Seattle Open Data Visualization

This repository contains scripts to visualize Seattle's street network data along with neighborhood boundaries using GeoJSON and CSV files.

Dataset Sources

• Seattle Neighborhood Map Atlas Neighborhoods
  • Link to dataset
• Seattle Street Network Database (SND)
  • Link to dataset
• Seattle GeoJSON
  • Link to dataset
• Seattle Building Outlines 2023
  • Link to dataset
• Seattle City Limits
  • Link to dataset
• FCC National Broadband Map
  • Link to dataset
• 2020 Census Tracts - Seattle
  • Link to dataset

Requirements

• pandas
• geopandas
• matplotlib

You can install the required libraries using:

pip install pandas geopandas matplotlib

Usage

Script 1: Seattle Street Network and Neighborhood Boundaries

1. Download the required GeoJSON files:

   • Neighborhood_Map_Atlas_Neighborhoods.geojson
   • Street_Network_Database_SND_5675931800012836898.geojson
   • seattle.geojson

2. Update the file paths in the script to point to the downloaded GeoJSON files.

3. Run the script:

python seattle-snd.py

Script 2: Seattle Street Network, Neighborhood Boundaries, and Building Outlines

1. Download the required GeoJSON files:

   • Neighborhood_Map_Atlas_Neighborhoods.geojson
   • Street_Network_Database_SND_5675931800012836898.geojson
   • seattle.geojson
   • Building_Outlines_2023_5720112647185159859.geojson

2. Update the file paths in the script to point to the downloaded GeoJSON files.

3. Run the script:

python seattle-snd-bo.py

Script 3: Seattle FCC Broadband Data

1. Download the required GeoJSON and CSV files:

   • Neighborhood_Map_Atlas_Neighborhoods.geojson
   • Street_Network_Database_SND_5675931800012836898.geojson
   • seattle.geojson
   • Seattle_City_Limits_-2760504394019241448.geojson
   • bdc_53_FibertothePremises_fixed_broadband_J24_04mar2025.csv
   • bdc_53_Cable_fixed_broadband_J24_04mar2025.csv
   • 2020_Census_Tracts_Seattle_600198261190227915.geojson
   • 2020_Census_Block_Groups_-_Seattle.geojson

2. Update the file paths in the script to point to the downloaded GeoJSON and CSV files.

3. Run the script:

python seattle-fcc-broadband.py

Output

• seattle-snd.py will generate a seattle-snd.pdf file with the visualization of Seattle's street network and neighborhood boundaries.
• seattle-snd-bo.py will generate a seattle-snd-bo.pdf file with the visualization of Seattle's street network, neighborhood boundaries, and building outlines.
• seattle-fcc-broadband.py will generate a seattle-fcc-broadband.pdf file with the visualization of Seattle's broadband data. (Census Tracts-Level)
• seattle-fcc-broadband-bg.py will generate a seattle-fcc-broadband-bg.pdf file with the visualization of Seattle's broadband data. (Census Block Groups-Level)

Output of seattle-fcc-broadband.py

data.columns: Index(['frn', 'provider_id', 'brand_name', 'location_id', 'technology',
       'max_advertised_download_speed', 'max_advertised_upload_speed',
       'low_latency', 'business_residential_code', 'state_usps', 'block_geoid',
       'h3_res8_id'],
      dtype='object')
census_tracts.columns: Index(['OBJECTID', 'GEOID20', 'GROSS_ACRES', 'LAND_ACRES', 'WATER_ACRES',
       'NAME', 'TRACT_NUMB', 'BASENAME', 'UVDA_AREA', 'CRA_NO', 'CRA_GRP',
       'GEN_ALIAS', 'DETL_NAMES', 'C_DISTRICT', 'geometry'],
      dtype='object')
         GEOID20  max_download_speed_in_tract
0    53033000402                         2000
1    53033000403                         2000
2    53033000700                         8000
3    53033003302                         8000
4    53033003601                         2000
..           ...                          ...
172  53033008101                        10000
173  53033010401                         2000
174  53033011401                         2000
175  53033011700                         2000
176  53033011901                         2000

[177 rows x 2 columns]
[  2000   8000   1200  10000 100000   6000 400000]

Output of seattle-fcc-broadband-bg.py

data.columns: Index(['frn', 'provider_id', 'brand_name', 'location_id', 'technology',
       'max_advertised_download_speed', 'max_advertised_upload_speed',
       'low_latency', 'business_residential_code', 'state_usps', 'block_geoid',
       'h3_res8_id'],
      dtype='object')
census_block_groups.columns: Index(['OBJECTID', 'GEOID20', 'GROSS_ACRES', 'ACRES_LAND', 'ACRES_WATER',
       'TRBG', 'NAMELSAD', 'BG', 'TRACT', 'TRACT_LABEL', 'Shape__Area',
       'Shape__Length', 'geometry'],
      dtype='object')
          GEOID20  max_download_speed_in_tract
0    530330002021                       2000.0
1    530330002022                       2000.0
2    530330004022                       2000.0
3    530330013003                       2000.0
4    530330017013                       2000.0
..            ...                          ...
531  530330074042                       2000.0
532  530330080033                       2000.0
533  530330091001                       8000.0
534  530330092002                       8000.0
535  530330104013                       2000.0

[536 rows x 2 columns]
[  2000.   8000.     nan  10000.   6000.   5000. 100000.   1200.   1500.
 400000.]