Downtown San Diego Homeless Sleepers

Five years of monthly geographic positions for homeless sleepers in Downtown San Diego

sandiegodata.org-dowtown_homeless-9

Resources | Packages | Documentation| Contacts| References| Data Dictionary

Resources

  • neighborhood_totals. Totals for each of the neighborhoods for 2018 and 2019, from the published Downtown San Diego Partnership statistics
  • monthly_totals. Monthly totals since 2012, from the published Downtown San Diego Partnership statistics
  • homeless_counts. Counts of homeless sleepers in Downtown San Diego, 2014 to mid 2018.
  • imputed_counts. The homeless_counts file with missing months probablistically imputed
  • downtown_blocks. Census TIGER geographic regions for the blocks that have one or more homeless sleepers in this dataset.

Documentation

This dataset provides geographic locations for homeless sleepers in Downtown San Diego, as counted by enumerators from the Downtown San Diego Partnership. These counts have been done monthly since 2012, and this dataset provides counts since 2014.

The count is done on paper maps with handwritten count marks. The San Diego Regional Data Library’s Downton Homelessness project converted these scanned count maps using a web based image annotation tool.

Base and Imputed Datasets

This distribution has two version of the homeless counts, the base set, homeless_counts, which has three months of missing data, and the imputed dataset, imputed_counts in which these three missing months, 2014-08, 2014-09, and 2015-06 are probabilistically re-created.

The Imputation process uses the total count for the month, from the monthly_totals dataset, as the number of sleepers that the imputed month will have. Then, the imputation process collects at least 1,000 records with dates near the missing month into a sample set, calculating the portion of records that are in each census block. Then the algorithm randomly selects that number of records for each census block, and adjusts random blocks so the total of the whole set of selected records is equal to the official count for the month.

The result is an imputed month that has the same number of records as the official count for the month and as the same portion of sleepers in each census block as in adjacent months.

Notes

Notes for the source dataset, as of April 2019:

The preliminary methodology (shown in yellow) [ March 2017 and earlier] counted two individuals per tent and two individuals per vehicle.

On April 27, 2017, the Clean & Safe program adopted the counting methodology used by the Regional Task Force on the Homeless. Per the methodology (represented above by the dark green cells), [April 2017 to April 2018, inclusive ] Clean & Safe staff counted 1.75 individuals per visible tent/structure and 1.66 individuals per vehicle that showed clear signs of habitation.

In May 2018, the Regional Task Force on the Homeless updated the methodology for vehicles to 2.03 that shows clear signs of habitation (the methodology of 1.75 individuals per tent/structure remained the same). The Clean & Safe program implemented this methodology on May 31, 2018 (represented in orange).

* Denotes the months that The Downtown Fellowship of Ministries and Churches collaborated with us on the monthly homeless sleep count.

**East Village breakout areas include the following: * North East East Village is the area north of Market Street & east of Park Ave. * North West is the area north of Market Street & west of Park Ave. * South East is the area east of Park Ave. south of Market Street * South West is the area west of Park Ave. south of Market Street

Caveats

Because this data is extracted, manually, from manually recorded maps, there are a lot of quality issues.

Missing Months

Because of conversion errors and some complications with the source maps, there are several months of data that are excluded from this dataset

  • August 2014. In the original datasets, August 2014 was a duplicate of September 2014.
  • September 2014. Comfusion related to the duplication of September and August resulted in September being incompletely processed.
  • June 2015. All of the map images for this month were blank. An alternate source PDF exists that is not blank, but this file was not used.
  • March 2018 on. The converted data includes months up to July 2018, but the data for April, May, June and July 2018 shows very low values for the East Village neghborhood, so these months are excluded. These months are re-created, probabilistically, in the imputed_counts dataset.

Not Using Ocupancy Multipliers

Since about 2017, HUD has instructed point-in-time homeless counts to multiply the counts of sleepers in structures and vehicles by factors to account for people who may be obsured and not directly countable. From April 2017 on, Downtown San Diego Partnership has been using these factors.

However, this dataset does not use the occupancy factors, to allow analysts to apply these factors consistently across all months of data. As a result, the counts from this dataset diverge from the official counts after March 2017.

Other Issues

  • The total_count often does not match the sum of counts on the map. These sums were made by hand, by the enumerator who made the counts, so there are occasional arithmetic errors.
  • There are many instances of missing values for rain or temp
  • Some dates include the day of the month, but many don’t These dates have a day of month of 1.
  • Date day of month is generally unreliable. Only the year and month are reliable, except for files noted above.
  • The neighborhood value is based on the map names, so in some months the East Village neighborhood is separated into east_village and east_village_south

Comparison to Official Published Counts

This plot shows, per month, the offical published counts from San Diego Downtown Partnership versus the total from this dataset. Note the descrepancies dues to issues noted above, including missing months, minor differences in some months, and the divergence after March 2017 due to occupancy multiplers.

Versions

  1. Initial Version
  2. Reload upstream source, with better datatypes
  3. Add monthly and neighborhood totals
  4. Updated to version 4 source package
  5. Updates to version 5 of source package, which excluded several months with quality problems, improved documentation
  6. Added column for source file, removed duplicated data form marina, 2016
  7. Added imputed counts dataset.
  8. Updated the monthly_totals dataset
  9. Added downtown_blocks geography and removed April 2018 on.

Contacts

Packages

Accessing Packages in Metapack

import metapack as mp
# ZIP Package
pkg = mp.open_package('http://library.metatab.org/sandiegodata.org-dowtown_homeless-9.zip')
# CSV Package
pkg = mp.open_package('http://library.metatab.org/sandiegodata.org-dowtown_homeless-9.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

homeless_counts | imputed_counts | neighborhood_totals | monthly_totals | downtown_blocks

homeless_counts

Column NameData TypeDescription
neighborhoodstringNeighborhood, from the label on the source map
datedateDate of the observation. Many dates have no know day of month; for these dates, the day of month is set to 1
typestringType of sleeper: individual, vehicle or structure
tempfloatTemperature, if it was written on the source map
rainstringRain or clear, if it was written on the source map
geoidstringCensus geoid for the 2010 Census block the observation is in, in ACS format
xnumberX geographic position, in California State Plane 6, EPSG:2230
ynumberY geographic position, in California State Plane 6, EPSG:2230
geometrystringGeographic position of observation, in Lat/Lon, EPSG:4326

imputed_counts

Column NameData TypeDescription
neighborhoodstringNeighborhood, from the label on the source map
datedatetimeDate of the observation. Many dates have no know day of month; for these dates, the day of month is set to 1
typestringType of sleeper: individual, vehicle or structure
tempintegerTemperature, if it was written on the source map
rainstringRain or clear, if it was written on the source map
geoidstringCensus geoid for the 2010 Census block the observation is in, in ACS format
xnumberX geographic position, in California State Plane 6, EPSG:2230
ynumberY geographic position, in California State Plane 6, EPSG:2230
geometrystringGeographic position of observation, in Lat/Lon, EPSG:4326

neighborhood_totals

Column NameData TypeDescription
datedateDate of count. Only the month and year are significant.
east_villageintegerNeighborhood count
city_centerintegerNeighborhood count
columbiaintegerNeighborhood count
marinaintegerNeighborhood count
cortezintegerNeighborhood count
gaslampintegerNeighborhood count

monthly_totals

Column NameData TypeDescription
datedateDate of total. Only the year and month are significant
countintegerTotal count

downtown_blocks

Column NameData TypeDescription
statefp10string
countyfp10string
tractce10string
blockce10string
geoid10string
name10string
mtfcc10string
ur10string
uace10string
funcstat10string
aland10integer
awater10integer
intptlat10number
intptlon10number
geometrystring

References

Urls used in the creation of this data package.

  • metapack+http://library.metatab.org/sandiegodata.org-downtown_homeless-source-6.csv#counts. Final homeless sleeper counts
  • metapack+http://library.metatab.org/sandiegodata.org-downtown_homeless-source-6.csv#files. Final file annotations, including total counts, temperature and weather.
  • shape+https://www2.census.gov/geo/pvs/tiger2010st/06_California/06073/tl_2010_06073_tabblock10.zip. 2010 Census blocks for San Diego County

Last Modified 2019-10-07T18:38:05