GIS Data Visualization Specialist for Choropleth Map Series Using ArcGIS 10.8
A comparative choropleth map series of population across all 159 Georgia counties, showing how four classification methods change the interpretation of the same census data.
Data visualization plays a critical role in transforming raw demographic data into meaningful spatial insights. In this project, Spatial Nexus delivered a comprehensive choropleth mapping solution using population data from the U.S. Census Bureau, focusing on all 159 counties in Georgia.
As a GIS Analyst, the objective was to design a comparative choropleth map series that highlights how different classification methods influence the interpretation of spatial patterns. Using ArcGIS 10.8, four distinct maps were created to ensure analytical depth and visual consistency.
The same population data can tell very different stories depending on how it is classified.
Non-specialists rarely see how classification choices influence a map.
Four maps needed identical styling so differences came only from the method.
OBJECTIVES
What the project set out to do
Map population for all 159 counties in Georgia, USA.
Compare Equal Interval, Quantile, Natural Breaks (Jenks) and Standard Deviation classification.
Keep consistent cartographic design across the series.
Deliver a clean, professional PDF map series.
Methodology
Our workflow, step by step
01
Data preparation
Census population figures were joined to county boundaries and checked for completeness.
02
Classification
Four maps were produced using Equal Interval, Quantile, Natural Breaks (Jenks) and Standard Deviation methods.
03
Consistent styling
A shared colour scheme, legend design and layout ensured a fair visual comparison.
04
Layout and export
The four maps were arranged in a single comparative series and exported as a high-quality PDF.
Visual output
The map
Select the image to view it at full size.
Results
What the analysis delivered
A side-by-side series that shows clearly how classification shapes interpretation.
Natural Breaks and Quantile maps highlight different patterns from the same data.
A reusable reference for teaching and reporting on thematic mapping.
Key highlights
Why this project stands out
Four classification methods compared
All 159 Georgia counties
Consistent professional styling
High-quality PDF deliverable
Data sources
Data used
U.S. Census Bureau population data
Georgia county boundaries (159 counties)
Tools and technologies
Software and methods
ArcGIS 10.8Spatial analysis and map production
Population DataU.S. Census Bureau
GIS Classification TechniquesStatistical data grouping and visualization
Deliverables
What the client received
Comparative choropleth map series (PDF)
Prepared GIS dataset
Classification notes
WHY IT MATTERS
Choosing the right classification method prevents misleading maps. This series makes those choices transparent for analysts, students and decision-makers.
FAQ
Questions about this project
What is a choropleth map?
A thematic map that shades areas such as counties according to a data value, for example population.
Which classification method is best?
It depends on the data distribution and purpose. Natural Breaks suits clustered data, Quantile gives equal counts per class, and Equal Interval is easiest to read.
Which software was used?
ArcGIS 10.8 for analysis, classification and map production.
Employment Density Mapping of Dübendorf City Using GIS & Kernel Density Analysis
A GIS-based employment density map of Dübendorf, Switzerland, using Kernel Density Estimation to reveal where jobs concentrate and support urban and economic planning.
Understanding the spatial distribution of employment is critical for effective urban planning and economic development. In this project, Spatial Nexus developed a comprehensive employment density map for Dübendorf, transforming complex datasets into actionable geospatial insights.
The objective was to analyze and visualize the concentration of employment opportunities across the city, enabling planners, policymakers, and businesses to better understand the local economic landscape. By leveraging advanced GIS techniques, the project delivers a clear and data-driven representation of urban employment patterns.
Employment, business and demographic data were held in separate tables with no spatial view.
Planners could not see where jobs clustered or how employment related to transport and land use.
Point-based records were too dense to interpret directly on a map.
OBJECTIVES
What the project set out to do
Map the spatial distribution of employment across the city.
Identify employment hotspots and lower-density areas.
Relate job concentrations to transport networks and land-use zones.
Deliver a clear, presentation-ready map for decision-makers.
Methodology
Our workflow, step by step
01
Data integration
Business, employment and demographic records were cleaned, geocoded and joined into one spatial database.
02
Projection and preparation
All layers were projected to a metric coordinate system so distances and search radii were accurate.
03
Kernel Density Estimation
KDE converted employment points into a continuous density surface, weighted by number of jobs.
04
Overlay analysis
The density surface was overlaid with transport and land-use layers to interpret why hotspots occur.
05
Cartographic design
A clear colour ramp, legend and context layers produced a readable, professional map.
Visual output
The map
Select the image to view it at full size.
Results
What the analysis delivered
Clear identification of the main employment centres in Dübendorf.
Visible relationship between job clusters, transport corridors and commercial zoning.
Evidence base for infrastructure, transport and economic development planning.
Key highlights
Why this project stands out
Kernel Density Estimation weighted by employment
Integration of business, demographic and land-use data
Transport and zoning overlays
Presentation-ready cartography
Data sources
Data used
Employment and business location records
Demographic datasets
Land-use zoning layers
Transport network data
Tools and technologies
Software and methods
ArcGISSpatial analysis and density modeling
QGISData processing and visualization
GIS DatasetsEmployment, demographics, land use, and transport networks
Microsoft OfficeReporting and data management
Deliverables
What the client received
Employment density map (print and digital)
Processed GIS datasets
Density raster surface
Methodology summary
WHY IT MATTERS
Employment density mapping gives planners an objective view of where economic activity happens, supporting decisions on transport investment, zoning and business development.
FAQ
Questions about this project
What is kernel density estimation in GIS?
KDE turns individual point locations into a smooth surface that shows how concentrated features are across an area, here weighted by the number of jobs at each location.
Which software was used?
ArcGIS for spatial analysis and density modelling, and QGIS for data processing and visualisation.
Can this analysis be applied to other cities?
Yes. The same workflow works for any city with geocoded employment or business data.
Client & Workforce Distribution Mapping Using GIS (Switzerland)
A GIS strategy map for a Swiss cleaning services company, visualising client locations and workforce distribution to optimise service areas and operations.
Understanding the spatial relationship between clients and workforce is critical for operational efficiency and service optimization. In this project, Spatial Nexus developed a comprehensive GIS-based strategy map for a cleaning services company in Switzerland, enabling clear visualization of client locations and employee distribution.
The objective was to transform raw operational data into an intelligent geospatial framework that supports data-driven decision-making and service area optimization.
