Documentation

CitySDK v2.0 (Beta)

CitySDK streamlines the development of applications that use the Census Bureau Data. It combines the Census Data API with TIGER to provide GeoJSON (geographic shapes), and geocoder to provide translations from latitude/longitude to the required geography study area. This abstracts the multiple APIs into one query allowing for developers, data scientisit, and civic innovators to drastically reduce the time to explore data, get insight and develop applications.

CitySDK is maintained by Logan Powell.

Installation

npm install citysdk

The citysdk Function

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

• The first is an options object with a set of key/value pair parameters (See “Parameters” below)
• The second is a conventional (error, response) node-style callback, which will be called upon completion of the census function and applied to the response

census({ key: value }, function(error, response) {
  //callback
});

Alternatively you can create a CitySDK promise by using the following snippet.

function censusPromise(args) {
  return new Promise(function(resolve, reject) {
    census(args, function(err, json) {
      if (!err) {
        resolve(json);
      } else {
        reject(err);
      }
    });
  });
}

Parameters

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

Geocoding (latitude/longitude -> FIPS code)

CitySDK provides a way for the user to identify their geographic scope of interest using a geographic coordinate (lat + lng), given that these identifiers (FIPS/GEOIDs) are not common knowledge.

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

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

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

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:

• Census statistics are returned as a standard JSON object rather than the csv-like format of the “raw” API
• Statistical values are translated into properly typed numbers (Integers and Floats instead of strings), whereas all values are returned as strings via the “raw” API
• Annotation values (e.g., error codes) that are returned (e.g., American Community Survey error codes) in places where data would be expected are returned as strings (rather than numbers) to make differentiating them from values a simple type check.

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:

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.

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

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:

Predicates are used to create a sub-selection of statistical values based on a given range or categorical qualifyer. Example below shows getting the range of Virginia counties that have a population between 0 and 100,000.

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:

census(
  {
    vintage: "timeseries", // required
    geoHierarchy: {
      // required
      us: "*"
    },
    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:

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

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

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)

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.

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:

More Information about Cartography Files

• For more information about the files translated herein please visit the Census Bureau’s Cartographic Boundary File Description Page
• For a comparison of the available formats of geographic area files, please visit the Census Bureau’s TIGER Products page