About this 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 expansive 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 command:
undefinedCoordinate 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:
undefinedCoordinate 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.
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 â .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 â .geojson, .json Description â Second most popular, Geojson is typically used in web-based mapping used by storing the coordinates as JSON.
Geography Markup Language â .gml Description â Similar to Geojson, GML has more data for the same amount of information.
Google Keyhole Markup Language â .kml, .kmz Description â 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 â .tif, .tiff, .ovr ERDAS Imagine â .img IDRISI Raster â .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
SETUP:
Import Modules
# @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 psycopg2
import matplotlib.pyplot as plt import IPython from IPython.core.display import HTML import os from branca.colormap import linear
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.intaker import Intake from dataplay.acs import retrieveAcsData from dataplay.merge import mergeDatasets
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:
(.geojson or .shp) file directly using Geo-pandas
(.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
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' undefined 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 = 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)geopandas.geodataframe.GeoDataFrame
GeoDataFrames are only possible when one of the columns are of a 'Geometry' Datatype
csa_gdf.dtypesOBJECTID int64 ,CSA2010 object ,hhchpov14 float64 ,hhchpov15 float64 ,hhchpov16 float64 ,hhchpov17 float64 ,hhchpov18 float64 ,hhchpov19 float64 ,CSA2020 object ,hhchpov20 float64 ,hhchpov21 float64 ,Shape__Area float64 ,Shape__Length float64 ,geometry geometry ,dtype: object
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)
OBJECTID | CSA2010 | hhchpov14 | hhchpov15 | hhchpov16 | hhchpov17 | hhchpov18 | hhchpov19 | CSA2020 | hhchpov20 | hhchpov21 | Shape__Area | Shape__Length | geometry | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Allendale/Irvington/S. Hilton | 41.55 | 38.93 | 34.73 | 32.77 | 35.27 | 32.6 | Allendale/Irvington/S. Hilton | 21.42 | 21.42 | 6.38e+07 | 38770.17 | POLYGON ((-76.65726 39.27600, -76.65726 39.276... |
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 undefined 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)
Unnamed: 0 | OBJECTID | CSA2010 | hhchpov14 | hhchpov15 | hhchpov16 | hhchpov17 | hhchpov18 | hhchpov19 | CSA2020 | hhchpov20 | hhchpov21 | Shape__Area | Shape__Length | geometry | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0 | 1 | Allendale/Irvington/S. Hilton | 41.55 | 38.93 | 34.73 | 32.77 | 35.27 | 32.6 | Allendale/Irvington/S. Hilton | 21.42 | 21.42 | 6.38e+07 | 38770.17 | POLYGON ((-76.657 39.276, -76.657 39.276, -76.... |
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)geopandas.geodataframe.GeoDataFrame
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.dtypesUnnamed: 0 int64 ,OBJECTID int64 ,CSA2010 object ,hhchpov14 float64 ,hhchpov15 float64 ,hhchpov16 float64 ,hhchpov17 float64 ,hhchpov18 float64 ,hhchpov19 float64 ,CSA2020 object ,hhchpov20 float64 ,hhchpov21 float64 ,Shape__Area float64 ,Shape__Length float64 ,geometry geometry ,dtype: object
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 undefined 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.dtypesUnnamed: 0 int64 ,OBJECTID int64 ,CSA2010 object ,hhchpov14 float64 ,hhchpov15 float64 ,hhchpov16 float64 ,hhchpov17 float64 ,hhchpov18 float64 ,hhchpov19 float64 ,CSA2020 object ,hhchpov20 float64 ,hhchpov21 float64 ,Shape__Area float64 ,Shape__Length float64 ,geometry geometry ,dtype: object
type(csa_gdf)geopandas.geodataframe.GeoDataFrame
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)geopandas.geodataframe.GeoDataFrame
Approach 2: Method 2: Convert Column(s) to Coordinate
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) """"\nexe_df['x'] = pd.to_numeric(exe_df['x'], errors='coerce')\nexe_df['y'] = pd.to_numeric(exe_df['y'], errors='coerce')\n# exe_df = exe_df.replace(np.nan, 0, regex=True)\n \n# An example of loading in an internal BNIA file\ngeometry=[Point(xy) for xy in zip(exe_df.x, exe_df.y)]\nexe_gdf = gpd.GeoDataFrame( exe_df.drop(['x', 'y'], axis=1), crs=crs, geometry=geometry)\n"
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)
Unnamed: 0 | OBJECTID | Data_type | Attach | ProjNm | Descript | Location | URL | Name | PhEmail | Comments | GlobalID | geometry | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3 | 4 | 5 | Artists & Resources | NaN | Open Works | Maker Space | 1400 Greenmount Ave, Baltimore, MD, 21202, USA | http://www.openworksbmore.com | Alyce Myatt | alycemyattconsulting@gmail.com | One of Jane Brown's projects! | 140e7db7-33f1-49cd-8133-b6f75dba5851 | POINT (-76.608 39.306) |
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 = retrieve_acs_data IPython.core.display.HTML("") # state, county, tract, tableId, year, saveOriginal, save df = retrieve_acs_data(state, county, tract, tableId, year) df.head(1) df.to_csv('tracts_data.csv')
B19049_001E_Median_household_income_in_the_past_12_months_(in_2017_inflation-adjusted_dollars)_--_Total | B19049_002E_Median_household_income_in_the_past_12_months_(in_2017_inflation-adjusted_dollars)_--_Householder_under_25_years | B19049_003E_Median_household_income_in_the_past_12_months_(in_2017_inflation-adjusted_dollars)_--_Householder_25_to_44_years | B19049_004E_Median_household_income_in_the_past_12_months_(in_2017_inflation-adjusted_dollars)_--_Householder_45_to_64_years | B19049_005E_Median_household_income_in_the_past_12_months_(in_2017_inflation-adjusted_dollars)_--_Householder_65_years_and_over | state | county | tract | |
---|---|---|---|---|---|---|---|---|
NAME | ||||||||
Census Tract 2710.02 | 38358 | -666666666 | 34219 | 40972 | 37143 | 24 | 510 | 271002 |
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
# Obtained from: https://raw.githubusercontent.com/bniajfi/bniajfi/main/CSA-to-Tract-2010.csv crosswalk = pd.read_csv('CSA-to-Tract-2010.csv') crosswalk.tail(1)
TRACTCE10 | GEOID10 | CSA2010 | |
---|---|---|---|
199 | 280500 | 24510280500 | Oldtown/Middle East |
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)
CSA2010 | hhchpov15 | hhchpov16 | hhchpov17 | hhchpov18 | geometry | |
---|---|---|---|---|---|---|
0 | Allendale/Irvington/S. Hilton | 38.93 | 34.73 | 32.77 | 35.27 | POLYGON ((-76.65726 39.27600, -76.65726 39.276... |
# 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 )---Handling Left Dataset Options--- Left column: tract ---Handling Right Dataset Options--- Right column: tract ---Ensuring Compatability Between merge_how (val: 'outer') and the Right Dataset--- Column or ['inner','left','right','outer'] value: {merge_how} ---Checking Crosswalk Dataset Options--- crosswalk_left_col TRACTCE10 ---Casting Datatypes from-to: Left->Crosswalk Datasets--- Before Casting: -> Column One: tract int64 -> Column Two: TRACTCE10 int64 After Casting: -> Column One: tract int64 -> Column Two: TRACTCE10 int64 ---Casting Datatypes from-to: Right->Crosswalk Datasets--- Before Casting: -> Column One: CSA2010 object -> Column Two: CSA2010 object After Casting: -> Column One: CSA2010 object -> Column Two: CSA2010 object ---All checks complete. Status: True --- ---PERFORMING MERGE : LEFT->CROSSWALK --- Column One : tract int64 How: CSA2010 Column Two : TRACTCE10 int64 Local Column Values Not Matched [10000] 2 Crosswalk Unique Column Values [ 10100 10200 10300 10400 10500 20100 20200 20300 30100 30200 40100 40200 60100 60200 60300 60400 70100 70200 70300 70400 80101 80102 80200 80301 80302 80400 80500 80600 80700 80800 90100 90200 90300 90400 90500 90600 90700 90800 90900 100100 100200 100300 110100 110200 120100 120201 120202 120300 120400 120500 120600 120700 130100 130200 130300 130400 130600 130700 130803 130804 130805 130806 140100 140200 140300 150100 150200 150300 150400 150500 150600 150701 150702 150800 150900 151000 151100 151200 151300 160100 160200 160300 160400 160500 160600 160700 160801 160802 170100 170200 170300 180100 180200 180300 190100 190200 190300 200100 200200 200300 200400 200500 200600 200701 200702 200800 210100 210200 220100 230100 230200 230300 240100 240200 240300 240400 250101 250102 250103 250203 250204 250205 250206 250207 250301 250303 250401 250402 250500 250600 260101 260102 260201 260202 260203 260301 260302 260303 260401 260402 260403 260404 260501 260604 260605 260700 260800 260900 261000 261100 270101 270102 270200 270301 270302 270401 270402 270501 270502 270600 270701 270702 270703 270801 270802 270803 270804 270805 270901 270902 270903 271001 271002 271101 271102 271200 271300 271400 271501 271503 271600 271700 271801 271802 271900 272003 272004 272005 272006 272007 280101 280102 280200 280301 280302 280401 280402 280403 280404 280500] ---PERFORMING MERGE : LEFT->RIGHT --- Column One : CSA2010 object How: outer Column Two : CSA2010 object
mergedf.dtypesNAME object ,B19049_001E_Median_household_income_in_the_past_12_months_(in_2017_inflation-adjusted_dollars)_--_Total int64 ,B19049_002E_Median_household_income_in_the_past_12_months_(in_2017_inflation-adjusted_dollars)_--_Householder_under_25_years int64 ,B19049_003E_Median_household_income_in_the_past_12_months_(in_2017_inflation-adjusted_dollars)_--_Householder_25_to_44_years int64 ,B19049_004E_Median_household_income_in_the_past_12_months_(in_2017_inflation-adjusted_dollars)_--_Householder_45_to_64_years int64 ,B19049_005E_Median_household_income_in_the_past_12_months_(in_2017_inflation-adjusted_dollars)_--_Householder_65_years_and_over int64 ,state int64 ,county int64 ,tract int64 ,CSA2010 object ,hhchpov15 float64 ,hhchpov16 float64 ,hhchpov17 float64 ,hhchpov18 float64 ,geometry geometry ,dtype: object
# 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)
OBJECTID | Data_type | Attach | ProjNm | Descript | Location | URL | Name | PhEmail | Comments | |
---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Artists & Resources | NaN | Joe | Test | 123 Market Pl, Baltimore, MD, 21202, USA |
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()
Location | Address | |
---|---|---|
0 | 100 N. Holliday St, Baltimore, MD 21202 | Baltimore City Council |
1 | 200 E Pratt St, Baltimore, MD | The Gallery at Harborplace |
2 | 2401 Liberty Heights Ave, Baltimore, MD | Mondawmin Mall |
3 | 201 E Pratt St, Baltimore, MD | Harborplace |
4 | 3501 Boston St, Baltimore, MD | The Shops at Canton Crossing |
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'] = geometry
address_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() '''
Basic Operations
Inspection
def geomSummary(gdf): return type(gdf), gdf.crs, gdf.columns; # for p in df['Tract'].sort_values(): print(p) geomSummary(csa_gdf)(geopandas.geodataframe.GeoDataFrame, ,
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!
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# # 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'] = 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);
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# 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): # print("readInGeometryData-READFILE STARTING") 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 unknown)" ); 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): # print("READINGEOMETRYDATA-MAIN STARTING") # 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) ); url, porg, geom, lat, lng, revgeocode, save, in_crs, out_crs = getGeoParams(url, porg, geom, lat, lng, revgeocode, save, in_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)
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def 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
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# 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(): if pd.isna(row[lat_col]) or pd.isna(row[lon_col]): continue if( row[lat_col] != row[lat_col] ): print("Invalid coordinates, skipping marker:", row) continue folium.CircleMarker([row[lat_col], row[lon_col]], radius=pt_radius, popup=row[popup], 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(): if pd.isna(row[lat_col]) or pd.isna(row[lon_col]): continue popup_html = '' + '' folium.Marker( location=[row[lat_col], row[lon_col]], popup=folium.Popup(popup_html, max_width=450), 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_map
'.join([f'{col}: {row[col]}' for col in popup]) + '
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['POINT_X'] = geoloom_gdf['geometry'].x geoloom_gdf['POINT_Y'] = geoloom_gdf['geometry'].y geoloom_gdf.head(1)
OBJECTID | Data_type | Attach | ProjNm | Descript | Location | URL | Name | PhEmail | Comments | GlobalID | geometry | POINT_X | POINT_Y | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Artists & Resources | NaN | Joe | Test | 123 Market Pl, Baltimore, MD, 21202, USA | e59b4931-e0c8-4d6b-b781-1e672bf8545a | POINT (-76.60661 39.28746) | -76.61 | 39.29 |
map_points(geoloom_gdf, lat_col='POINT_Y', lon_col='POINT_X', zoom_start=11, plot_points=True, cluster_points=True, pt_radius=15, draw_heatmap=False, heat_map_weights_col=None, heat_map_weights_normalize=True, heat_map_radius=15, popup=['ProjNm', 'Data_type', 'Location', 'URL', 'Name', 'PhEmail', 'Comments'])
import pandas as pd url = "C:/Users/charl/Documents/GitHub/karpatic/src/ipynb/labs/scraped_data.csv" readInGeometryData(url, geom='geometry')
Processing Geometry is tedius enough to merit its own handler
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) csa_gdf.head(1)
OBJECTID | CSA2010 | hhchpov14 | hhchpov15 | hhchpov16 | hhchpov17 | hhchpov18 | hhchpov19 | CSA2020 | hhchpov20 | hhchpov21 | Shape__Area | Shape__Length | geometry | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Allendale/Irvington/S. Hilton | 41.55 | 38.93 | 34.73 | 32.77 | 35.27 | 32.6 | Allendale/Irvington/S. Hilton | 21.42 | 21.42 | 6.38e+07 | 38770.17 | POLYGON ((-76.65726 39.27600, -76.65726 39.276... |
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 | NaN | Joe | Test | 123 Market Pl, Baltimore, MD, 21202, USA | -8.53e+06 | 4.76e+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', # method=False, df=geoloom_gdf, # df=False, polys=csa_gdf, # polys=False, ptsCoordCol='geometry', # ptsCoordCol=False, polygonsCoordCol='geometry', # polygonsCoordCol=False, polyColorCol='hhchpov18', # polyColorCol=False polygonsLabel='CSA2010', # polygonsLabel='polyOnPoint', pntsClr='red', # pntsClr='red', polysClr='white' # polysClr='white', ) # interactive=False
geoloom_w_csas.head(2)
OBJECTID | CSA2010 | hhchpov14 | hhchpov15 | hhchpov16 | hhchpov17 | hhchpov18 | hhchpov19 | CSA2020 | hhchpov20 | hhchpov21 | Shape__Area | Shape__Length | geometry | pointsinpolygon | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Allendale/Irvington/S. Hilton | 41.55 | 38.93 | 34.73 | 32.77 | 35.27 | 32.60 | Allendale/Irvington/S. Hilton | 21.42 | 21.42 | 6.38e+07 | 38770.17 | POLYGON ((-76.65726 39.27600, -76.65726 39.276... | 0 |
1 | 2 | Beechfield/Ten Hills/West Hills | 22.31 | 19.42 | 21.22 | 23.92 | 21.90 | 15.38 | Beechfield/Ten Hills/West Hills | 14.77 | 14.77 | 4.79e+07 | 37524.95 | POLYGON ((-76.69479 39.30201, -76.69465 39.301... | 0 |
You'll see you have a 'pointsinpolygons' column now.
geoloom_w_csas.plot( column='pointsinpolygon', legend=True)
geoloom_w_csas.head(1)
OBJECTID | CSA2010 | hhchpov14 | hhchpov15 | hhchpov16 | hhchpov17 | hhchpov18 | hhchpov19 | CSA2020 | hhchpov20 | hhchpov21 | Shape__Area | Shape__Length | geometry | pointsinpolygon | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | Allendale/Irvington/S. Hilton | 41.55 | 38.93 | 34.73 | 32.77 | 35.27 | 32.6 | Allendale/Irvington/S. Hilton | 21.42 | 21.42 | 6.38e+07 | 38770.17 | POLYGON ((-76.65726 39.27600, -76.65726 39.276... | 0 |
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 undefined
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 ]
Folium
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
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='ProjNm')
And Time Sliders
Choropleth Timeslider
https://github.com/python-visualization/folium/blob/master/examples/TimeSliderChoropleth.ipynb
import geopandas as gpd import numpy as np import pandas as pd from branca.colormap import linear # conditionally loaded -> from dataplay import geoms u = intaker.Intake rdf = u.getData('https://services1.arcgis.com/mVFRs7NF4iFitgbY/ArcGIS/rest/services/Biz1_/FeatureServer/0/query?where=1%3D1&outFields=*&returnGeometry=true&f=pgeojson') # rdf.set_index('CSA2010', drop=True, inplace=True) rdf.drop(labels=['OBJECTID_1', 'Shape__Area', 'Shape__Length'], axis=1, inplace=True) ndf = rdf.filter(regex='biz1|CSA2010', axis=1) # Calculate number of years available n_periods = len(ndf.columns) - 1 # Get starting year. startAt = "20"+ndf.columns[1][-2:] # Create a 'YEAR' index with the assumption that all following years exist datetime_index = pd.date_range(startAt, periods=n_periods, freq="Y") dt_index_epochs = datetime_index.astype(int) // 10 ** 9 dt_index = dt_index_epochs.astype("U10")
rdf.head()
styledata = {} # For the Index of each CSA for idx, csa in rdf.iterrows(): df = pd.DataFrame( { "color": csa.values[1:-1] }, index=dt_index, ) styledata[idx] = df max_color, min_color = 0, 0 for country, data in styledata.items(): max_color = max(max_color, data["color"].max()) min_color = min(max_color, data["color"].min()) 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"] = 1 styledict = { str(country): data.to_dict(orient="index") for country, data in styledata.items() } # { CSA : { timestamp: {color: value, opacity:value } }, # CSA : { timestamp: {color: value, opacity:value } }, # ... # }
styledict
import folium from folium.plugins import TimeSliderChoropleth m = folium.Map([39.28759453969165, -76.61278931706487], width='50%', height='50%', zoom_start=12) g = TimeSliderChoropleth( rdf.to_json(), styledict=styledict, ).add_to(m) m
m.save(outfile= "test.html")