Modeling Problems Geographically
Aggregate and Normalize
Uncover patterns in datasets by filtering, simplifying, aggregating, and enriching data. This scenario compares the density of crime incidents to population by town.
Intersect and Extract
Define an area of interest, extract parcels that intersect the area in question, join parcel data by CAMA id and visualize property parcels by land use.
Buffer and Route
Using network analysis tools map service to determine all ski resorts in New Hampshire with a specified distance from a point of interest. Buffer a multiple distances (Buffer Rings) around the point of reference.
Identify, quantify, and find spatial patterns in your data. This example identifies patterns of crime incidents in New Hampshire. Extract crime data from the FBI’s NIBRS database. Filter data to only display 2018 data. Enrich data with population, income, poverty, unemployment and other demographic variables.
Using the Geometry to perform advanced geometric and spatial relationship operations on points, lines, and polygons.
View, project, and perform spatial analyses on your data in any coordinate system.
Layers of Information
Spatial analysis is a process in which you model problems geographically, derive results by computer processing, and then explore and examine those results. This type of analysis has proven to be highly effective for evaluating the geographic suitability of certain locations for specific purposes, estimating and predicting outcomes, interpreting and understanding change, detecting important patterns hidden in your information, and much more.