How do you analyze space with GIS tools?

1 Spatial data and layers

Spatial data is any data that has a geographic location, such as coordinates, addresses, boundaries, or features. Spatial data can be stored and displayed in GIS as layers, which are collections of related spatial features. For example, a layer can represent roads, buildings, rivers, or land use types. Layers can be vector or raster, depending on the format and structure of the data. Vector layers use points, lines, and polygons to represent discrete features, while raster layers use grid cells to represent continuous values, such as elevation, temperature, or population density.

2 Spatial queries and selections

One of the most basic operations in spatial analysis is to query and select spatial data based on certain criteria. For example, you can use a spatial query to find out which features in one layer intersect, contain, or are within a certain distance from features in another layer. You can also use an attribute query to filter features based on their non-spatial properties, such as name, type, or value. You can combine spatial and attribute queries to perform more complex selections. For example, you can select all the schools that are within a 10 km buffer of a river and have more than 500 students.

3 Spatial statistics and measurements

Another common operation in spatial analysis is to calculate statistics and measurements for spatial data. For example, you can use spatial statistics to describe the distribution, concentration, or relationship of features or values in a layer. You can also use spatial measurements to calculate the area, perimeter, length, distance, or angle of features or segments. Spatial statistics and measurements can help you identify patterns, trends, anomalies, or correlations in spatial data. For example, you can use spatial statistics to measure the spatial autocorrelation of crime rates or the spatial regression of house prices.

4 Spatial modeling and interpolation

A more advanced operation in spatial analysis is to create models and interpolate values for spatial data. For example, you can use spatial modeling to simulate scenarios, predict outcomes, or optimize solutions for spatial problems. You can also use spatial interpolation to estimate values for locations where data is missing or sparse, based on the values of nearby locations. Spatial modeling and interpolation can help you explore possibilities, test hypotheses, or fill gaps in spatial data. For example, you can use spatial modeling to plan the optimal location of a new facility or use spatial interpolation to create a surface of rainfall from point measurements.

5 Spatial visualization and mapping

The final operation in spatial analysis is to visualize and map the results of your analysis. For example, you can use spatial visualization to display the spatial data and layers in different ways, such as symbols, colors, sizes, or charts. You can also use spatial mapping to create maps that communicate your findings, such as thematic maps, cartograms, or web maps. Spatial visualization and mapping can help you explore, understand, and share spatial data and analysis. For example, you can use spatial visualization to compare the spatial patterns of different variables or use spatial mapping to create an interactive web map that shows the impact of a natural disaster.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?