Project-level housing inventory for 2012 to 2018.
hudexchange.info-hic-project-1
DUring the annual Point in Time Count, HUD also collects information about sheltered homeless, with detailed information about housing projects, including number of beds, occupancy, funding, and many other variables.
There is no formal data dictionary, so the dataset is lighly documented; the meaning of the fields is probably part of the HDX web application.
Combined years of sheltered and unsheltered homeless counts, by CoC.
hudexchange.info-pit-coc-1
HUD publishes an Excel workbook with sheltered and unsheltered counts, aggregated to CoC, for the whole country, for 2007 on. In this dataset, each year is in a seperate worksheet, and later years have more columns than earlier years, and most column names include the year as a suffix.
This data package combines al of these years into a single file, with normalized column names. Cells in years that don’t have a column that are in later years are filled with nulls, and each row is prefixed with the year of the file that the original row was sourced from.
Data related to homelessness from an ArcGIS tutorial
arcgis.com-risk-3
ESRI [publishes a tutorial for creating maps for analyzing homelesness](<http://desktop.arcgis.com/en/analytics/case-studies/la-county-homel essness-2-pro-workflow.htm>), with a link to the data used. Many of the data are from lahsa.org and economicrt.org, but the package also includes many other columns. The data download includes a .gdb file, which includes no documentation, although there may be documentation in the HomelessnessPkg.ppkx file.
Standardized Records from the Demographic Survey of Unsheltered Individuals
economicrt.org-demographic_survey-1
The Los Angeles Homeless Services Authority (LAHSA) conducts annual in-person surveys of the unsheltered homeless population in Los Angeles. This data package includes the standardized versions of the annual survey data for 2011 through 2017, combined into a single file (combined_survey) and another multi year file that was produced by UCLA, which includes only the common columns from the multi-year files. (multiyear_survey)
Numeric county codes used by California government.
sandiegodata.org-county_codes-ca-3
These county codes are used in California Department of Education CDS identification numbers, and a variety of other places. They are different from the FIPS codes for counties used by the US Census.
These codes were scraped from a website.
California public schools, private schools and districts.
cde.ca.gov-schools-2.1.1. Modified 2022-06-17T15:44:42
The source data, from the California Department of Education, does not explicitly reference a license, but since the top-level website for California indicates that data published on the State of California website is generally in the public domain, we assume it is in the public domain. Derived data included in this package is also in the public domain. Metadata in this package ( contained in the metadata.csv file ) is derived from metadata for the source package, and is also in the public domain.
The Child and Adult Care Food Program (CACFP) provides federal funding to participating child care and adult day care centers, allowing the centers to serve nutritious meals to enrolled participants
cde.ca.gov-cacfp_sites-1.2.1. Modified 2022-06-15T17:58:35
California Department of Education CACFP sites file, with extra features, include:
program_types field is broken out into dummy variablesThere are 5 different methods for matching sites to schools in the schools file, each with its own code:
Extract from the Sangis Roads_all file, for roads in San Diego city
sangis.org-roads-sandiego-1
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
all_roads
Point locations of businesses and tract aggregations of counts of businesses by type.
sangis.org-business_sites-2
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
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.