Geospatial data is any data that contains location information. Geographic information can come in a variety of forms &ndash geographic coordinates such as latitude and longitude, street addresses, and others – and can be captured by a variety of methods – GPS, IP addresses, LIDAR, historical records, digitization, and others. In short, if data can be placed on a map, it can be characterized as geospatial data.

Geospatial Data Models

There are two basic geospatial data models:

• ​Vector
  • ​Coordinate-based data model that represents geographic features as points, lines, and polygons
  • Tabular data is associated with each vector feature
  • Common formats include Shapefiles, File Geodatabase Feature Classes, and few others
• Raster
  • Pixel-based data model that defines space as an array of equally sized cells arranged in rows and columns
  • Each cell, or pixel, holds one value, but can also contain more than one band to hold more than one value
  • Common formats include TIFF, JPEG, MrSID, Esri GRID, File Geodatabase Raster, netCDF, and many others!

Geospatial data on its own is not very useful — it commonly comes in the form of CSV files, shapefiles, or file geodatabase feature classes. This raw data is only made useful after it is imported into a GIS software, which interprets the data and allows us to analyze and visualize it.

What we can do with geospatial data varies widely depending what the data represents and what we want to investigate using that data. For example, if we have a set of geospatial data that contains transaction locations for a U.S. business, we can visualize that data on a map and import U.S. Census figures to calculate how many purchases are coming from urban vs. rural areas, or we can analyze the data and highlight where customers are grouped vs. where they are spread out, or we can utilize a wide range of other analyses.

The possibilities are always plentiful when working with geospatial data, so it helps to have a well-defined question or goal before you start running analyses and visualizing your data.

Coordinate Systems

All geospatial data has a coordinate system, or projection, that the data is stored in that ties the feature to a location related to the Earth's surface. When working with GIS projects that incorporate more than one layer of geospatial data, each layer must be visualized in the same coordinate system on a map. GIS software is designed to align geospatial data with different coordinate systems on the fly using complex calculations called transformations, however, it is best practice to convert all data in one project into the same coordinate system.

From Esri:

Coordinate systems, also known as map projections, are arbitrary designations for spatial data. Their purpose is to provide a common basis for communication about a particular place or area on the earth's surface. The most critical issue in dealing with coordinate systems is knowing what the projection is and having the correct coordinate system information associated with a dataset.

Geographic vs. Projected Coordinate Systems

Coordinate systems come in two forms:

• Geographic
  • ​Based on a spheroidal model of the earth
  • Reference lines are parallels (latitude) and meridians (longitude)
  • Uses geographic coordinates and angular measurements (x,y – where x is lon., y is lat.) to define unique positions on the earth
  • Defined by a geographic datum
• Projected
  • Represents the curved/spherical surface of the earth to a planar or flat surface
  • Requires a systematic mathematical transformation of the earth's lines of longitude and latitude onto a plane
  • Uses length-based units (e.g. m and ft)
  • Introduces distortion of the map data, but designed to minimize four areas:
    • Distance → Equidistant
    • Area → Equal area
    • Shape → Conformal
    • Direction → Azimuthal
  • Three primary types
    • Planar – surface is tangent to the globe
    • Conical – surface formed into a cone
    • Cylindrical – surface is formed into a cylinder

Choosing a coordinate system is an important part of conducting geospatial analysis and managing your geospatial data with GIS software can help with this effort. Data often starts off in one coordinate system and will need to be projected, or transformed, to a different coordinate system in order to produce an accurate analysis. The primary goal when choosing a coordinate system is to minimize error and distortions for the analysis being performed or the visualization being produced. It also requires asking questions like:

• Where in the world is the project area?
• What scale and extent is the analysis?
  • Small scale, large extent (World to State)
  • Large scale, small extent (County to City)
• What type of measurements will be performed?
  • Simple locations (Geographic Coord. Sys.)
  • Distances (Equidistant Projected Coord. Sys.)
  • Areas (Equal Area Projected Coord. Sys.)
• What coordinate system is the data already in?
• How will changing the coordinate system affect raster data?

These questions are a few of the more important questions to consider, but there could be more issues to consider depending on your analysis.