This topic includes attribute queries, spatial queries, and proximity analysis.
Analyzing data spatially often reveals new
information. Visualizing where things occur (and how many) can lead us to
better understand what is happening in different places.
Why does location matter?
Most analyses, whether spatial or not, start by asking questions of the data (querying). Attribute queries use the values in the data, whereas spatial queries use the location of the data. Spatial data has geometric and topological properties. Geometric properties include position and measurements, such as length, direction, area, and volume. Spatial data also has topological properties that entail relational characteristics such as connectivity, inclusion, and adjacency. Using these spatial properties, the questions that can be asked of data can be expanded and new insights can be gained.
Descriptive statistics can help describe the main features of a dataset. Combined with location, the main features of an area can be explained and, subsequently, mapped to tell a clear message. Understanding what is happening is important; additionally, knowing where it is happening is more powerful for both interpreting and reporting results.
This topic includes a number of case studies that, in part, use the attribute and spatial properties of data. These are exploratory analyses, designed to demonstrate an approach to a specific problem using ArcGIS. For each case study, additional resources have been made available including workflows that describe how the analysis was done in ArcGIS and a GPK (geoprocessing package) in which all resources (models, scripts, data, layers, and files) needed to perform the described analysis are included in the package.
What questions can I answer?
By understanding and comparing places, you could answer these types of questions:
- Where are the events located?
- Where do the values occur?
- How many features are in that location?
- What types are in this area?
- Are certain characteristics found nearby?