citysdk

CitySDK v2.0 (Beta)

Thank You’s due to some very generous Clojurians:

Installation

npm install citysdk

The citysdk Function

CitySDK v2.0 exports a single function, which takes two arguments:

Parameters

Brief overview of each argument parameter that can be passed into CitySDK v2.0

Parameter Type Description Geocodes Stats GeoJSON GeoJSON with Stats
vintage int/str The reference year (typically release year) of the data
geoHierarchy object The geographic scope and hierarchical path to the data
sourcePath array Refers to the Census product of interest    
values array For statistics, values request counts/estimates via variable IDs    
geoResolution str Resolution of GeoJSON ("20m", "5m", and "500k" available)    
predicates object Used as a filter available on some values   *   *
statsKey str You may request a key for Census’ statistics API here   **   **

* : optional ** : optional for < 500 requests daily

Geocoding (latitude/longitude -> FIPS code)

With the exception of “microdata” statistics (not yet available via Census’ API), all Census data is aggregated to geographic areas of different sizes. As such, all of Census’ API’s require a set of/unique geographic identifier(s) to return any data (AKA: FIPS). Given that these identifiers are not common knowledge, the CitySDK provides a way for the user to identify their geographic scope of interest using a geographic coordinate (lat + lng).

Under the hood, this functionality calls the TigerWeb Web Mapping Service with the lat & lng provided and pipes the resulting FIPS codes into your options argument with the appropriate GEOIDs for identifying your geographic area of interest.

For a list of geographies currently available for geocoding with this feature, see the Geographies Available by Vintage section below.

There are two ways to scope your geography using this functionality:

  1. Request a single geographic area
  2. Request all of a descendant geography-type of a coordinate-specified geographic area

Example: Request a single geographic area by coordinate

RETURN TYPE: JSON

You may pass a {"lat" : <float>, "lng" : <float>} object as the first and only value for the geoHierarchy key:

import census from 'citysdk'

census({
    "vintage" : 2015,    // required
    "geoHierarchy" : {   // required
      "county" : {
        "lat" : 28.2639, 
        "lng" : -80.7214
      }
    }
  }, 
  (err, res) => console.log(res)
)

// result -> {"vintage":"2015","geoHierarchy":{"state":"12","county":"009"}}

Notice how the function prepends an additional geographic component ("state" : "12") to the options object. In order to fully qualify the geographic area (GEOID) associated with the county, the state is needed. In this example the fully qualified GEOID would be 12009 with the first two digits (12) qualifying the state and 009 qualifying the county within that state. This appropriate geographic hierarchy creation is handled by the function for you.

Example: Request all of a descendant geography-type within a coordinate-specified geographic area

RETURN TYPE: JSON

import census from 'citysdk'

census({
    "vintage" : "2015",   // required
    "geoHierarchy" : {    // required
      "state" : {
        "lat" : 28.2639, 
        "lng" : -80.7214
      },
      "county" : "*"      // <- syntax = "<descendant>" : "*"
    }
  }, 
  (err, res) => console.log(res)
)

// result -> {"vintage":"2015","geoHierarchy":{"state":"12","county":"*"}}

All Census-defined geographic areas are composed of Census “Blocks”. Some of these composed areas - themselves - compose into higher-order areas. These nested relationships between certain geographic areas allows the Census data user to request all descendants of a particular type.

Caveats

1) In this example, we added a second geographic level to our geoHierarchy object ("county" : "*"). It is important to use the "*" expression signifying that you want all of the specified level of descendants within the geography for which you supply a coordinate. No other expression will work. 2) Internally, the CitySDK converts the geoHierarchy object to an ordered set, so this part of your request object must be in descending hierarchical order from parent -> descendant. E.g. - in the above - an object that contained {"county" : "*", "state" : {"lat" <lat> "lng" <lng>}} will not work.

Statistics

This parameter set will call the Census Statistics API and reformat the results with a couple highly requested features:

There are two ways to request Census statistics using citysdk: 1) Calling for values of estimates and other statistical values (required) 2) Apply a filter by using predicates (optional)

For both of these options, a sourcePath needs to be supplied. This is the fully qualified path to the product. For more information about how to find the sourcePath to your product of interest, go to the Developers’ Microsite and - in any of the examples of making a call - take the path between <vintage>/ and the ?get. For example, for American Community Survey 1-year you’ll the first example (2017) shows:

https://api.census.gov/data/2017/acs/acs1?get=NAME,group(B01001)&for=us:1
                           └─┬─┘└───┬────┘        
                         vintage sourcePath

The corresponding sourcePath for this endpoint is ["acs", "acs1"]

Example: get "values" by ID:

RETURN TYPE: JSON

import census from 'citysdk'

census({
    "vintage" : 2015,        // required
    "geoHierarchy" : {       // required  
      "county" : {
        "lat" : 28.2639, 
        "lng" : -80.7214
      }
    },
    "sourcePath" : ["cbp"],  // required 
    "values" : ["ESTAB"]     // required 
  }, 
  (err, res) => console.log(res)
)

// result -> [{"ESTAB":13648,"state":"12","county":"009"}]

Here, we added the parameters for sourcePath (the path to the survey and/or source of the statistics) and values (the identifiers of the statistics we’re interested in). By including these parameters within your argument object, you trigger the census function to get statistics. This “deploy on parameter set” strategy is how the census function determines your intent.


🤔 Help for Discovering Census data

You’re probably thinking: “How am I supposed to know what codes to use inside those parameters?” - or - “Where did that "cbp" & "ESTAB" stuff come from?” The data sets covered by the CitySDK are vast. As such, this is the steepest part of the learning curve. But, don’t worry, there are a number of different resources available to assist you in your quest:

  1. The Census Developers’ Microsite <- START HERE
  2. The Census Discovery Tool.
  3. Census Slack and Gitter developer communities.
  4. Data Experts

Example: get "values" by ID (with key):

RETURN TYPE: JSON

census({
    "vintage" : 2015,             // required
    "geoHierarchy" : {            // required
      "county" : {
        "lat" : 28.2639, 
        "lng" : -80.7214
      }
    },
    "sourcePath" : ["cbp"],        // required 
    "values" : ["ESTAB"],          // required 
    "statsKey" : "<your key here>" // required for > 500 calls per day
  }, 
  (err, res) => console.log(res)
)

// result -> [{"ESTAB":13648,"state":"12","county":"009"}]

Example: Filter results by predicates:

RETURN TYPE: JSON

predicates

Predicates are used to create a sub-selection of statistical values based on a given range or categorical qualifyer.

census({
    "vintage" : "2017",
    "geoHierarchy" : {
      "state" : "51",
      "county" : "*"
    },
    "sourcePath" : ["acs", "acs1"],  
    "values" : ["NAME"],            
    "predicates" : {                
        "B01001_001E" : "0:100000"  // number range separated by `:`
    },
    "statsKey" : "<your key here>"
  }, 
  (err, res) => console.log(res)
)

/* result:
    [
      {
        "NAME":"Augusta County, Virginia",
        "B01001_001E" : 75144,
        "state":"51",
        "county":"015"
      },
      {
        "NAME":"Bedford County, Virginia",
        "B01001_001E" : 77974,
        "state":"51",
        "county":"019"
      },
      ... 
    ]
*/

Timeseries data (Statistics Only)

If you’d like to use “timeseries” data, you may do so for statistics only. Mapping timeseries data is currently unsupported. Note that many timeseries products rely heavily on the "predicates" option:

Example: get 'timeseries" data:

RETURN TYPE: JSON

census({
    "vintage" : "timeseries",// required
    "geoHierarchy" : {       // required  
      "county" : {
        "lat" : 28.2639, 
        "lng" : -80.7214
      }
    },
    "sourcePath" : ["asm", "industry"],  // required 
    "values" : ["EMP","NAICS_TTL","GEO_TTL"],
    "predicates": {"time": "2016", "NAICS": "31-33"}
  }, 
  (err, res) => console.log(res)
)

/* result:
[{"EMP": 11112764, 
  "NAICS_TTL": "Manufacturing", 
  "GEO_TTL": "United States", 
  "time": "2016", 
  "NAICS": "31-33", 
  "us":"1"}]
*/

For some sources (e.g., the American Community Survey), most of the values can also be used as predicates, but are optional. In others, (e.g., International Trade), predicates are a key part of the statistical query. In either case, at least one value within values must be supplied.

Cartographic GeoJSON

You can also use the CitySDK to retrieve Cartographic Boundary files, which have been translated into GeoJSON. The only additional parameter you’ll need to know is a simple declaration of geoResolution of which there are three options:

Resolution Map Scale Benefits Costs
500k 1:500,000 Greatest variety of summary levels & Most detailed largest file sizes
5m 1:5,000,000 Balance between size and detectable area size lowest variety of available area types
20m 1:20,000,000 Smallest file sizes lowest level of detail

See the full available Cartographic GeoJSON in the Geographies Available by Vintage section


Example: Saving the file locally in Node.js using fs

RETURN TYPE: JSON STRING

var fs = require("fs")

census({
    "vintage" : 2017,
    "geoHierarchy" : {
      "metropolitan statistical area/micropolitan statistical area": "*"
    },
    "geoResolution" : "500k" // required
  }, 
  (err, res) => {
    fs.writeFile("./directory/filename.json", 
      JSON.stringify(res), 
      () => console.log("done")
  )}
)

This would convert the returned geojson to a string, which allows it to be saved via Node.js’ fileSystem API.

Notable Example:

census({
    "vintage" : "2017",
    "geoHierarchy" : {
      "state": "51",
      "county": "*"
    },
    "geoResolution" : "500k" // required
  }, 
  (err, res) => console.log(res)
)

It’s important to note that - when querying for these GeoJSON files - you may retrieve a larger area than your request argument specifies. The reason for this is that the files are (currently) stored at two geographic levels: National and by State. Thus, the query above will attempt to resolve, at the state level, all counties, but because counties are stored at the national level in vintage 2017, all the counties in the US will be returned by this query.

If you wish to get back only those geographies you specify, you may do so by using the last and perhaps most useful feature included in the v2.0 release: Getting GeoJSON with statistics included within the "FeatureCollection" properties object!

GeoJSON Merged with Statistics

RETURN TYPE: JSON

There are a number of reasons you might want to merge your statistics into their GeoJSON/geographic boundaries, all of which are relevant when seeking to map Census data:

1) Creating choropleth maps of statistics (e.g., using values) 2) Mapping only those geographies that meet a certain set of criteria 3) Showing a user their current Census geographic context (i.e., leveraging the Geocoding capabilities of CitySDK)

Dynamic Use Example

A more dynamic example of using stats merged with GeoJSON on the fly with citysdk can be found here:

mapbox-geocoding

Type in a county name and see the unweighted sample count of the population (ACS) for all the Block Groups within that County.

Use Chrome for best results (mapbox-gl geocoder caveat)

source code

All Counties

census({
    "vintage" : "2017",
    "geoHierarchy" : {
      "county": "*"
    },
    "sourcePath" : ["acs", "acs5"],  
    "values" : ["B19083_001E"],    // GINI index             
    "statsKey" : "<your key here>",
    "geoResolution" : "500k"
  },
)

In this example, we use citysdk to create the payload and then save it via Nodes fs.writeFileSync and then serve it via a Mapbox-GL map.

counties

source code

Notable Example:

All ZCTAs (zip code tabulation areas in the US)

census({
    "vintage" : "2017",
    "geoHierarchy" : {
      "zip-code-tabulation-area" : "*"
    },
    "sourcePath" : ["acs", "acs5"],  
    "values" : ["B19083_001E"], // GINI index             
    "statsKey" : "<your key here>",
    "geoResolution" : "500k"
  },
)

This is a very large request, in fact, one of the largest you could possibly make in a single citysdk function call. It is so large, in fact that it currently only works on Node and only if you increase your node --max-old-space-size=4096. With large merges (such as all counties or zctas), it is recommended not to try to use citysdk dynamically, but - rather - to munge your data before hand with citysdk and then serve it statically to your mapping library, as was done here:

Zip Code Tabulation Areas

source code

Other Argument Examples:

// Call the WMS only
{ 
  "vintage": 2014,
  "geoHierarchy": { "state": { "lat": 28.2639, "lng": -80.7214 }, "county": '*' } 
}

// Getting the stats for a single county filtering out any county with population over 100,000
{ 
  "vintage": 2016,
  "geoHierarchy": { "county": { "lat": 28.2639, "lng": -80.7214 } },
  "sourcePath": [ "acs", "acs5" ],
  "values": [ "B01001_001E" ]
  "predicates": { "B00001_001E": "0:100000" },
}

// strings are valid as vintages as well
{ 
  "vintage": "2015",
  "geoHierarchy": { "county": { "lat": 28.2639, "lng": -80.7214 } },
  "sourcePath": [ "cbp" ],
  "values": [ "ESTAB" ]
}

// Just geojson for all the counties within a state located by a given coordinate
{ 
  "vintage": 2014,
  "geoHierarchy": { "state": { "lat": 28.2639, "lng": -80.7214 }, "county": "*" },
  "geoResolution": "500k"
}

// For large request expect to have to increase `node --max-old-space-size=4096`
{ 
  "vintage": 2016,
  "sourcePath": [ "acs", "acs5" ],
  "values": [ "B25001_001E" ],
  "geoHierarchy": { "zip-code-tabulation-area": "*" },
  "geoResolution": "500k"
}

Census Cartography Files in GeoJSON Format

The Census Bureau publishes both high and low accuracy geographic area files to accommodate the widest possible variety of user needs (within feasibility). Cartography Files are simplified representations of selected geographic areas from the Census Bureau’s Master Address File/Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) system. These boundary files are specifically designed for small scale thematic mapping (i.e., for visualizations).

For a while now, we have published our cartography files in the .shp format. More recently, we expanded our portfolio of available formats to .kml. It is with this release that we follow suit with the community at large to release these boundaries in .json (GeoJSON) format.

Geographies Available by Vintage

The most comprehensive set of geographies and vintages can be found within the 500k set. Some vintages - 103 through 110 - are references to sessions of Congress and only contain a single geographic summary level: "congressional district" The following tables represent the availability of various geographic summary levels through the remaining vintages:

Geographic Area Type 1990 2000 2010 2012 2013 - 2015 2016 - 2017
"alaska native regional corporation"  
"american indian-area/alaska native area/hawaiian home land"  
"block group"  
"combined new england city and town area"        
"combined statistical area"      
"congressional district"    
"consolidated cities"    
"county"  
"county subdivision"  
"division"    
"metropolitan statistical area/micropolitan statistical area"      
"new england city and town area"      
"place"  
"public use microdata area"        
"region"    
"school district (elementary")      
"school district (secondary")      
"school district (unified")      
"state"  
"state legislative district (lower chamber")  
"state legislative district (upper chamber")  
"tract"  
"urban area"  
"us"      
"zip code tabulation area"      

More Information about Cartography Files

Community

Dedicated Data Experts

If you’re new to Census data and need some help figuring out which of the many products Census curates for public use, don’t hesitate to reach out to these contacts for help: