Geovis Project Assignment, TMU Geography, SA8905, Fall 2025

By Debbie Verduga

Welcome to my Geo Viz Tutorial!

I love ArcGIS and I love Power BI. Each shines in their own way, which is why I was excited to discover the new ArcGIS integration within Microsoft Power BI. Let’s dive in and explore what it can do!

First off, traditional business intelligence (BI) technologies offer powerful analytical insights but often fall short in providing geospatial context. Conversely, geospatial technologies offer map centric capabilities and advanced geoprocessing, but often lack insights integration.

ArcGIS for Power BI directly integrates spatial analytics with business intelligence without the need of geoprocessing tools or advanced level expertise. This integration has the potential to empower everyday analysts to explore and leverage spatial data without needing to be highly experts in GIS.

This tutorial will demonstrate how to use ArcGIS for Power BI to:

Integrate multiple data sources – using City of Toronto neighbourhood socio-demographics, crime statistics, and gun violence data.
Perform data joins within Power BI – by linking datasets effortlessly without needing complex GIS tools.
Create interactive spatial dashboards – by blending BI insights with mapping capabilities, including thematic maps and hotspot analysis.

You will need TMU Credentials for:

Power BI Online
ArcGIS Online
Data

Dataset	File Type	Source
Neighbourhood Socio-demographic Data	Excel	Simply Analytics (Pre-Aggregated Census Data)
Neighbourhood Crime Rates	SHP File (Boundary)	ArcGIS Online Published by Toronto Police Service
Shooting & Firearm Discharge	SHP File (Point/Location)	ArcGIS Online Published by Toronto Police Service

Power BI Online

Log into https://app.powerbi.com/ using your TMU credentials. To help you out, watch this tutorial to get started with Power BI Online.

ArcGIS Online

Log Into https://www.arcgis.com/ using your TMU credentials. Make sure you explore data available for your analysis. For this tutorial we will be using Toronto Police Service Open data including neighbourhood crime rates and shootings and firearms discharges.

Loading Data

To create a new report in Power BI, you need a set of data to start. For this analysis, we will use neighbourhood level socio-demographic data in an excel format. This data includes total population and variables that are often associated with marginalized communities including low income, unemployment rates, no education and visible minorities rates. This data does not have spatial reference, however, the neighbourhood name and unique identifier is available in the data.

Add Data > Click on New Report > Get Data > Choose Data Source > Upload File > Browse to your file location > Click Next > Select Excel Tab you wish to Import > Select Transform Data

Semantic Model

This will take you to the report’s Semantic Model. Think of a semantic model as the way you transform raw data into a business-ready format for analysis and reporting. The opportunities for manipulating data here are endless!

Using the semantic model you can define relationships with other data, incorporate business logic, perform calculations, summarize, pivot and manipulate data in many ways.

What I love about semantic models is that it remembers every transformation you have made to the data. If you made a mistake, don’t sweat it, come back to the semantic model and undo. It’s that simple.

Once you load data you have two options. You can create a report or create a semantic model. Let’s create a report for now.

Create Report > Name Semantic Model

Report Layout

The report layout functions as a canvas. This is where you add all your visualizations. On the right panel you have Filters, Visualizations and Data.

Visuals are added by clicking on the icons. If you hover over the icon, you can see what they are called. There are default visuals, but you can add more visuals from the microsoft store.

The section below the visual icons helps guide how to populate the visual with your data.

Each visual is configured differently. For example, a chart/graph requires a y-axis and y-axis. To populate these visuals, drag and drop the column from your data table into these fields/values.

Add a Visual

Lets add an interactive visualization that displays the low income rate based on the neighbourhood selected from a list.

From the visualization panel > Add the Card Visual

From the data panel > Drag the variable column into the Fields

By default, Power BI summarizes the statistics in your entire data. Lets create an interactive filter to interact with this statistic based on a selected neighborhood.

From the Visualizations Panel > Add the Slicer Visual > Drag and drop the column that has the neighbourhood name > Filter from the slicer any given neighbourhood.

The slicer now interacts with the Card visual to filter out its respective statistic.

Congrats! We have created our very first interactive visualization! Well done :)

Pro Tip: To validate that calculations and visualizations are correct in Power BI, use excel to manipulate data in the same format and validate that visualizations are correct.

Load Data from ArcGIS Online

To demonstrate the full integration of Power BI and geospatial data, let’s bring data from ESRI’s ArcGIS Online. Authoritative open data is available in this portal and can be directly accessed through Power BI.

Linking Data

When you think about integrating non-spatial data with spatial/location data, you will need to keep in mind that at the very least, you will need common identifiers to be able to link data together. For example, the neighbourhood data has a neighbourhood name and identifier which are also available in the data published by the Toronto Police Service including neighbourhood crime rates and shootings and firearms discharges.

Add ArcGIS for Power BI Visual

Add the ArcGIS for Power BI visual > Log in using your TMU Credentials.

Add Layers

Click on Layers Icon > Switch to ArcGIS Online > Search for Layers by Name > Select Layer > Done

This will add the layer to your map. You can go back and add more layers. You can also add layers from your own ArcGIS Online account.

ArcGIS Navigation Menu

The navigation menu on the left panel of this window allows you to perform the following geospatial functions

Change and style symbology
Change the Base Map
Change Layer Properties
Analysis tab allows you to
- Conduct Drive Time and Buffer
- Add demographic data
- Link/Join Data
- Find Similar Data

For the purposes of this analysis, we will:

Establish a join between our neighbourhood socio-demographic data and the spatial data including crime rates and shootings and firearms discharges
Develop a thematic map of one crime type
Develop a shootings hot spot analysis

Data Cleansing

When linking data, the common neighbourhood IDs from all these different data sources are not in the same format. For example, in my data, the ID is an integer format. However, in the Shooting and Firearms Data, this field is in a text format padded with zeros.

In Power BI, we can create a clean table with neighbourhood information that acts as a neighbourhood dimension table to link data together and manipulate the columns to facilitate data linkages. Let’s create a neighbourhood dimension table.

Create a clean Neighbourhood Table

Open the Semantic Model > Change to Editing Mode > Transform Data > Right Click on Table > Duplicate Neighbourhood Table > Right Click on new table to Rename > Choose Column > Remove all Unwanted Columns > Click on Hood ID Column > Click Transform > Change to Whole Number > Right Click on Column to Rename > Add Custom Column (Formula Below) > Save

Formula = Text.PadStart(Text.From([HOOD_158]), 3, “0”)

The custom column and formula takes the HOOD ID, changes it into a text field and adds padding of zeros. This will match the neighbourhood ID format in the shootings and firearm discharge data. Keep in mind, the only data you can manipulate is the data within your semantic model. You cannot change or manipulate the data sourced from ArcGIS Online.

Relationships

The newly created table in the semantic model needs to be related to the neighbourhood socio-demographic data to make sure that all tables are related to each other.

Establish a Relationship

In the semantic model view > Click Manage Relationships > Add New Relationship > Select From Table and Identifier > Select To Table and Identifier > Establish Cardinality and Cross-Filter Direction to Both > Save > Close

Congrats! We created a clean table that will function as a neighbourhood dimension table to facilitate data joins. We also learned how to establish a relationship with other tables in your semantic model. This comes handy as you can integrate multiple sources of data.

Let’s return to the report and continue building visualizations.

Joining Non-Spatial Data with Spatial Data

Neighbourhood Crime

Now we will join our non-spatial data from our semantic model with our spatial data in ArcGIS Online.

Join Non-Spatial Data with ArcGIS Online Data

Click on the ArcGIS Visual > Analysis Tab > Join Layer > Target Layer > Power BI Data (Drag & Drop Hood ID into the Join Layer Field in the Visualization Pane > Additional Options will now Appear on the Map> Select Join Field > Join Operation Aggregate > Interaction Filter > Create Join > Close

Congrats! We have created a join between the non-spatial data in Power BI and the spatial data in ArcGIS Online.

Change the neighbourhood filter to see this map filter and zoom into the selected neighbourhood.

Thematic Map

Now, let’s create a thematic map with one of the crime rates variables in this dataset.

On the left hand panel of the map:

Click on Layers > Click on Symbology > Active Layer > Select Field Assault Rate 2024 > Change to Colour > Click on Style Options Tab > Change Colour Ramp to another Colour > Change Classification > Close Window.

Congrats! We created a thematic map in Power BI using ArcGIS Online boundary file.

Change the neighbourhood filter to see how the map interacts with the data. Since this is a thematic map, we may not want to filter all the data, instead, we just want to highlight the area of interest.

Click on Analysis > Join Layer > Change the Interaction Behaviour to Highlight instead of Filter> Update Join

Check this again by changing the neighbourhood filter. Now, the map just highlights the neighbourhood instead of filtering it.

Customizing Map Controls

When you have the ArcGIS visual selected, you have the ability to customize your settings using the visualization panel. This controls the map tools available in the report. This can come in handy when setting up a default extent in your map for this report or allowing users to have control over the map.

Shootings and Firearm Discharges

Let’s visualize location data and create a hot spot analysis. To save time, copy and paste the map you created in the step before.

In the new map, add the Shootings and Firearms Data.

Challenge: Practice what you have learned thus far!

Change the base layer to a dark theme.
Add the Shootings and Firearm Discharge Data from ArcGIS Online.
Create a join with this layer and the data in Power BI.
Play around with changing the colour and shape of the symbology.

Hotspot Analysis

Now create a heat map from the Shooting and Firearm Discharges location data.

Click on Layers > Symbology > Change Symbol Type from Location to Heat Map > Style Options > Change Colour Ramp > Change Blur Radius > Close

Congrats! We have created a heat map in Power BI!

This map is dynamic, when you filter the neighbourhood from the list, the hot spot map filters as well.

Customizing your report

It’s time to customize the rest of the report by adding visualizations, incorporating additional maps, adjusting titles and text, changing the canvas background, and bringing in more data to enrich the overall analysis.

I hope you’re just as excited as I am to start using Power BI alongside ArcGIS. By blending these two powerful tools, you can easily bring different data sources together and unlock a blend of BI insights and spatial intelligence—all in one place.

References

Neighbourhood Crime Rates – Toronto Police Service Open Data

Shooting and Firearm Discharges – Toronto Police Service Open Data

Environics Analytics – Simply Analytics

By: Ganesha Loree

Geovis Project Assignment, TMU Geography, SA8905, Fall 2025

INTRODUCTION

`Chicago is considered the most gang-occupied city in the United States, with 150,000 gang-affiliated residents, representing more than 100 gangs. In 2024, 46 gangs and their boundaries across Chicago were mapped by the City of Chicago. Factors about the formation of gangs have been of interest and a topic of research for many years all over the world (Assari et al., 2020), but for the purpose of this project, these factors are going to stem from demographics of Chicago. For instance, Chicago has deep roots within gang history and culture. Not only gangs but violent crimes are also dense. Demographics such as income, education, housing, race, etc., play factors within the neighbourhoods of Chicago and could be part of the cause of gang history.

METHODOLOGY

Step 1: Data Preparation

Chicago Neighbourhood Census Data (2025): Over 200 socioeconomic and demographic data for each neighbourhood was obtained from the Chicago Metropolitan Agency for Planning (CMAP) (Figure 1). In July 2025 their Community Data Snapshot portal released granular insights into population characteristics, income levels, housing, education, and employment metrics across Chicago’s neighbourhoods.

**Figure 1**: *Census data for Chicago, 2024*

Chicago Neighbourhood Boundary Files: official geographic boundaries for Chicago neighbourhoods were downloaded from the City of Chicago’s open data portal (Figure 2). These shapefiles were used to spatially join census data and support geospatial visualization.

**Figure 2**: *Chicago Data Portal – Neighborhood Boundaries*

Chicago Gang Territory Boundaries (2024): Gang territory data from 2024 was sourced from the Chicago Police Department’s GIS portal (Figure 3). These boundaries depict areas of known gang influence and were integrated into the spatial database to support comparative analysis with neighbourhood-level census indicators.

Figure 3a: *2024 Gang Boundaries Data Portal*

Step 2: Technology

Once the data was downloaded, they were applied to software to visualize the data. A combination of technologies was used, ArcGIS Pro and Sketchup (Web). ArcGIS Pro was used to import all boundary files, where neighbourhood census data was joined to Chicago boundary shapefile using unique identifier such as Neighbourhood Name (Figure 4).

**Figure 4:** *ArcGIS Pro Data Join Table*

Gang territory boundary polygons overlaid with neighborhood boundaries to enable spatial intersection and proximity analysis (Figure 5).

**Figure 5**: *Shapefiles of Chicago’s Neighbourhoods and Gangs*

Within ArcGIS Pro, the combined map of both boundaries allowed for analysis of the neighbourhoods with the most gang boundaries. Rough sketch of these neighbourhoods was made by circling the neighbourhoods of a clean map of Chicago, where the bigger circles show the areas with more gang areas and the stars indicate the neighborhoods with no gang boundaries (Figure 6). The CMAP was used to look at the demographics of the neighborhoods with the most area of gangs and compared to the areas with no gang areas (e.g. O’Hare).

**Figure 6**: *Chicago neighborhood outlines with markers*

SketchUp

SketchUp is 3D modeling tool that is used to generate and manipulate 3D models and is often used in architecture and interior design. Using this software for this project was a different purpose of the software; by importing Chicago neighborhoods outline as an image I was able to trace the neighborhoods.

Step 3: Visualization with 3D Extrusions (Sketch Up)

Determined the highest height of the 3D maps models, was based on the total number of neighborhoods (98) and total number of gangs records/areas (46). Determining which neighbourhoods had the most gang boundaries was based on the gang area number which was provided in the Gang Boundary file. The gang with the most area totaled to shape area of 587,893,900m², where the smallest shape area is 217,949m². Similar process was done with neighbourhood area measurements. Neighbourhoods were raised based on the number of gang areas that were present within that neighbourhood (as previously shown in Figure 5). 5’ (feet) is the highest neighbourhood, and 4” (inches) is the lowest neighbourhood where gangs are present, neighbourhoods that do not have gangs are not elevated.

A different approach was applied to the top 3 gangs map model, where the highest remains same in each gang, but are placed in the neighbourhoods that have that gang present. For instance, Gangster Disciples were set at approximately 5 feet (5′ 3/16″ or 1528.8 mm), Black P Stones at almost 4 feet (3′ 7/8″ or 936.6 mm), and Latin Kings at a little over 1 foot (1′ 8 1/4″ or 514.4 mm).

Map Design

Determined what demographic factors were going to be used to compare with gang areas, for example, income, race, and top 3 gangs (Gangster Disciples, Black P Stones, and Latin Kings). Two elements present with the two demographic maps (height and colour), where colour indicates the demographic factor and the height represents the gang presence (Figure 7).

**Figure 7**: *3D map models of Chicago gangs based on Race and Income*

LIMITATIONS

There was limited information available about the gang areas, which only consisted of gang name, shape area, and length measurements. In terms of SketchUp’s limitations, the free web version as some restrictions, had to manually draw the outline of Chicago neighbourhoods which was time consuming. In addition, SketchUp scale system was complex and was not consistent between maps. To address tis, each corner of the map was measured with the Tape Measure Tool to ensure uniformity. Lastly, when the final product was viewed in augmented reality (AR), the map quality was limited such as the neighbourhood outlines were gone, and the only parts that were visible were the colour parts of the models.

CONCLUSION (Key Insights)

The most visual pattern shown from the race map is the areas with more gang activity have a large population of African Americans (Figure 7). For the income map, indicated in green, more gang areas have lower income whereas the areas with higher income do not have gangs in those neighborhoods. Based on the top three gangs, Gangster Disciples have the most gang boundaries across Chicago neighborhoods (Figure 8). Gangster Disciples takes up 33.6% of the area in km², founded in 1964 in Englewood.

**Figure 8**: *3D map of the top 3 gangs in Chicago, 2024*

FINAL PRODUCT

The final product, is user interactive through a QR code that allows viewers to look at the map models using augmented reality (AR) just by pointing your mobile device camera at the QR code below.

Being aware that the quality of the AR has its limits, the SketchUp map models can be viewed using the Geovis Map Models button below.

Geovis map models

Reference

Assari, S., Boyce, S., Caldwell, C. H., Bazargan, M., & Mincy, R. (2020). Family income and gang presence in the neighborhood: Diminished returns of black families. Urban Science, 4(2), 29.

Tag: ArcGIS

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