope newcsa.plot(column='CSA2010' )

convex_hull

newcsa = csa_gdf.copy() newcsa['geometry'] = csa_gdf.convex_hull newcsa.plot(column='CSA2010' ) # , cmap='OrRd', scheme='quantiles' # newcsa.boundary.plot( )

simplify

newcsa = csa_gdf.copy() newcsa['geometry'] = csa_gdf.simplify(30) newcsa.plot(column='CSA2010' )

buffer

newcsa = csa_gdf.copy() newcsa['geometry'] = csa_gdf.buffer(0.01) newcsa.plot(column='CSA2010' )

rotate

newcsa = csa_gdf.copy() newcsa['geometry'] = csa_gdf.rotate(30) newcsa.plot(column='CSA2010' )

scale

newcsa = csa_gdf.copy() newcsa['geometry'] = csa_gdf.scale(3, 2) newcsa.plot(column='CSA2010' )

skew

newcsa = csa_gdf.copy() newcsa['geometry'] = csa_gdf.skew(1, 10) newcsa.plot(column='CSA2010' )

Advanced

Create Geospatial Functions

Operations:

  • Reading in data (points/ geoms) -- Convert lat/lng columns to point coordinates -- Geocoding address to coordinates -- Changing coordinate reference systems -- Connecting to PostGisDB's
  • Basic Operations
  • Saving shape data
  • Get Polygon Centroids
  • Working with Points and Polygons -- Map Points and Polygons -- Get Points in Polygons

Input(s):

  • Dataset (points/ bounds) url
  • Points/ bounds geometry column(s)
  • Points/ bounds crs's
  • Points/ bounds mapping color(s)
  • New filename

Output: File

This function will handle common geo spatial exploratory methods. It covers everything discussed in the basic operations and more!

# export # # Work With Geometry Data # Description: geomSummary, getPointsInPolygons, getPolygonOnPoints, mapPointsInPolygons, getCentroids # def workWithGeometryData(method=False, df=False, polys=False, ptsCoordCol=False, polygonsCoordCol=False, polyColorCol=False, polygonsLabel='polyOnPoint', pntsClr='red', polysClr='white', interactive=False): def geomSummary(df): return type(df), df.crs, df.columns; def getCentroid(df, col): return df[col].representative_point() # df['geometry'].centroid # To 'import' a script you wrote, map its filepath into the sys def getPolygonOnPoints(pts, polygons, ptsCoordCol, polygonsCoordCol, polygonsLabel, interactive): count = 0 # We're going to keep a list of how many points we find. boundaries = [] # Loop over polygons with index i. for i, pt in pts.iterrows(): # print('Searching for point within Geom:', pt ) # Only one Label is accepted. poly_on_this_point = [] # Now loop over all polygons with index j. for j, poly in polygons.iterrows(): if poly[polygonsCoordCol].contains(pt[ptsCoordCol]): # Then it's a hit! Add it to the list poly_on_this_point.append(poly[polygonsLabel]) count = count + 1 # pts = pts.drop([j]) # We could do all sorts, like grab a property of the # points, but let's just append the number of them. boundaries.append(poly_on_this_point) clear_output(wait=True) # Add the number of points for each poly to the dataframe. pts = pts.assign(CSA2010 = boundaries) if (interactive): print( 'Total Points: ', (pts.size / len(pts.columns) ) ) print( 'Total Points in Polygons: ', count ) print( 'Prcnt Points in Polygons: ', count / (pts.size / len(pts.columns) ) ) return pts # To 'import' a script you wrote, map its filepath into the sys def getPointsInPolygons(pts, polygons, ptsCoordCol, polygonsCoordCol, interactive): count = 0 total = pts.size / len(pts.columns) # We're going to keep a list of how many points we find. pts_in_polys = [] # Loop over polygons with index i. for i, poly in polygons.iterrows(): # print('Searching for point within Geom:', poly ) # Keep a list of points in this poly pts_in_this_poly = 0 # Now loop over all points with index j. for j, pt in pts.iterrows(): if poly[polygonsCoordCol].contains(pt[ptsCoordCol]): # Then it's a hit! Add it to the list, pts_in_this_poly += 1 # and drop it so we have less hunting. # pts = pts.drop([j]) # We could do all sorts, like grab a property of the # points, but let's just append the number of them. pts_in_polys.append(pts_in_this_poly) if (interactive): print('Found this many points within the Geom:', pts_in_this_poly ) count += pts_in_this_poly clear_output(wait=True) # Add the number of points for each poly to the dataframe. polygons['pointsinpolygon'] = gpd.GeoSeries(pts_in_polys) if (interactive): print( 'Total Points: ', total ) print( 'Total Points in Polygons: ', count ) print( 'Prcnt Points in Polygons: ', count / total ) return polygons def mapPointsandPolygons(pnts, polys, pntsCl, polysClr, polyColorCol): print('mapPointsandPolygons'); # We restrict to South America. ax = 1 if polyColorCol: ax = polys.plot( column=polyColorCol, legend=True) else: ax = polys.plot( color=polysClr, edgecolor='black') # We can now plot our ``GeoDataFrame``. pnts.plot(ax=ax, color=pntsClr) return plt.show() if method=='summary': return geomSummary(df); if method=='ponp': return getPolygonOnPoints(df, polys, ptsCoordCol, polygonsCoordCol, polygonsLabel, interactive); if method=='pinp': return getPointsInPolygons(df, polys, ptsCoordCol, polygonsCoordCol, interactive); if method=='pandp': return mapPointsandPolygons(df, polys, pntsClr, polysClr, polyColorCol); if method=='centroid': return getCentroid(df, col);# export # draw_heatmap, cluster_points, plot_points, def map_points(data, lat_col='POINT_Y', lon_col='POINT_X', zoom_start=11, plot_points=True, cluster_points=False, pt_radius=15, draw_heatmap=False, heat_map_weights_col=None, heat_map_weights_normalize=True, heat_map_radius=15, popup=False): """Creates a map given a dataframe of points. Can also produce a heatmap overlay Arg: df: dataframe containing points to maps lat_col: Column containing latitude (string) lon_col: Column containing longitude (string) zoom_start: Integer representing the initial zoom of the map plot_points: Add points to map (boolean) pt_radius: Size of each point draw_heatmap: Add heatmap to map (boolean) heat_map_weights_col: Column containing heatmap weights heat_map_weights_normalize: Normalize heatmap weights (boolean) heat_map_radius: Size of heatmap point Returns: folium map object """ df = data.copy() ## center map in the middle of points center in middle_lat = df[lat_col].median() middle_lon = df[lon_col].median() curr_map = folium.Map(location=[middle_lat, middle_lon], zoom_start=zoom_start) # add points to map if plot_points: for _, row in df.iterrows(): print([row[lat_col], row[lon_col]], row[popup][0]) folium.CircleMarker([row[lat_col], row[lon_col]], radius=pt_radius, popup=row[popup][0], fill_color="#3db7e4", # divvy color ).add_to(curr_map) if cluster_points: marker_cluster = MarkerCluster().add_to(curr_map) for index, row in df.iterrows(): folium.Marker( location=[row[lat_col],row[lon_col]], popup=row[popup][0], icon=None ).add_to(marker_cluster) # add heatmap if draw_heatmap: # convert to (n, 2) or (n, 3) matrix format if heat_map_weights_col is None: stations = zip(df[lat_col], df[lon_col]) else: # if we have to normalize if heat_map_weights_normalize: df[heat_map_weights_col] = \ df[heat_map_weights_col] / df[heat_map_weights_col].sum() stations = zip(df[lat_col], df[lon_col], df[heat_map_weights_col]) curr_map.add_child(plugins.HeatMap(stations, radius=heat_map_radius)) return curr_mapdef maps_points(df, lat_col='POINT_Y', lon_col='POINT_X', zoom_start=11, \ plot_points=False, pt_radius=15, \ draw_heatmap=True, heat_map_weights_col=None, \ heat_map_weights_normalize=True, heat_map_radius=15): """Creates a map given a dataframe of points. Can also produce a heatmap overlay Arg: df: dataframe containing points to maps lat_col: Column containing latitude (string) """ ## center map in the middle of points center in middle_lat = df[lat_col].median() middle_lon = df[lon_col].median() curr_map = folium.Map(location=[middle_lat, middle_lon], zoom_start=zoom_start) # add points to map if plot_points: for _, row in df.iterrows(): folium.CircleMarker([row[lat_col], row[lon_col]], radius=pt_radius, popup=row['name'], fill_color="#3db7e4", # divvy color ).add_to(curr_map) # add heatmap if draw_heatmap: # convert to (n, 2) or (n, 3) matrix format if heat_map_weights_col is None: stations = zip(df[lat_col], df[lon_col]) else: # if we have to normalize if heat_map_weights_normalize: df[heat_map_weights_col] = \ df[heat_map_weights_col] / df[heat_map_weights_col].sum() stations = zip(df[lat_col], df[lon_col], df[heat_map_weights_col]) curr_map.add_child(plugins.HeatMap(stations, radius=heat_map_radius)) return curr_map

Processing Geometry is tedius enough to merit its own handler

# export # reverseGeoCode, readFile, getGeoParams, main def readInGeometryData(url=False, porg=False, geom=False, lat=False, lng=False, revgeocode=False, save=False, in_crs=4326, out_crs=False): def reverseGeoCode(df, lat ): # STREET CITY STATE ZIP NAME # , format_string="%s, BALTIMORE MD" geometry = [] geolocator = Nominatim(user_agent="my-application") for index, row in df.iterrows(): try: geol = geolocator.geocode(row[lat], timeout=None) pnt = Point(geol.longitude, geol.latitude) geometry.append(pnt) except: geometry.append(Point(-76, 39) ) print(row[lat]); return geometry def readFile(url, geom, lat, lng, revgeocode, in_crs, out_crs): df = False gdf = False ext = isinstance(url, pd.DataFrame) if ext: ext='csv' else: ext = url[-3:] #XLS # b16 = pd.read_excel('Jones.BirthsbyCensus2016.XLS', sheetname='Births') # The file extension is used to determine the appropriate import method. if ext in ['son', 'kml', 'shp', 'pgeojson']: gdf = gpd.read_file(url) if ext == 'csv': df = url if isinstance(url, pd.DataFrame) else pd.read_csv(url) # Read using Geom, Lat, Lat/Lng, revGeoCode if revgeocode=='y': df['geometry'] = reverseGeoCode(df, lat) elif geom: df['geometry'] = df[geom].apply(lambda x: loads( str(x) )) elif lat==lng: df['geometry'] = df[lat].apply(lambda x: loads( str(x) )) elif lat!=lng: df['geometry'] = gpd.points_from_xy(df[lng], df[lat]); gdf = GeoDataFrame(df, crs=in_crs, geometry='geometry') #crs=2248 if not out_crs == in_crs: gdf = gdf.to_crs(epsg=out_crs) return gdf def getGeoParams(url, porg, geom, lat, lng, revgeocode, save, in_crs, out_crs): addr=False if not url: url = input("Please enter the location of your dataset: " ) # if url[-3:] == 'csv' : # df = pd.read_csv(url,index_col=0,nrows=1) # print(df.columns) # Geometries inside if geom and not (lat and lng): porg = 'g' # Point data inside elif not geom and lat or lng: porg = 'p'; if not lat: lat = lng if not lng: lng = lat # If the P/G could not be infered... if not (porg in ['p', 'g']): if not revgeocode in ['y', 'n']: revgeocode = input("Do your records need reverse geocoding: (Enter: y/n') " ) if revgeocode == 'y': porg = 'p'; lng = lat = input("Please enter the column name where the address is stored: " ); elif revgeocode == 'n': porg = input("""Do the records in this dataset use (P)oints or (g)eometric polygons?: (Enter: 'p' or 'g') """ ); else: return getGeoParams(url, porg, geom, lat, lng, revgeocode, save, in_crs, out_crs); if porg=='p': if not lat: lat = input("Please enter the column name where the latitude coordinate is stored: " ); if not lng: lng = input("Please enter the column name where the longitude cooridnate is stored: (Could be same as the lat) " ); elif porg=='g': if not geom: geom = input("Please enter column name where the geometry data is stored: (*optional, skip if unkown)" ); else: return getGeoParams(url, porg, geom, lat, lng, revgeocode, save, in_crs, out_crs) if not out_crs: out_crs=in_crs return url, porg, geom, lat, lng, revgeocode, save, in_crs, out_crs # This function uses all the other functions def main(url, porg, geom, lat, lng, revgeocode, save, in_crs, out_crs): # Check for missing values. retrieve them if (isinstance(url, pd.DataFrame)): print('Converting DF to GDF') elif (not (url and porg) ) or ( not (porg == 'p' or porg == 'g') ) or ( porg == 'g' and not geom) or ( porg == 'p' and (not (lat and lng) ) ): return readInGeometryData( *getGeoParams(url, porg, geom, lat, lng, revgeocode, save, in_crs, out_crs) ); # print(f"RECIEVED url: {url}, \r\n porg: {porg}, \r\n geom: {geom}, \r\n lat: {lat}, \r\n lng: {lng}, \r\n revgeocode: {revgeocode}, \r\n in_crs: {in_crs}, \r\n out_crs: {out_crs}") # Quit if the Columns dont exist -> CSV Only # status = checkColumns(url, geom, lat, lng) # if status == False: print('A specified column does not exist'); return False; # Perform operation gdf = readFile(url, geom, lat, lng, revgeocode, in_crs, out_crs) # Tidy up # Save # if save: saveGeoData(gdf, url, fileName, driver='esri') return gdf return main(url, porg, geom, lat, lng, revgeocode, save, in_crs, out_crs)

As you can see we have a lot of points. Lets see if there is any better way to visualize this.

Example: Using the advanced Functions

Playing with Points: Geoloom

Points In Polygons

The red dots from when we mapped the geoloom points above were a bit too noisy.

Lets create a choropleth instead!

We can do this by aggregating by CSA.

To do this, start of by finding which points are inside of which polygons!

Since the geoloom data does not have a CSA dataset, we will need merge it to one that does!

Lets use the childhood poverty link from example one and load it up because it contains the geometry data and the csa labels.

# from dataplay.intaker import Intake # csa_gdf = Intake.getData('https://raw.githubusercontent.com/bniajfi/bniajfi/main/CSA-to-Tract-2010.csv')# This dataset is taken from the public database provided by BNIAJFI hosted by Esri / ArcGIS # BNIA ArcGIS Homepage: https://data-bniajfi.opendata.arcgis.com/ csa_gdf_url = "https://services1.arcgis.com/mVFRs7NF4iFitgbY/ArcGIS/rest/services/Hhchpov/FeatureServer/0/query?where=1%3D1&outFields=*&returnGeometry=true&f=pgeojson" csa_gdf = readInGeometryData(url=csa_gdf_url, porg=False, geom='geometry', lat=False, lng=False, revgeocode=False, save=False, in_crs=2248, out_crs=False)

And now lets pull in our geoloom data. But to be sure, drop the empty geometry columns or the function directly below will now work.

geoloom_gdf_url = "https://services1.arcgis.com/mVFRs7NF4iFitgbY/ArcGIS/rest/services/Geoloom_Crowd/FeatureServer/0/query?where=1%3D1&outFields=*&returnGeometry=true&f=pgeojson" geoloom_gdf = readInGeometryData(url=geoloom_gdf_url, porg=False, geom='geometry', lat=False, lng=False, revgeocode=False, save=False, in_crs=4326, out_crs=False) geoloom_gdf = geoloom_gdf.dropna(subset=['geometry']) # geoloom_gdf = geoloom_gdf.drop(columns=['POINT_X','POINT_Y']) geoloom_gdf.head(1)
OBJECTID Data_type Attach ProjNm Descript Location URL Name PhEmail Comments POINT_X POINT_Y GlobalID geometry
0 1 Artists & Resources None Joe Test 123 Market Pl, Baltimore, MD, 21202, USA -8.527809e+06 4.762932e+06 e59b4931-e0c8-4d6b-b781-1e672bf8545a POINT (-76.60661 39.28746)

And now use a point in polygon method 'ponp' to get the CSA2010 column from our CSA dataset added as a column to each geoloom record.

geoloom_w_csas = workWithGeometryData(method='pinp', df=geoloom_gdf, polys=csa_gdf, ptsCoordCol='geometry', polygonsCoordCol='geometry', polyColorCol='hhchpov18', polygonsLabel='CSA2010', pntsClr='red', polysClr='white')

You'll see you have a 'pointsinpolygons' column now.

geoloom_w_csas.plot( column='pointsinpolygon', legend=True)
geoloom_w_csas.head(1)

Polygons in Points

Alternately, you can run the ponp function and have returned the geoloom dataset

geoloom_w_csas = workWithGeometryData(method='ponp', df=geoloom_gdf, polys=csa_gdf, ptsCoordCol='geometry', polygonsCoordCol='geometry', polyColorCol='hhchpov18', polygonsLabel='CSA2010', pntsClr='red', polysClr='white')

We can count the totals per CSA using value_counts

Alternately, we could map the centroid of boundaries within another boundary to find boundaries within boundaries

geoloom_w_csas['POINT_Y'] = geoloom_w_csas.centroid.y geoloom_w_csas['POINT_X'] = geoloom_w_csas.centroid.x # We already know the x and y columns because we just saved them as such. geoloom_w_csas['POINT_X'] = pd.to_numeric(geoloom_w_csas['POINT_X'], errors='coerce') geoloom_w_csas['POINT_Y'] = pd.to_numeric(geoloom_w_csas['POINT_Y'], errors='coerce') # df = df.replace(np.nan, 0, regex=True) # And filter out for points only in Baltimore City. geoloom_w_csas = geoloom_w_csas[ geoloom_w_csas['POINT_Y'] > 39.3 ] geoloom_w_csas = geoloom_w_csas[ geoloom_w_csas['POINT_Y'] < 39.5 ]map_points(geoloom_w_csas, lat_col='POINT_Y', lon_col='POINT_X', zoom_start=11, plot_points=True, cluster_points=False, pt_radius=7, draw_heatmap=True, heat_map_weights_col='POINT_X', heat_map_weights_normalize=True, heat_map_radius=15, popup='CSA2010')[39.3059284576752, -76.6084962613261] Midtown [39.354049947202, -76.594919959319] Northwood [39.3053725867077, -76.6165491304473] Midtown [39.3053725867077, -76.6165491304473] Midtown [39.3053725867077, -76.6165491304473] Midtown [39.3205305004358, -76.62029445046] Medfield/Hampden/Woodberry/Remington [39.3132344995704, -76.6156319686376] Greater Charles Village/Barclay [39.3025382004351, -76.6123550083559] Midtown [39.330960442152, -76.6097324686376] North Baltimore/Guilford/Homeland [39.3394246352966, -76.5728076182136] Lauraville [39.3663642396761, -76.5807452381971] Loch Raven [39.3112287786895, -76.6169870308888] Greater Charles Village/Barclay [39.3313095000031, -76.6273815000012] Medfield/Hampden/Woodberry/Remington [39.3111954460472, -76.6168148083572] Greater Charles Village/Barclay [39.3097800000032, -76.6165900000012] Midtown [39.3053725867077, -76.6165491304473] Midtown [39.3140701557246, -76.6357692877849] Penn North/Reservoir Hill [39.3140701557246, -76.6357692877849] Penn North/Reservoir Hill [39.3061130466302, -76.6162883877901] Midtown [39.3017150000032, -76.6202300000012] Midtown [39.3017150000032, -76.6202300000012] Midtown [39.3159400000032, -76.6096900000012] Greater Charles Village/Barclay [39.3243857282909, -76.6294667363678] Medfield/Hampden/Woodberry/Remington [39.3518547124792, -76.5618773076933] Harford/Echodale [39.3540395711272, -76.5949191991194] Northwood [39.3454084293701, -76.6310377077168] Greater Roland Park/Poplar Hill [39.3054254932224, -76.6429111990029] Sandtown-Winchester/Harlem Park [39.3053725867077, -76.6165491304473] Midtown [39.3053735782905, -76.6166029730092] Midtown [39.3098540513334, -76.6422915487344] Sandtown-Winchester/Harlem Park [39.331329442152, -76.6275211151839] Medfield/Hampden/Woodberry/Remington [39.331329442152, -76.6275211151839] Medfield/Hampden/Woodberry/Remington [39.3061685720253, -76.6163105668306] Midtown [39.3557085114907, -76.6325299272257] Greater Roland Park/Poplar Hill [39.3557085114907, -76.6325299272257] Greater Roland Park/Poplar Hill [39.3320899965888, -76.5492359719015] Cedonia/Frankford
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But if that doesn't do it for you, we can also create heat maps and marker clusters

# https://github.com/python-visualization/folium/blob/master/examples/MarkerCluster.ipynb# MarkerCluster.ipynb m = folium.Map(location=[39.28759453969165, -76.61278931706487], zoom_start=12) marker_cluster = MarkerCluster().add_to(m) stations = geoloom_w_csas.apply(lambda p: folium.Marker( location=[p['POINT_Y'],p['POINT_X']], popup='Add popup text here.', icon=None ).add_to(marker_cluster), axis=1 ) m
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map_points(geoloom_w_csas, lat_col='POINT_Y', lon_col='POINT_X', zoom_start=11, plot_points=True, cluster_points=False, pt_radius=15, draw_heatmap=False, heat_map_weights_col='POINT_X', heat_map_weights_normalize=True, heat_map_radius=15, popup='CSA2010')[39.3059284576752, -76.6084962613261] Midtown [39.354049947202, -76.594919959319] Northwood [39.3053725867077, -76.6165491304473] Midtown [39.3053725867077, -76.6165491304473] Midtown [39.3053725867077, -76.6165491304473] Midtown [39.3205305004358, -76.62029445046] Medfield/Hampden/Woodberry/Remington [39.3132344995704, -76.6156319686376] Greater Charles Village/Barclay [39.3025382004351, -76.6123550083559] Midtown [39.330960442152, -76.6097324686376] North Baltimore/Guilford/Homeland [39.3394246352966, -76.5728076182136] Lauraville [39.3663642396761, -76.5807452381971] Loch Raven [39.3112287786895, -76.6169870308888] Greater Charles Village/Barclay [39.3313095000031, -76.6273815000012] Medfield/Hampden/Woodberry/Remington [39.3111954460472, -76.6168148083572] Greater Charles Village/Barclay [39.3097800000032, -76.6165900000012] Midtown [39.3053725867077, -76.6165491304473] Midtown [39.3140701557246, -76.6357692877849] Penn North/Reservoir Hill [39.3140701557246, -76.6357692877849] Penn North/Reservoir Hill [39.3061130466302, -76.6162883877901] Midtown [39.3017150000032, -76.6202300000012] Midtown [39.3017150000032, -76.6202300000012] Midtown [39.3159400000032, -76.6096900000012] Greater Charles Village/Barclay [39.3243857282909, -76.6294667363678] Medfield/Hampden/Woodberry/Remington [39.3518547124792, -76.5618773076933] Harford/Echodale [39.3540395711272, -76.5949191991194] Northwood [39.3454084293701, -76.6310377077168] Greater Roland Park/Poplar Hill [39.3054254932224, -76.6429111990029] Sandtown-Winchester/Harlem Park [39.3053725867077, -76.6165491304473] Midtown [39.3053735782905, -76.6166029730092] Midtown [39.3098540513334, -76.6422915487344] Sandtown-Winchester/Harlem Park [39.331329442152, -76.6275211151839] Medfield/Hampden/Woodberry/Remington [39.331329442152, -76.6275211151839] Medfield/Hampden/Woodberry/Remington [39.3061685720253, -76.6163105668306] Midtown [39.3557085114907, -76.6325299272257] Greater Roland Park/Poplar Hill [39.3557085114907, -76.6325299272257] Greater Roland Park/Poplar Hill [39.3320899965888, -76.5492359719015] Cedonia/Frankford
Make this Notebook Trusted to load map: File -> Trust Notebook
map_points(geoloom_w_csas, lat_col='POINT_Y', lon_col='POINT_X', zoom_start=11, plot_points=True, cluster_points=False, pt_radius=1, draw_heatmap=True, heat_map_weights_col=None, heat_map_weights_normalize=True, heat_map_radius=15, popup='CSA2010')[39.3059284576752, -76.6084962613261] Midtown [39.354049947202, -76.594919959319] Northwood [39.3053725867077, -76.6165491304473] Midtown [39.3053725867077, -76.6165491304473] Midtown [39.3053725867077, -76.6165491304473] Midtown [39.3205305004358, -76.62029445046] Medfield/Hampden/Woodberry/Remington [39.3132344995704, -76.6156319686376] Greater Charles Village/Barclay [39.3025382004351, -76.6123550083559] Midtown [39.330960442152, -76.6097324686376] North Baltimore/Guilford/Homeland [39.3394246352966, -76.5728076182136] Lauraville [39.3663642396761, -76.5807452381971] Loch Raven [39.3112287786895, -76.6169870308888] Greater Charles Village/Barclay [39.3313095000031, -76.6273815000012] Medfield/Hampden/Woodberry/Remington [39.3111954460472, -76.6168148083572] Greater Charles Village/Barclay [39.3097800000032, -76.6165900000012] Midtown [39.3053725867077, -76.6165491304473] Midtown [39.3140701557246, -76.6357692877849] Penn North/Reservoir Hill [39.3140701557246, -76.6357692877849] Penn North/Reservoir Hill [39.3061130466302, -76.6162883877901] Midtown [39.3017150000032, -76.6202300000012] Midtown [39.3017150000032, -76.6202300000012] Midtown [39.3159400000032, -76.6096900000012] Greater Charles Village/Barclay [39.3243857282909, -76.6294667363678] Medfield/Hampden/Woodberry/Remington [39.3518547124792, -76.5618773076933] Harford/Echodale [39.3540395711272, -76.5949191991194] Northwood [39.3454084293701, -76.6310377077168] Greater Roland Park/Poplar Hill [39.3054254932224, -76.6429111990029] Sandtown-Winchester/Harlem Park [39.3053725867077, -76.6165491304473] Midtown [39.3053735782905, -76.6166029730092] Midtown [39.3098540513334, -76.6422915487344] Sandtown-Winchester/Harlem Park [39.331329442152, -76.6275211151839] Medfield/Hampden/Woodberry/Remington [39.331329442152, -76.6275211151839] Medfield/Hampden/Woodberry/Remington [39.3061685720253, -76.6163105668306] Midtown [39.3557085114907, -76.6325299272257] Greater Roland Park/Poplar Hill [39.3557085114907, -76.6325299272257] Greater Roland Park/Poplar Hill [39.3320899965888, -76.5492359719015] Cedonia/Frankford
Make this Notebook Trusted to load map: File -> Trust Notebook

And Time Sliders

Choropleth Timeslider

https://github.com/python-visualization/folium/blob/master/examples/TimeSliderChoropleth.ipynb

To simulate that data is sampled at different times we random sample data for n_periods rows of data. Note that the geodata and random sampled data is linked through the feature_id, which is the index of the GeoDataFrame.

periods = 10 datetime_index = pd.date_range('2010', periods=periods, freq='Y') dt_index_epochs = ( datetime_index.astype(int) ).astype('U10') datetime_index# Style each boundry with randomness. styledata = {} for country in geoloom.index: df = pd.DataFrame( {'color': np.random.normal(size=periods), 'opacity': [1,2,3,4,5,6,7,8,9,1] }, index=dt_index_epochs ) df = df.cumsum() styledata[country] = df ax = df.plot()df.head()

We see that we generated two series of data for each country; one for color and one for opacity. Let's plot them to see what they look like.

max_color, min_color, max_opacity, min_opacity = 0, 0, 0, 0 for country, data in styledata.items(): max_color = max(max_color, data['color'].max()) min_color = min(max_color, data['color'].min()) max_opacity = max(max_color, data['opacity'].max()) max_opacity = min(max_color, data['opacity'].max()) linear.PuRd_09.scale(min_color, max_color)

We want to map the column named color to a hex color. To do this we use a normal colormap. To create the colormap, we calculate the maximum and minimum values over all the timeseries. We also need the max/min of the opacity column, so that we can map that column into a range [0,1].

max_color, min_color, max_opacity, min_opacity = 0, 0, 0, 0 for country, data in styledata.items(): max_color = max(max_color, data['color'].max()) min_color = min(max_color, data['color'].min()) max_opacity = max(max_color, data['opacity'].max()) max_opacity = min(max_color, data['opacity'].max())from branca.colormap import linear cmap = linear.PuRd_09.scale(min_color, max_color) def norm(x): return (x - x.min()) / (x.max() - x.min()) for country, data in styledata.items(): data['color'] = data['color'].apply(cmap) data['opacity'] = norm(data['opacity'])

Finally we use pd.DataFrame.to_dict() to convert each dataframe into a dictionary, and place each of these in a map from country id to data.

geoloom.head(1)geoloom_gdf = gpd.read_file("https://services1.arcgis.com/mVFRs7NF4iFitgbY/ArcGIS/rest/services/Geoloom_Crowd/FeatureServer/0/query?where=1%3D1&outFields=*&returnGeometry=true&f=pgeojson"); m = folium.Map([39.28759453969165, -76.61278931706487], zoom_start=12) g = TimeSliderChoropleth( geoloom_gdf.to_json(), styledict={ str(country): data.to_dict(orient='index') for country, data in styledata.items() } ).add_to(m) m

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