Community and City and Tract Boundaries in San Diego County

by metapack

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

sandiegodata.org-geography-2018-13

Resources

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:

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San Diego Beachwatch Data, With Features

by metapack

Water quality data for San Diego county beaches from CEDEN, with added features for log transformation, quantiles and group codes.

sandiegodata.org-beachwatch-4

Resources

Documentation

This datasets rebuilds ceden.waterboards.ca.gov-beachwatch-sandiego with constant and null columns removed and many features added. It also breaks out station information into a seperate datasets, and enumerates the many difference combinations of methodname/analyte/unit, adding a code for each group to the dataset in measure_code. The measure code identifies sets of records that have compatible measurements.

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55+ ADOD and General Population Projections - SD County

by metapack

Current (2012) ADOD and General population data along with projections for 2020 and 2030 for San Diego county

sdcounty.ca.gov-adod-2012-san-diego-county-1

Resources

  • adod-prevalence. Table 1. Estimates of Prevalence of Alzheimer’s Disease and Other Dementias by Subregional Area, 55 Years and Over, San Diego County, 2012 - 2030
  • hospital-discharge. Table 2. Number of Emergency Department or Hospital Discharged Patients with Any Mention of Alzheimer’s Disease and Other Dementias by Subregional Area, 55 Years and Over, San Diego County, 2012 - 2030
  • elder-population-2012. Table 3. 2012 Population by Age Group and Subregional Area, 55 Years and Over, San Diego County
  • elder-population-2020. Table 4. 2020 Population Projections by Age Group and Subregional Area, 55 Years and Over, San Diego County
  • elder-population-2030. Table 5. 2030 Population Projections by Age Group and Subregional Area, 55 Years and Over, San Diego County

Documentation

Contacts

  • Origin

sdcounty.ca.gov, County of San Diego Health and Human Services Agency

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San Diego Parking Time Series

by metapack

15 minute interval parking utilization for 1600 parking zones in San Diego city.

sandiego.gov-cityiq_parking-2

Resources

Documentation

This datasets is compiled from parking events scraped from the San Diego CityIQ smart streetmap system, via the cityiq Python package. The dataset is compiled from PKIN and PKOUT events between the dates of Sept 2018 and Feb 2019 for the whole SaN Diego system.

The dataset is heavily processed to eliminate duplicate events because there are many spurious events, but an excess of PKIN events. When computing the number of cars parked in all parking zones, the excess of PKIN events results in about 60,000 extra cars per month. These issues are explored in an [Jupyter Notebook](<https://github.com/sandiegodata/cityiq/blob/master/notebooks/CityIQ%20 Leaking%20PKIN%20Events.ipynb>)

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Local Climate Data For San Diego 2008 to 2018

by metapack

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noaa.gov-localclimate-200808_201807-san-4

Resources

  • lcd-san. Local climate data for San Diego

Documentation

This dataset is an extract from the NOAA’s Local Climatological Data, via the online tool.

  • Station: WBAN:23188 ( San Diego )
  • Begin Date: 2008-08-01 00:00
  • End Date: 2018-07-05 23:59

Contacts

  • Wrangler

Eric Busboom, Civic Knowledge

Packages

Accessing Packages in Metapack

import metapack as mp
# ZIP Package
pkg = mp.open_package('https://library-metatab-org.s3.amazonaws.com/noaa.gov-localclimate-200808_201807-san-4.zip')
# CSV Package
pkg = mp.open_package('https://library-metatab-org.s3.amazonaws.com/noaa.gov-localclimate-200808_201807-san-4.csv') 

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

Last Modified 2018-08-09T14:59:58

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NLSY97 2013 HDF5 Conversion

by metapack

The NLSY97 as an HDF5 file

nlsinfo.org-nlsy97-2013-1

Documentation

This datapackage links to a HDF5 conversion of the NLSY97, with responses to 2013.

Because the HDF file is very large and complex to produce, this package links to the file on the web. The file was produces external to the package.

This is much more complicated to use than other files. The publicdata python package must be installed to get the Nlsy classes that can read the HDF5 file. Typical useage is:

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LAHSA Homeless Count

by metapack

Counts of homeless per tract, from the Los Angeles Point In Time Count, for 2018

lahsa.org-homeless_count-2018-los_angeles-2

Resources

Documentation

There are notes in the source Excel file, but they are embedded as an image, so these following notes are from OCR:

Data Prepared by Los Angeles Homeless Services Authority

Last updated 08/23/2018

Components of the Homeless Count

Street Count (all census tracts): Captures a point in time estimate of the
unsheltered population.

Shelter Count (from Homeless Management Integration System): Captures the
homeless population in emergency shelters, transitional housing, and safe
havens. The shelter count in this dataset excludes Cal WORKS hotel/ motel
vouchers and domestic violence shelters for confidentiality reasons.

Youth Count (sample census tracts): Collaborative process with youth
stakeholders to better understand and identify homeless youth.

Demographic Survey (sample census tracts): Captures the demographic
characteristics of the unsheltered homeless population.

Notes

LAHSA does not recommend aggregating census tract-level data to calculate
numbers for other geographic levels. Due to rounding, the census tract-level
data may not add up to the total for Los Angeles City Council District,
Supervisorial District, Service Planning Area, or the Los Angeles Continuum of
Care.

The Los Angeles Continuum of Care does not include the Cities of Long Beach,
Glendale, and Pasadena and will not equal the countywide Homeless Count Total.

Street Count Data include persons found outside, including persons found living
in cars, vans, ca mpers/RVs, tents, and makeshift shelters. The conversion
factors used to estimate the number of persons found Iivi ng outside are the
following: For fa milies- Makeshift Shelter = 2.42, Car = 2.96, Van = 2.43, Ca
mper/RV = 3.35, Tent = 2.75; For Individuals- Makeshift Shelter 1.67, Car =
1.54, Van = 1.62, Camper/RV = 1.76, Tent = 1.52.

Please visit https://www.lahsa.org/homeless-count/home to view and download
data.

Last updated 08/23/2018

Eagle Rock/Arroyo Seco Boundaries were updated

Contacts

  • Wrangler

Eric Busboom, Civic Knowledge

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ZIP-Tract Crosswalk

by metapack

Data files linking ZIP codes to 2010 Census tracts.

OrderedDict([('time', '2016'), ('origin', 'huduser.gov'), ('dataset', 'zip-tract'), ('version', '2'), ('@value', 'huduser.gov-zip_tract-2016-2')])

Resources

Documentation

Contacts

  • Origin

U.S. Department of Housing and Urban Development

  • Creator

Robert Renner, U.S. Department of Housing and Urban Development

  • Wrangler

Eric Busboom, Civic Knowledge

Packages

Accessing Packages in Metapack

import metapack as mp
# ZIP Package
pkg = mp.open_package('https://library-metatab-org.s3.amazonaws.com/huduser.gov-zip_tract-2016-2.zip')
# CSV Package
pkg = mp.open_package('https://library-metatab-org.s3.amazonaws.com/huduser.gov-zip_tract-2016-2.csv') 

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

Last Modified 2017-04-03T19:58:57

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Tracts in San Diego County

by metapack

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census.gov-tracts-2017-sandiego-1

Resources

Documentation

Contacts

  • Wrangler

Eric Busboom, Civic Knowledge

Packages

Accessing Packages in Metapack

import metapack as mp
# ZIP Package
pkg = mp.open_package('https://library-metatab-org.s3.amazonaws.com/census.gov-tracts-2017-sandiego-1.zip')
# CSV Package
pkg = mp.open_package('https://library-metatab-org.s3.amazonaws.com/census.gov-tracts-2017-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

Last Modified 2018-08-09T14:59:41

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US County Boundaries

by metapack

US county boundaries 500k resolution.

census.gov-counties-2017-2

Resources

Documentation

Contacts

  • Wrangler

Eric Busboom, Civic Knowledge

Packages

Accessing Packages in Metapack

import metapack as mp
# ZIP Package
pkg = mp.open_package('https://library-metatab-org.s3.amazonaws.com/census.gov-counties-2017-2.zip')
# CSV Package
pkg = mp.open_package('https://library-metatab-org.s3.amazonaws.com/census.gov-counties-2017-2.csv') 

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

Last Modified 2018-08-09T14:59:28

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