Downloadable files for the Unduplicated Pupil Count (UPC) of free or reduced price meal (FRPM) eligibility, English learner (EL), and foster youth data from the California Longitudinal Pupil Achievement Data System (CALPADS).

cde.ca.gov-calpads_upc-1.1.1. Modified 2021-03-11T00:51:50

Resources

• upc_13_lea. Unduplicated pupil count, academic year 2013, LEA Level
• upc_13_school. Unduplicated pupil count, academic year 2013, School Level
• upc_14_lea. Unduplicated pupil count, academic year 2014, LEA Level
• upc_14_school. Unduplicated pupil count, academic year 2014, School Level
• upc_15_lea. Unduplicated pupil count, academic year 2015, LEA Level
• upc_15_school. Unduplicated pupil count, academic year 2015, School Level
• upc_16_lea. Unduplicated pupil count, academic year 2016, LEA Level
• upc_16_school. Unduplicated pupil count, academic year 2016, School Level
• upc_17_lea. Unduplicated pupil count, academic year 2017, LEA Level
• upc_17_school. Unduplicated pupil count, academic year 2017, School Level
• upc_combined_lea. Combined LEA files, 2014 on
• upc_combined_school. Combined LEA files, 2014 on

Documentation

Unduplicated Pupil Counts are the counts of pupils that are in one or more categories of eligibility for free or reduced price meals (FRMP), an important part of the Local Control Funding Formula.

Calculation of current expense (cost) of education per average daily attendance (ADA) pursuant to Education Code Section 41372.

cde.ca.gov-current_expense-1.2.2. Modified 2022-08-09T16:57:45

Resources

• current_expense_98. Current Expense file for academic year 1998
• current_expense_99. Current Expense file for academic year 1999
• current_expense_00. Current Expense file for academic year 2000
• current_expense_01. Current Expense file for academic year 2001
• current_expense_02. Current Expense file for academic year 2002
• current_expense_03. Current Expense file for academic year 2003
• current_expense_04. Current Expense file for academic year 2004
• current_expense_05. Current Expense file for academic year 2005
• current_expense_06. Current Expense file for academic year 2006
• current_expense_07. Current Expense file for academic year 2007
• current_expense_08. Current Expense file for academic year 2008
• current_expense_09. Current Expense file for academic year 2009
• current_expense_10. Current Expense file for academic year 2010
• current_expense_11. Current Expense file for academic year 2011
• current_expense_12. Current Expense file for academic year 2012
• current_expense_13. Current Expense file for academic year 2013
• current_expense_14. Current Expense file for academic year 2014
• current_expense_15. Current Expense file for academic year 2015
• current_expense_16. Current Expense file for academic year 2016
• current_expense_17. Current Expense file for academic year 2017
• current_expense_18. Current Expense file for academic year 2018
• current_expense_19. Current Expense file for academic year 2019
• current_expense_20. Current Expense file for academic year 2020
• combined_current_expense. Combined file, includes all years

Documentation

Calculation of current expense (cost) of education per average daily attendance.

Student absenteeism data by ethnicity, gender, program subgroup, and grade span. Chronic absenteeism counts, cumulative enrollment, and chronic absenteeism rate data are provided.

cde.ca.gov-absenteeism-2

Resources

• chronic_absenteeism. Combined Chronic Absenteeism files

Documentation

Documentation Links

• https://www.cde.ca.gov/ds/sd/sd/filesabd.asp

Contacts

• Wrangler

Eric Busboom, Civic Knowledge

Packages

• zip https://library-metatab-org.s3.amazonaws.com/cde.ca.gov-absenteeism-2.zip
• csv https://library-metatab-org.s3.amazonaws.com/cde.ca.gov-absenteeism-2.csv
• source https://github.com/Metatab/metatab-packages.git

Accessing Packages in Metapack

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

chab17 | chab16 | chronic_absenteeism

Stream discharge rates for the Fashion Valley station on the San Diego river, from 1988 on

waterservices.usgs.gov-stream_discharge-fashion_valley_sd-2

Resources

• fashion_valley. Stream discharge rates for the San Diego River, Fashion Valley Station.

Documentation

Original Notice

The data file includes this comment header:

    # ---------------------------------- WARNING ----------------------------------------
    # Some of the data that you have obtained from this U.S. Geological Survey database may not 
    # have received Director's approval.  Any such data values are qualified as provisional and 
    # are subject to revision.  Provisional data are released on the condition that neither the 
    # USGS nor the United States Government may be held liable for any damages resulting from its use.
    #  Go to http://help.waterdata.usgs.gov/policies/provisional-data-statement for more information.
    #
    # File-format description:  http://help.waterdata.usgs.gov/faq/about-tab-delimited-output
    # Automated-retrieval info: http://help.waterdata.usgs.gov/faq/automated-retrievals
    #
    # Contact:   gs-w_support_nwisweb@usgs.gov
    # retrieved: 2018-07-24 15:45:47 -04:00 (natwebcaas01)
    #
    # Data for the following 1 site(s) are contained in this file
    #    USGS 11023000 SAN DIEGO R A FASHION VALLEY AT SAN DIEGO CA
    # -----------------------------------------------------------------------------------
    #
    # TS_ID - An internal number representing a time series.
    # IV_TS_ID - An internal number representing the Instantaneous Value time series from which the daily statistic is calculated.
    #
    # Data provided for site 11023000
    #    TS_ID       Parameter    Statistic  IV_TS_ID       Description
    #    7745        00060        00003      14023          Discharge, cubic feet per second (Mean)
    #
    # Data-value qualification codes included in this output:
    #     A  Approved for publication -- Processing and review completed.
    #     e  Value has been estimated.
    #     P  Provisional data subject to revision.
    #

Documentation Links

• https://waterdata.usgs.gov/ca/nwis/uv?11023000Web page with links to graphs and downloads
• https://waterservices.usgs.gov/rest/DV-Test-Tool.htmlUSGS Daily Values Web Service URL Generation Tool

Contacts

• Wrangler

Eric Busboom, Civic Knowledge

UTM Grid Zone 11

usngcenter.org-usng-ca11-1

Resources

• ca11_usng. US National Grid for California, Zone 11

Documentation

Contacts

• Wrangler

Eric Busboom, Civic Knowledge

Packages

• csv https://library-metatab-org.s3.amazonaws.com/usngcenter.org-usng-ca11-1.csv
• zip https://library-metatab-org.s3.amazonaws.com/usngcenter.org-usng-ca11-1.zip
• source https://github.com/san-diego-water-quality/water-datasets.git

Accessing Packages in Metapack

import metapack as mp
# ZIP Package
pkg = mp.open_package('https://library-metatab-org.s3.amazonaws.com/usngcenter.org-usng-ca11-1.zip')
# CSV Package
pkg = mp.open_package('https://library-metatab-org.s3.amazonaws.com/usngcenter.org-usng-ca11-1.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 2018-08-09T15:00:34

The Atlas presents spatial data about supermarket accessibility and provides census-tract level data for food access

usda.gov-food_access-1

Resources

• sd_food_atlas. Food atlas, San Diego county tracts only

Documentation

The Food Access Research Atlas:

• Presents a spatial overview of food access indicators for low-income and other census tracts using different measures of supermarket accessibility;
• Provides food access data for populations within census tracts; and
• Offers census-tract-level data on food access that can be downloaded for community planning or research purposes.

This version of the Atlas dataset has only tracts in San Diego couny, and tract identifiers have been converted from Tiger format Geoids to ACS format.

High and Low ocean water levels, at the La Jolla station, from year 2000 to present.

tidesandcurrents.noaa.gov-water_levels-la_jolla-1

Resources

• water_levels. High and Low Ocean water levels for the La Jolla station.

Documentation

Access URls

To get the access URLs, visit the web tool page and click on the “Show Data Listing” button. This will display a table. On the right side of the table, click the button “Web Services”. This will pop up a dialog with URLs. On the line for “Water Levels” right click on the “CSV” badge and copy the URL.

San Diego county Tracts, MSAs and SRAs, based on 2010 Census boundaries, as extracted by SANGIS.

OrderedDict([('dataset', 'census-regions'), ('time', '2010'), ('version', '4'), ('space', 'sandiego'), ('origin', 'sangis.org'), ('@value', 'sangis.org-census_regions-2010-sandiego-4')])

Resources

• tracts. 2010 Census Tract boundaries
• msa. 2010 Metro Statistical Area boundaries
• sra. San Diego County Sub-Regional Areas boundaries
• zip. ZIP Code boundaries
• tract-sra-msa-xwalk. Crosswalk between crosswalks, tracts, zip codes and SRAs.

Documentation

Documentation Links

• docs/tracts-sras-and-msaaOverview of how SRAs are composed of tracts and related to MSAs.

Contacts

• Origin

SANGIS

A combined dataset of NOAA weather reports, USGS flow rates for the Sn Diego River and NOAA tide levels for La Jolla.

sandiegodata.org-tides_rain_river-6

Resources

• tides_river_rain. Combined max, min tides, San Diego river flow and daily precipitation

Documentation

This package combines three other datasets for use in the water quality data ptoject:

* waterservices.usgs.gov-stream_discharge-fashion_valley_sd
* tidesandcurrents.noaa.gov-water_levels-la_jolla#water_levels
* noaa.gov-localclimate-200808_201807-san#lcd-san

The resulting dataset has daily values for rainfall, flow rate for the San Diego river, and the max and min tide level at La Jolla.

__

sandiegodata.org-la_parcels-2

Resources

• la-planning-code.
• tract_to_parcels.

Documentation

Static files for links between LA parcels and LA tracts.

See the notebooks directory for the code bits thatgenerated the files. They are slow and take a lot of memory, since there are about 2M parcels in the file

Contacts

• Wrangler

Eric Busboom, Civic Knowledge

Packages

• zip https://library-metatab-org.s3.amazonaws.com/sandiegodata.org-la_parcels-2.zip
• csv https://library-metatab-org.s3.amazonaws.com/sandiegodata.org-la_parcels-2.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/sandiegodata.org-la_parcels-2.zip')
# CSV Package
pkg = mp.open_package('https://library-metatab-org.s3.amazonaws.com/sandiegodata.org-la_parcels-2.csv') 

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

References

Urls used in the creation of this data package.