Extract from the Sangis Roads_all file, for roads in San Diego city

sangis.org-roads-sandiego-1

Resources

• all_roads. Original all roads file

Documentation

Contacts

• Wrangler

Eric Busboom, Civic Knowledge

Packages

• zip https://library-metatab-org.s3.amazonaws.com/sangis.org-roads-sandiego-1.zip
• csv https://library-metatab-org.s3.amazonaws.com/sangis.org-roads-sandiego-1.csv
• source https://github.com/SDRDLAnalysts/data-projects.git

Accessing Packages in Metapack

import metapack as mp
# ZIP Package
pkg = mp.open_package('https://library-metatab-org.s3.amazonaws.com/sangis.org-roads-sandiego-1.zip')
# CSV Package
pkg = mp.open_package('https://library-metatab-org.s3.amazonaws.com/sangis.org-roads-sandiego-1.csv') 

resource = pkg.resource('resource_name') # Get a resource
df = resource.dataframe() # Create a pandas Dataframe
gdf = resource.geoframe() # Create a GeoPandas GeoDataFrame

Data Dictionary

all_roads

Point locations of businesses and tract aggregations of counts of businesses by type.

sangis.org-business_sites-2

Resources

• business_sites. Business sites, geographic file
• tract_counts_rows. Business counts per tract, by type of business, oriented by rows
• tract_counts_columns. Business counts per tract, by type of business, oriented by columns

Documentation

This dataset is based on the SANGIS BUSINESS_SITES data file, which is compiled from the Assessors Business Property Account Master file. This file is different from the City of San Diego’s business list, in that it includes records for the whole county, it categorizes businesses differently ( not using NAICS codes ) and for some categories, it does not appear to be very accurate.

The HPI provides overall scores and more detailed data on specific policy action areas that shape health, like housing, transportation, education and more.

healthyplacesindex.org-healthy_places_index-san_diego-4

Resources

• hpi. Healthy Places Index master file

Documentation

The Healthy Places Index is a collection of indicators produced by the Public Health Alliance of Southern California from a wide variety of data sources to study social determinants of health. It offers index values for many topics – about 80 columns – as well as a weighted aggreate index value, all at the tract level.

Combines geographic boundary files for communities, cities and tracts in San Diego County.

sandiegodata.org-geography-2018-13

Resources

• tracts. Tract census information
• places. Census place information, which includes incorporated cities and unincorporated towns like Julian and Romona
• places_boundaries. Census place boundaries, which includes incorporated cities and unincorporated towns like Julian and Romona
• city_boundaries. Boundary shapes for cities
• sd_community_boundaries. Boundary shapes for SD City communities
• county_boundaries. Boundary shapes for unincorporated County areas
• district_boundaries. Boundaries for special districts, such as the Promize Zone
• county_communities_boundaries. Boundaries for county communities
• combined_boundaries. Boundary shapes for all communities, county regions and cities combined
• tract_boundaries. Tract boundary shapes
• tract_communities. Link between tracts and cities, communities and county regions, with one record per tract. No geometry.
• sd_county_boundary. Boundary shape for SD County

Documentation

This package combines three SANGIS datasets for communities and cities in San Diego county into a single file, in the communities resource, with Census tract definitions for San Diego count. The source files are:

A collection of data for demographics and housing from the Census planning database. Files are broken into counties, for San Diego and Los Angeles

sandiegodata.org-planning-tracts-9

Resources

• planning_db_sd. San Diego county planning database tracts
• planning_db_la. Los Angeles county planning database tracts

Documentation

The Planing Database is a Census product that combines a range of data from the American Community Survey and 2010 Decenial Census into a single file, with one row per census tract. This version of the file includes only tracts in San Diego County. The file is linked to ACS format geoids to identify tracts, so it can be easily linked to other tracts data. This data package includes links to two such files, one for San Diego communities, and one for tract geographics.

Monthly homeless counts for the Gaslamp district in Downtown San Diego

downtownsandiego.org-homeless_count-gaslamp-2

Resources

• gaslamp.

Documentation

Since 2014, the Downtown San Diego Partnership has been conducting monthly counts of the homeless in the Downtown neighborhood. The positions of homeless sleepers are recorded on paper maps, which are compiled into a spreadsheet. The San Diego Regional Data Library is running a project to digitize these paper maps](https://downtown-homelesness.sandiegodata.org/) into geographic datasets.

This dataset covers only the Gaslamp district, from 2014-01-01 to 2018-06-28.

Counts of pedestrian traffic from the San Diego CityIQ system

cityiq.io-pedestrians-san_diego-1

Resources

• pedestrians. Pedestrians

Documentation

This dataset is pedestrian directions and counts for walkwaks in the San Diego CityIQ system. The package includes pedestrian counts for most walkway locations in the San Diego CityIQ system, but not all of them. The date range for the data is 1 August 2018 to the last full month before the package was built. For Version 1, then end date is 1 June 2019.

All assets types extracted form the San Diego City Iq system

cityiq.io-objects-san_diego-5. Modified 2020-04-12T16:44:03

Resources

• assets. All assets
• locations. All locations: walkways, traffic lanes and parking zones
• walkways. Walkways
• traffic_lanes. Traffic lanes
• parking_zones. Parking zones
• cameras. Cameras
• mics. Microphones
• nodes. Nodes
• em_sensors. Electro-magnetic sensors
• env_sensors. Environmental sensors

Documentation

These datafile are extracts of the assets and locations from the San Diego CityIQ system. Refer to the [CityIQ developer documentation](<https://ie-cities-docs.run.aws-usw02-pr.ice.predix.io/#c_overview _of_general_apis.html>) for details about these data records. The data are extracted using the cityiq Python package. See the ExtractAssets.ipynb notebook for the extract process.