Using GIS Effectively to Visualize Government Data: A Case Study from Colorado Christopher Harless Torry van Slyke.

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Presentation transcript:

Using GIS Effectively to Visualize Government Data: A Case Study from Colorado Christopher Harless Torry van Slyke

Introduction GIS: More than just pretty maps

First, a disclaimer We are not GIS gurus: We have only scratched the surface of what GIS can do. Our office is just beginning to use GIS analysis in our audits. Our goal is to spur your imagination.

GIS for Data Analysis Why use GIS? Because some questions require spatial thinking for an answer. Maps help us see and analyze: –Concentrations (population density) –Distributions (which areas have more hospitals?) –Distance relations (marijuana shops that are within 1,000 feet of a school)

GIS for Data Analysis The Aha! Moment for us: GIS is not just a graphical tool for creating beautiful maps; it is also a database. As such, we can use it to create data tables for importing into our analytic tool of choice (MS Access, for me).

Case Study Background Local Sales Taxes in Colorado

A very complex system –297 cities, towns, counties, special districts, local improvement districts, and transportation authorities Each jurisdiction has authority to set its own sales tax rate and define allowable exemptions within statutory parameters.

Local Sales Taxes in Colorado The amount of sales taxes owed depends on the location of the sale within the patchwork of jurisdictions. The Colorado Department of Revenue is responsible for collecting sales taxes from businesses on behalf of most local governments in the state.

Example of Complexity

The Question for the Audit Legislative request: Are local governments receiving the correct amount of sales tax revenue? Refocusing the question: How do we know businesses are collecting the right amounts for the right jurisdictions? A key control = the registration of businesses for sales tax licenses

Why GIS? Information provided by businesses on applications is insufficient –ZIP codes are imprecise –Business owners may not know whether they are truly inside or outside a municipality Ultimate goal: not so much a map as a table of data that we can analyze and compare to Department data –The map is a means to an end

How we used GIS From spatial analysis to audit finding

GIS Terminology GIS data is vector (points, lines, polygons) or raster –Points: buildings, mountain peaks, fire hydrants –Lines: roads, rivers, flight paths –Polygons: cities, lakes, land parcels Anything with a geometric boundary encircling an area –Raster: digital aerial photographs, satellite imagery, scanned maps Matrix of cells/pixels where each contains a value representing information Layer: Contents of a map that displays geographic datasets within data frame

The World into GIS Layers

GIS Terminology (cont.) Attribute Table: Visualize, query, and analyze the data underlying a layer Each field can store a specific type of data (e.g., county, special district, street address) that relates to each record

What We Needed 1.Tax jurisdiction boundaries (layers) –Municipalities, counties, special districts (e.g., RTD), local improvement districts, transportation authorities 2.Business Addresses 3.Tool to convert addresses to points Geocoded points

What We Did Union: Combined individual tax layers into one multifaceted layer Geocode: Assigned X,Y coordinates for each business address and plotted as points Intersect: Combined the layer and point information into a single attribute table showing which tax jurisdictions contain or touch the points

Analysis Comparing the GIS Results to Department Data

Results Snapshot

Findings for Tax Year 2014 Sites reviewed: 103,836 Total sites misregistered: 11,070 (11%) Sites misregistered for state- administered jurisdictions: 3,746 Potential over-collections: $3.3 mil. Potential under-collections: $3.8 mil.

Gaps in the Department’s System

Questions?

1525 Sherman Street, 7 th Floor, Denver, Colorado