3D Approach to Visualizing Crime on Campus: Laser-Cut Acrylic Hexbins

By: Lindi Jahiu

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2021

INTRODUCTION

Crime on campus has long been at the forefront of discussion regarding safety of community members occupying the space. Despite efforts to mitigate the issue—vis-à-vis increased surveillance cameras, increased hiring of security personnel, etc.—, it continues to persist on X University’s campus. In an effort to quantify this phenomenon, the university’s website collates each security incident that takes place on campus and details its location, time (reported and occurrence), and crime type, and makes it readily available for the public to view through web browser or email notifications. This effort to collate security incidents can be seen as a way for the university to first and foremost, quickly notify students of potential harm, but also as a means to understanding where incidents may be clustering. The latter is to be explored in the subsequent geo-visualization project which attempts to visualize three years worth of security incidents data, through the creation of a 3D laser-cut acrylic hexbin model. Hexbinning refers to the process of aggregating point data into a predefined hexagon that represents a given area, in this case, the vertex-to-vertex measurement is 200 metres. By proxy of creating a 3D model, it is hoped that the tangibility, interchangeability, and gamified aspects of the project will effectively re-conceptualize the phenomena to the user, and in-turn, stress the importance of the issue at hand. 

DATA AND METHODS

The data collection and methodology can be divided into two main parts: 2D mapping and 3D modelling. For the 2D version, security incidents from July 2nd, 2018 to October 15th, 2021 were manually scraped from the university’s website (https://www.ryerson.ca/community-safety-security/security-incidents/list-of-security-incidents/) and parsed into columns necessary for geocoding purposes (see Figure 1). Once all the data was placed into the excel file, they would be converted into a .csv file and imported into the ArcGIS Pro environment. Once there, one simply right clicks on the .csv and clicks “Geocode Table”, and follows the prompts for inputting the data necessary for the process (see inputs in Figure 2). Once ran, the geocoding process showed a 100% match, meaning there was no need for any alterations, and now shows a layer displaying the spatial distribution of every security incident (n = 455) (see Figure 3). To contextualize these points, a base map of the streets in-and-around the campus was extracted from the “Road Network File 2016 Census” from Scholars GeoPortal using the “Split Line Features” tool (see output in Figure 3). 

Figure 1. Snippet of spreadsheet containing location, postal code, city, incident date, time of incident, and crime type, for each of the security incidents.

Figure 2. Inputs for the Geocoding table, which corresponds directly to the values seen in Figure 1.

Figure 3. Base map of streets in-and-around X University’s campus. Note that the geo-coded security incidents were not exported to .SVG – only visible here for demonstration purposes.

To aggregate these points into hexbins, a certain series of steps had to be followed. First, a hexagonal tessellation layer was produced using the “Generate Tessellation” tool, with the security incidents .shp serving as the extent (see snippet of inputs in Figure 4 and output in Figure 5). Second, the “Summarize Within” tool was used to count the number of security incidents that fell within a particular polygon (see snippet of inputs in Figure 6 and output in Figure 7). Lastly, the classification method applied to the symbology (i.e. hexbins) was “Natural Breaks”, with a total of 5 classes (see Figure 7). Now that the two necessary layers have been created, namely, the campus base map (see Figure 3 – base map along with scale bar and north arrow) and tessellation layer (see Figure 5 – hexagons only), they would both be exported as separate images to .SVG format – a format compatible with the laser cutter. The hexbin layer that was classified will simply serve as a reference point for the 3D model, and was not exported to .SVG (see Figure 7).

Figure 4. Snippet of input when using the “Generate Tessellation” geoprocessing tool. Note that these were not the exact inputs, spatial reference left blank merely to allow the viewer to see what options were available.

Figure 5. Snippet of output when using the “Generate Tessellation” geoprocessing tool. Note that the geo-coded security incidents were not exported to .SVG – only visible here for demonstration purposes.

Figure 6. Snippet of input when using the “Summarize Within” geoprocessing tool.

Figure 7. Snippet of output when using the “Summarize Within” geoprocessing tool. Note that this image was not exported to .SVG but merely serves as a guide for the physical model.

When the project idea was first conceived, it was paramount that I familiarized myself with the resources available and necessary for this project. To do so, I applied for membership to the Library’s Collaboratory research space for graduate students and faculty members (https://library.ryerson.ca/collab/ – many thanks to them for making this such a pleasurable experience). Once accepted, I was invited to an orientation, followed by two virtual consultations with the Research Technology Officer, Dr. Jimmy Tran. Once we fleshed out the idea through discussion, I was invited to the Collaboratory to partake in mediated appointments. Once in the space, the aforementioned .SVG files were opened in an image editing program where various aspects of the .SVG were segmented into either Red, Green, or Blue, in order for the laser cutter to distinguish different features. Furthermore, the tessellation layer was altered to now include a 5mm (diameter) circle in the centre of each hexagon to allow for the eventual insertion of magnets. The base map would be etched onto an 11×8.5 sheet of clear acrylic (3mm thick), whereas the hexagons would be cut-out into individual pieces at a size of 1.83in vertex-to-vertex. Atop of this, a black 11×8.5 sheet of black acrylic would be cut-out to serve as the background for the clear base map (allowing for increased contrast to accentuate finer details). Once in hand, the hexagons would be fixed with 5x3mm magnets (into the aforementioned circles) to allow for seamless stacking between pieces. Stacks of hexagons (1 to 5) would represent the five classes in the 2D map, but with height now replacing the graduated colour schema (see Figure 7 and Figure 9 – although the varying translucency of the clear hexagons is also quite evident and communicates the classes as well). The completed 3D model is captured in Figure 8, along with the legend in Figure 9 that was printed out and is to always be presented in tandem with the model. The legend was not etched into the base map so as to allow it to be used for other projects that do not use the same classification schema, and in-case I had changed my mind about a detail at some point.

Figure 8. 3D Laser-Cut Acrylic Hexbin Model depicting three-years worth of security incidents on campus. Multiple angles provided.

Figure 9. Legend which corresponds the physical model displayed in Figure 8. Physical version has been created as well and will be shown in presentation.

FUTURE RESEARCH DIRECTIONS AND LIMITATIONS

The geo-visualization project at-hand serves as a foundation for a multitude of future research avenues, such as: exploring other 3D modalities to represent human geography phenomenon; as a learning tool for those not privy to cartography; and as a tool to collect further data regarding perceived and experienced areas of crime. All of which expand on the aspects tangibility, interchangeability, and gamification harped on in the project at-hand. With the latter point, imagine a situation where a booth is set up on campus and one were to simply ask “using these hexagon pieces, tell us where you feel the most security incidents on campus would occur.” The answers provided would be invaluable, as they would yield great insight into what areas of campus community members feel are most unsafe, and what factors may be contributing to it (e.g. built environment features such as poor lighting, lack of cameras, narrowness, etc.), resulting in a synthesis between the qualitative and quantitative. Or on the point of interchangeability, if someone wanted to explore the distribution of trees on campus for instance, they could very well laser-cut their own hexbins out of green acrylic at their own desired size (e.g. 100m), and simply use the same base map.

Despite the fairly robust nature of the project, some limitations became apparent, more specifically: issues with the way a few security incident’s data were collected and displayed on the university’s website (e.g. non-existent street names, non-existent intersections, missing street suffixes, etc.); an issue where the exportation of a layer to .SVG resulted in the creation of repeated overlapping of the same images that had to be deleted before laser cutting; and lastly, future iterations may consider exaggerating finer features (e.g. street names) to make the physical model even more legible.

Geovisualization of Crime in the City of Toronto Using Time-Series Animation Heat Map in ARCGIS PRO

Hetty Fu

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2019

Background/Introduction

The City of Toronto Police Services have been keeping track of and stores historical crime information by location and time across the City of Toronto since 2014. This data is now downloadable in Excel and spatial shapefiles by the public and can be used to help forecast future crime locations and time. I have decided to use a set of data from the Police Services Data Portal to create a time series map to show crime density throughout the years 2014 to 2018. The data I have decided to work with are auto-theft, break and enter, robbery, theft and assault. The main idea of the video map I want to display is to show multiple heat density maps across month long intervals between 2014 to 2018 in the City of Toronto and focus on downtown Toronto as most crimes happen within the heart of Toronto.

The end result is an animation time-series map that shows density heat map snapshots during the 4-year period, 3-month interval at a time. Examples of my post are shown at the end of this blog post under Heat Map Videos.

Dataset

All datasets were downloaded through the Toronto Police Services Data Portal which is accessible to the public.

The data that was used to create my maps are:

  1. Assault
  2. Auto Theft
  3. Robbery
  4. Break and Enter
  5. Theft

Process Required to Generate Time-Series Animation Heat Maps

Step 1:  Create an additional field to store the date interval in ArcGis Pro.

Add the shapefile downloaded from the Toronto Police Services Portal intoArcGIS Pro.

First create a new field under View Table and then click on Add.             

To get only the date, we use the Calculate Field in the Geoprocessing tools with the formula

date2=!occurrence![:10]  

where Occurrence is the existing text field that contains the 10 digit date: YYYY-MM-DD. This removes the time of day which is unnecessary for our analysis.

Step 2: Create a layer using the new date field created.

Go into properties in the edited layer. Under the time tab, place in the new date field created from Step 1 and enter in the time extent of the dataset. In this case, it will be from 2014-01-01 to 2018-12-31 as the data is between 2014 to 2018.

Step 3: Create Symbology as Heat Map

Go into the Symbology properties for the edited layer and select heat map under the drop down menu. Select 80 as its radius which will show the size of the density concentration in a heat map. Choose a color scheme and set the method as Dynamic. The method used will show how each color in the scheme relates to a density value. In a Dynamic setting versus and constant, the density is recalculated each time the map scale or map extent changes to reflect only those features that are currently in view. The Dynamic method is useful to view the distribution of data in a particular area, but is not valid for comparing different areas across a map (ArcGIS Pro Help Online).

Step 4: Convert Map to 3D global scene.

Go to View tab on the top and select convert to global scene. This will allow the user to create a 3D map feature when showing their animated heat map.

Step 5: Creating the 3D look.

Once a 3D scene is set, press and hold the middle mouse button and drag it down or up to create a 3D effect.

Step 6: Setting the time-series map.

Under the Time tab, set the start time and end time to create the 3 month interval snapshot. Ensure that “Use Time Span” is checked and the Start and End date is set between 2014 and 2018. See the image below for settings.

Step 7: Create a time Slider Steps for Animation Purposes

Under Animation tab, select the appropriate “Append Time” (the transition time between each frame). Usually 1 second is good enough, anything higher will be too slow. Make sure to check off maintain speed and append front before Importing the time Slider Steps. See below image.

Step 8: Editing additional cosmetics onto the animation.

Once the animation is created, you may add any additional layers to the frames such as Titles, Time Bar and Paragraphs.

There is a drop down section in the Animation tab that will allow you to add these cosmetic layers onto the frame.

Animation Timeline by frames will look like this below.

Step 9: Exporting to Video

There are many types of exports the user can choose to create. Such as Youtube, Vimeo, Twitter, Instagram, HD1080 and Gif. See below image for the settings to export the create animation video. You can also choose the number of frames per second, as this is a time-series snapshot no more than 30 frames per second is needed. Choose a place where you would like to export the video and lastly, click on Export.

Conclusion/Recommendation/Limitation

As this was one of my first-time using ArcGIS Pro software, I find it very intuitive to learn as all the functions were easy to find and ready to use. I got lucky in finding a dataset that I didn’t have to format too much as the main fields I required were already there and the only thing required was editing the date format. The number of data in the dataset was sufficient for me to create a time series map that shows enough data across the city of Toronto spanning 3 months at a time. If there was less data, I would have to increase my time span. The 3D scene on ArcGIS Pro is very slow and created a lot of problems for me when trying to load my video onto set time frames. As a result of the high-quality 3D setting, I decided to use, it took couple of hours to render my video through the export tool. As the ArcGIS Pro software wasn’t made to create videos, I felt that there was lack of user video modification tools.

Heat Map Videos Export

  1. Theft in Downtown Toronto between 2014-2018. A Time-Series Heat Map Animation using a 3 month Interval.
  2. Robbery in Downtown Toronto between 2014-2018. A Time-Series Heat Map Animation using a 3 month Interval.
  3. Break and Enter in Downtown Toronto between 2014-2018. A Time-Series Heat Map Animation using a 3 month Interval.
  4. Auto Theft across the City of Toronto between 2014-2018. A Time-Series Heat Map Animation using a 3 month Interval.
  5. Assault across the City of Toronto between 2014-2018. A Time-Series Heat Map Animation using a 3 month Interval.

Toronto Theft: A Neighbourhood Investigation

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2019

By: Julia DiMartella-Orsi

Introduction:

ESRI’s creation of the Story Map changed the way we could visualize data. Not only did it allow for a broader audience to interact and create their own maps due to its easy to use design, it also contained many new amazing functions, templates, and themes. Users can personalize their story by adding in their own images, text, videos, and map layers by creating their own free ArcGIS Online account. Popular templates include Map Series, Tour, Journal, and Cascade.

Get started making your own Story Map here: http://storymaps-classicqa.arcgis.com/en/app-list/

Creating Your Story Map:

Once you have selected the template you want to use the choice is up to you. By clicking the “+” symbol you can choose to include text, media sources such as a videos, a new title page, or immersive content such as a web map.

ESRI also designed Story Maps to link to outside content and various social media sites such as Flickr and Unsplash. ‘Link to Content’ is also extremely useful as it allows users to add photos and videos found on the internet directly to their story map by copying and pasting their link.

To add interactive web maps into your story map users can link map layers from their ArcGIS Online account. Layers can be created in ArcGIS Online, but also in ArcMap where layers are exported as a zip file and imported onto your ArcGIS Online base map. Map layers can also be found online using the ‘add layer from the web’ or ‘search for layers’ options.  The layers that appear are based on the type of ArcGIS Online account you have created. Enterprise accounts contain additional layers provided by your organization, however ESRI also has free downloadable layers available for users without an organization.

Users also have the option to make their story maps public by clicking the globe icon, or private for their own personal use by clicking the lock icon. To save your story map select the floppy disk icon. Your saved map will appear under ‘My Content’ in your ArcGIS Online account.

My Story and Creating Web Maps:

Over the last few years, theft in Toronto has been increasing at a rapid rate. According to the Toronto Police Service, Toronto experienced a total of 5430 thefts between 2014-2018. However, these are only those that have been reported and documented by police. In order to analyze the distribution of theft across the city, the Toronto Police created a point dataset that summarized when and where each theft took place. Additional datasets were also created for prominent types of theft such as bicycle and auto theft.

To compare the number and types of theft in each Toronto neighbourhood I decided to create a story map using the Cascade template. This created a scrolling narrative that would allow viewers to observe the data in a clear, unique way. The reason why I chose to use a story map was due to the number of layers I wanted to compare, as well as use the ‘swipe tool’ to easily compare each neighbourhood. Therefore, I created a series of choropleth maps based on the 2014-2018 theft/crime data from the Toronto Police Open Data Portal.

The following steps were used to create each web map used in my Story Map:

Step 1: Download the point data and add the layer into ArcMap.

Step 2: Use the ‘spatial join’ analysis tool and select your neighbourhood boundary file as the target layer and the theft point data as the join feature. Make sure to select ‘join one to one’. This will produce a new layer with a ‘count’ field that counts the number of thefts in each neighbourhood – each neighbourhood is given a count.

Step 3: In order to produce accurate results, you must normalize your data. To do so add a new field into your attribute table (same layer with the count field) titled ‘Area’, and right click to select ‘calculate geometry’. Change the property to ‘area’ and choose the units you wish to use. Click ‘ok’ and the results will populate your new field.

Step 5: Export the layer and save it as a compressed zip folder. Import the data into ArcGIS Online by clicking the “Add” tab.

Step 6: Once you import your layer you are given a variety of styles to choose from. Select the one you like best (ex: choropleth) as well as the field you wish to map – in this case select ‘count’. To normalize ‘count’ select the ‘divided by’ dropdown and choose your ‘Area’ field. Change the colour of your map to your preference by clicking ‘symbols’.

Step 7: Save your layer to and select the tags that relate to your topic. The layer will now appear in ‘My Content’ where it can be added to your Story Map.

Step 8: To compare each layer add both layers you wish to compare to your story map by using the “+” symbol. Once you have done so, choose the transition type (ex: horizontal swipe) you want to use by clicking on the arrow below. The transition will take place as the user scrolls through your story map.

My Story Map titled “Toronto Theft: A Neighbourhood Investigation” can be viewed here:

https://arcg.is/uiemr