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GeoSpatial Query API: US Census Boundaries and Census Data

GeoSpatial Query API: US Census Boundaries and Census Data

By: Mobile Data Books Latest Version: 1.0.1

Product Overview

Facilitate your location and audience analysis, find where your target customers live, spot prospective markets, and pick the right locations for your marketing, sales, and other business activities.

With the GeoSpatial Query API you can:

  • Integrate the U.S. Census legal and statistical boundaries and geospatial point-in-polygon search into location-aware apps.
  • Display interactive maps with highlighted regions, overlays, and custom annotations.
  • Use geographic map as a User Interface, which allows users to select a specific geo-location on the map and analyze, interpret and visualize the demographic and economic data related to the selected location in the spatial context on the map.
  • Add over 200 fields of location-specific census demographic and economic data to the selected geo-location.
  • Easily handle large amount of data in mobile devices. GeoSpatial Query API is especially optimized for mobile. The small data size and high web service speed allows you to deliver only as much data as it is needed for the currently selected boundary level.

GeoSpatial Query API is built as a RESTful API and offers developers simplicity, enhanced performance, significantly increased scalability and speed. It offers great flexibility by supporting many different formats, including the most widespread data formats such as GeoJSON, KML, WKT. Customers will benefit from low bandwidth and very fast access to data. GeoJSON response is provided for debugging purposes or fallback for more traditional usage. In addition to the high performance, the response is delivered in binary form with compact memory layout reducing the memory consumption.

GeoSpatial Query API is available in both gRPC and HTTP REST API:

  • gRPC uses protocol buffers to provide an easy way to precisely define a service and auto generate reliable client libraries for iOS, Android and the servers providing the back end.
  • gRPC helps developers improve velocity, save bandwidth, do more over fewer TCP connections and save CPU usage.
  • gRPC uses HTTP/2, enabling applications to present both a HTTP 1.1 REST/JSON API and an efficient gRPC interface on a single TCP port. This provides developers with compatibility with the REST web ecosystem, while advancing a new, high-efficiency RPC protocol.

Supported data formats:
GeoSpatial Query API offers great flexibility by supporting many different formats and can run in any environment. It supports most of the programming languages, including GeoJSON, KML, WKT.

Protocol buffers
Protocol buffers currently support generated code in Java, Python, Objective-C, and C++. With Google's new proto3 language version, you can also work with Dart, Go, Ruby, and C#, with more languages to come.

GeoSpatialQuery FlatBuffers:
GeoSpatialQuery FlatBuffers is a performant binary geo-spatial file format suitable for serving large data. It is is an efficient cross platform serialization library for C++, C#, C, Go, Java, Kotlin, JavaScript, Lobster, Lua, TypeScript, PHP, Python, Rust and Swift.

  • Very compact - typically makes GeoJSON 6-8 times smaller. 2-2.5x smaller even when comparing gzipped sizes.
  • Very fast encoding and decoding - even faster than native JSON parse/stringify.
  • Easy incremental parsing - get features out as you read them, without the need to build in-memory representation of the whole data.
  • Access to serialized data without parsing/unpacking - the hierarchical data in a flat binary buffer is represented in such a way that it can still be accessed directly without parsing/unpacking, while also still supporting data structure evolution (forwards/backwards compatibility).
  • Memory efficiency and speed - The only memory needed to access your data is that of the buffer. It requires 0 additional allocations (in C++, other languages may vary). GeoSpatialQuery FlatBuffers is also very suitable for use with mmap (or streaming), requiring only part of the buffer to be in memory.
  • Partial reads - read only the parts you actually need, skipping the rest.
  • Very compact - typically makes GeoJSON 6-8 times smaller. 2-2.5x smaller even when comparing gzipped sizes.



Operating System

Linux/Unix, Amazon Linux Amazon Linux 2

Delivery Methods

  • Amazon Machine Image

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