Master of Spatial Analysis – The Blog – Page 2 – Course work by MSA students, fresh from downtown Toronto to your screen.

Drone Package Deployment Tutorial / Animation

Anugraha Udas

SA8905 – Cartography & Visualization
@RyersonGeo

Introduction

Automation’s prevalence in society is becoming normalized as corporations have begun noticing its benefits and are now utilizing artificial intelligence to streamline everyday processes. Previously, this may have included something as basic as organizing customer and product information, however, in the last decade, the automation of delivery and transportation has exponentially grown, and a utopian future of drone deliveries may soon become a reality. The purpose of this visualization project is to convey what automated drone deliveries may resemble in a small city and what types of obstacles they may face as a result of their deployment. A step-by-step process will also be provided so that users can learn how to create a 3D visualization of cities, import 3D objects into ArcGIS Pro, convert point data into 3D visualizations, and finally animate a drone flying through a city. This is extremely useful as 3D visualization provides a different perspective that allows GIS users to perceive study areas from the ground level instead of the conventional birds-eye view.

Area of Study

The focus area for this pilot study is Niagara Falls in Ontario, Canada. The city of Niagara Falls was chosen due to its characteristics of being a smaller city but nonetheless still containing buildings over 120 meters in height. These buildings sizes provide a perfect obstruction for simulating drone flights as Transport Canada has set a maximum altitude limit of 120 meters for safety reasons. Niagara Falls also contains a good distribution of Canada Post locations that will be used as potential drone deployment centres for the package deliveries. Additionally, another hypothetical scenario where all drones deploy from one large building will be visualized. In this instance, London’s gherkin will be utilized as a potential drone-hive (hypothetically owned by Amazon) that drones can deploy from (See https://youtu.be/mzhvR4wm__M). Due to the nature of this project being a pilot study, this method be further expanded in the future to larger dense areas, however, a computer with over 16GB of RAM and a minimum of 8GB of video memory is highly recommended for video rendering purposes. In the video below, we can see the city of Niagara Falls rendered in ArcPro with the gherkin represented in a blue cone shape, similarly, the Canada Post buildings are also represented with a dark blue colour.

City of Niagara Falls (Rendered in ArcPro)

Data

The data for this project was derived from numerous sources as a variety of file types were required. Regarding data directly relating to the city of Niagara Falls – Cellular Towers, Street Lights, Roads, Property parcel lines, Building Footprints and the Niagara Falls Municipal Boundary Shapefiles were all obtained from Niagara Open data and imported into ArcPro. Similarly, the Canada Post Locations Shapefile was derived from Scholar’s Geoportal. In terms of the 3D objects – London’s Gherkin, was obtained from TurboSquid in and the helipad was obtained from CGTrader in the form of DAE files. The Gherkin was chosen because it serves as a hypothetic hive building that can be employed in cities by corporations such as Amazon. Regarding the helipad 3D model, it will be distributed in numerous neighbourhoods around Niagara Falls as a drop-off zones for the drones to deliver packages. In a hypothetical scenario, people would be alerted on their phones as to when their package is securely arriving, and they would visit the loading zone to pick up their package. It should be noted that all files were copyright-free and allowed for personal use.

Process (Step by step)

Importing Files

First, access the Niagara Open Data website and download all the aforementioned files in the search datasets box. Ensure that the files are downloaded in SHP format for recognition in ArcPro (Names are listed at the end of this blog). Next, go on TurboSquid and search for the Gherkin and make sure that the price drop down has a minimum and maximum value of $0 (Figure 1). Additionally, search for ‘Simple helipad free 3D model’ on CGtrader. Ensure that these files are downloaded in DAE format for recognition in ArcPro. Once all files are downloaded open ArcPro and import the Shape files (via Add Data) to first conduct some basic analysis.

Basic GIS Analysis

First, double click on the symbology box for each imported layer, and a symbology dialog should open on the right-hand side of the screen. Click on the symbol box and assign each layer with a distinct yet subtle colour. Once this is finished, select the Canada Post Locations layer, and go to the analysis tab and select the buffer icon to create a buffer around the Canada Post Locations. Input features – The Canada Post Locations. Provide a file location and name in the output feature class and enter a value of 5 kilometres for distance and dissolve the buffers (Figure 2). The reason why 5km was chosen is that regular consumer drones have a battery that can last up to ten kilometres (or 30 min flight time), thus traveling to the parcel destination and back would use up this allotted flight time.

Once this buffer is created the symbology is adjusted to a gradient fill within the layer tab of the symbol. This is to show the groupings of clusters and visualize furthering distance from the Canada Post Locations. In this project we are assuming that the Canada Post Locations are where the drones are deploying from, thus this buffer shows the extent of the drones from the location (Figure 3). As we can see, most residential areas are covered by the drone package service. Next, we are going to give the Canada post buildings a distinct colour from the other buildings. Go to ‘Select by Location’ in the ‘Map’ tab and click ‘Select by Location’. In this dialog box, an intersection relationship is created where the input features are the buildings, and the selecting features is the Canada Post location point data. Hit okay, and now create a new layer from the selection and name it Canada Post buildings. Assign a distinct colour to separate the Canada Post buildings from the rest of the buildings.

3D Visualization – Buildings

Now we are going to extrude our buildings in terms of their height in feet. Click on the View tab in ArcPro and click on the Convert to local scene tab. This process essentially creates a 3D visual of your current map. Next you will notice that all of the layers are under 2D view, once we adjust the settings of the layers, we will drag these layers to the 3D layers section. To extrude the buildings, click on the layer and the appearance tab should come up under the feature layer. Click on the Type diagram drop down and select ‘Max Height’. Thereafter, select the field and choose ‘SHAPE_leng’ as this is the vertical height of the buildings and select feet as the unit. Give ArcPro some time and it should automatically move your building’s layer from the 2D to 3D layers section. Perform this same process with the Canada Post Buildings layer.

Now you should have a 3D view of the city of Niagara Falls. Feel free to move around with the small circle on the bottom left of the display page (Figure 4). You can even click the up arrow to show full control and move around the city. Furthermore, can also add shadows to the buildings by right clicking the map 3D layers tab and selecting ‘Display shadows in 3D’ under Illumination.

Converting Point Data into 3D Objects

In this step, we are going to convert our point data into 3D objects to visualize obstructions such as lamp posts and cell phone towers. First click the Street Lights symbol under 2D layers and the symbology pane should open up on the right side of Arc Pro. Click the current symbol box beside Symbol and under the layer’s icon change the type from ‘Shape Marker’ to 3D model marker (Figure 5).

Next, click style, search for ‘street-light’, and choose the overhanging streetlight. Drag the Street Light layer from the 2D layer to the 3D layer. Finally, right-click on the layer and navigate to display under properties. Enable ‘Display 3D symbols in real-world units’ and now the streetlamp point data should be replaced by 3D overhanging streetlights. Repeat this same process for the cellphone tower locations but use a different model.

Importing 3D objects & Texturing

Finally, we are going to import the 3D DAE helipad and tower files, place them in our local scene and apply textures from JPG files. First, go on the view tab, click on Catalog Pane and a Catalog should show up on the right side of the viewer. Expand the Databases folder and your saved project should show up as a GDB. Right-click on the GDB and create a new feature class. Name it ‘Amazon Tower’ and change the type from polygon to 3D object and click finish. You should notice that under Drawing Order there should be a new 3D layer with the ‘Amazon Tower’ file name. Select the layer, go on the edit tab and click create to open up the ‘Create Features’ dialog on the right side of the display panel (Figure 6). Click on the Model File tab, click the blue arrow and finally, click the + button. Navigate to your DAE file location, select it and now your model should show up in the view pane and it will allow you to place it on a certain spot. For our purposes, we’ll reduce the height to 30 feet and adjust the Z position to -40 to get rid of the square base under the tower. Click on the location of where you want to place the tower, close the create feature box, apply the multi-patch tool and clear the selection. Finally, to texture the tower, select the tower 3D object, click on the edit tab and this time hit modify. Under the new modify features pane select multi patch features under reshape. Now go on to Google and find a glass building texture JPG file that you like. Click load texture, choose the file, check the ‘Apply to all’ box and click apply. Now the Amazon tower should have the texture applied on it (Figure 7).

Animation

Finally, now that all of the obstructions are created, we are going to animate a drone flying through the city. Navigate to the animation tab on the top pane and click on timeline. This is where individual keyframes will be combined for the purpose of creating a drone package delivery. Navigate your view so that it is resting on a Canada Post Building and you have your desired view. Click on ‘Create first key frame’ to create your first view, next click up on the ‘full control view’ so that the drone flies up in elevation, and click the + to designate this as a new keyframe. Ensure that the height does not exceed 120 meters as this is the maximum altitude for drones, provided by Transport Canada (Bottom left box). Next, click and drag the hand on the viewer to move forward and back and click + for a new keyframe. Repeat this process and navigate the proposed drone to a helipad (Figure 8). Finally, press the ‘Move down’ button to land the done on the helipad and create a new key frame. Congratulations, you have created your first animation in ArcPro!

Discussion

Through the process of extruding buildings, maintaining a height less than 120 meters, adding in proposed landing spaces, and turning point data into real-world 3D objects we can visualize many obstructions that drones may face if drone delivery were to be implemented in the city of Niagara Falls. Although this is a basic example, creating an animation of a drone flying through certain neighbourhoods will allow analysts to determine which areas are problematic for autonomous flying and which paths would provide a safer option. Regarding the animation portion, there are two possible scenarios that have been created. First, is a drone deployment from the aforementioned Canada Post Locations. This scenario envisions Niagara Falls as having drone package deployment set out directly from their locations. This option would cover a larger area of Niagara Falls as seen through the buffer, however, having multiple locations may be hard to get funding for. Also, people may not want to live close to a Canada Post due to the noise pollution that comes from drones.

Scenario 1. Canada Post Delivery

The second scenario is to utilize a central building that drones can pickup packages from. This is exemplified as the hive delivery building as seen below. In sharp contrast to option 1, a central location may not be able to reach rural areas of Niagara Falls due to the distance limitations of current drones. However, two major benefits are that all drone deliveries could come from a central location and less noise pollution would occur as a result of this.

Scenario 2. Single HIVE Building

Conclusions & Future Research

Overall, it is evident that drone package deliveries are completely possible within the city of Niagara Falls. Through 3D visualizations in ArcPro, we are able to place simple obstructions such as conventional street lights and cell phone towers within the roads. Through this analysis and animation it is evident that they may not pose an issue to package delivery drones when incorporating communal landing zones. For future studies, this research can be furthered by incorporating more obstructions into the map; such as electricity towers, wiring, and trees. Likewise, future studies can also incorporate the fundamentals of drone weight capacity in relation to how far they can travel and overall speed of deliveries. In doing so, the feasibility of drone package deployment can be better assessed and hopefully implemented in future smart cities.

References

https://www.dji.com/ca/phantom-4/info

https://youtu.be/mzhvR4wm__M

3D Files

Gerkin Model DAE File https://www.turbosquid.com/3d-models/free-30-st-mary-axe-3d-model/991165

Simple Helipad DAE File – https://cgtrader.com/items/212615/download-page

Shape Files

Postal Outlet Points (2020) – Scholar’s GeoPortal

Niagara Falls Building Footprints (2010) – Niagara Open Data

Road Segments (2021) – Niagara Open Data

Niagara Falls Cellular Tower Locations (2021) – Niagara Open Data

Street Lighting Pilot Project (2021) – Niagara Open Data

Niagara Falls Municipal Boundary (2021) – Niagara Open Data

Niagara Falls Property Parcels (2021) – Niagara Open Data

Assessing Speed Camera Effects on Collisions in Toronto

Link to project: https://ryerson.maps.arcgis.com/apps/dashboards/e09127998c21447ea85ce713c1502fe4
Author: Steven (Shucheng) Wang
Geovis Project Assignment @RyersonGeo, SA8905, Fall 2021

Introduction

In 2017, 50 Automatic Speed Enforcement (ASE) cameras were installed throughout Toronto. These cameras work by taking pictures of vehicles which are speeding, and then issuing a fine to the owner of the vehicle. 2 Cameras are allocated to each ward located mainly near school zones for a total of 50, which will eventually be rotated out for a different set of 50 in different locations. This strategy is meant to reduce collisions by having people slow down in areas where the ASE cameras are present.

To visualize whether the installation of these cameras has made a difference on collisions in Toronto, I decided to use ArcGIS Dashboards. ArcGIS Dashboards is a tool that presents spatial data and associated statistics in an interactive format, allowing the user to get the answers to questions that they want.

In order to put this together, I collected collision data from the Toronto Police Public Safety Data Portal which includes data on collisions throughout Toronto from 2006 until 2021. I also collected data on the ASE locations from the Ontario Open Data Portal, and opened both datasets in ArcGIS Online to edit their symbology before adding it to a dashboard.

FIgure 2: Preparing the data for use in the dashboard

Now that the map was ready, I started to configure the actual dashboard. The main elements that I considered essential to include were:
• A filter system, to allow users to filter collisions under certain conditions
• A pie chart, to allow users to visualize the percentage of each type of collision depending on their filters
• A line or bar graph to allow users to see the distribution of collisions temporally.
These can all be easily added to a blank dashboard and configured using the “+” button on the ArcGIS Dashboards top header. The final dashboard with all the previously mentioned elements and the map frame can be seen below:

Dashboard Elements and Functions

The first element we will be looking at is the side panel on the left, which contains the date selector as well as multiple category selectors for different attributes. Each one opens an accordion-style menu when clicked, displaying all available filters for that particular category. These filters can be toggled on or off, and the map frame in the centre will reflect any filters made.

Figure 4: Visibility selector with rain toggled

The next element is the serial chart at the bottom of the dashboard, which contains two graphs stacked on top of each other. The first one is a line graph of the collisions by date all the way from 2006 until now, and the second one is a histogram of the collisions per hour based on a 24-hour clock. Both graphs contain a time slider at the top which can be used to zoom in and look at a particular time period in detail. However, the time slider is purely for viewing purposes and will not affect the map.

Specific time periods can also be selected by clicking on the graph and dragging your mouse over them, or by holding CTRL and clicking the time periods as well. For example, if you only wanted to see collisions from 2017 and onwards, you could click and drag your mouse over the part of the line graph where 2017 starts all the way until the far right side. Unlike the time slider, selecting time periods this way will reflect on the map frame.

The final elements are the legend and the pie chart to the right of the map frame. The legend displays the categorization for each data point, like which ASE camera is currently active vs. planned, or which collisions resulted in fatalities vs. injuries. The pie chart is stacked on top of the legend and displays the distribution of collision type. Similar to the serial charts, the pie chart will adjust to fit the map extent and the filters chosen. However, the legend is static and will not change regardless of filters or map extent.

Limitations & Conclusion

While a dashboard like this can be convenient in many ways, there are some limitations. For example, for the serial graphs there is no indication in the UI that time periods can be selected at all. I only found out about the function when I accidentally clicked on it; before, I had assumed that the time slider would provide that function and was confused why the data points on the map did not change when I adjusted the time slider. Additionally, it is much more difficult to see when time periods have been selected on light mode than on dark mode, which is why I set this dashboard to dark mode.

Another limitation is that there is no real way to conduct spatial analysis beyond the functions outlined earlier. Common tools like creating buffers or finding intersections that would be present in ArcMap/ArcGIS Pro/QGIS are nowhere to be found. You could do these analyses in those programs, create a layer from said analyses, and then import it into a webmap as a layer before adding it to a dashboard, but that would require you to rework the entire dashboard.

Overall, dashboards are a convenient way of allowing users who aren’t familiar with GIS to manipulate and visualize spatial data. It can be a great way to simplify data and create a neat tool that can identify trends or statistics at a glance. However, it is important to note that due to its limitations, its utility will depend greatly on your use case.

Under Construction Commercial Real Estate in Toronto Market

GeoVis Project @RyersonGeo, SA8905, Fall 2021, Mirza Ammar Shahid

Introduction

Commercial real estate is crucial part of the economy and is a key indicator of a region’s economic health. In the project different types of Under constriction projects within the Toronto market will be assessed. Projects that are under construction or are proposed to be completed within the next few years will be visualized. Some property types that will be looked at are, hospitality, office, industrial, retail, sports and entertainment etc. The distribution of each property type within the regions will be displayed. To determine the proportional distribution within each region by property type. Software that will be used is Tableau to create a visualization of the data which will be interactive to explore different data filters.

Data

The data for the project was obtained from the Costar group’s database. The data used was exported using all properties within the submarket of Toronto (York region, Durham region, Peel Region, Halton region). Under construction or proposed properties above the size of 7000 sqft were exported to be used for the analysis. Property name, address, submarket, size, longitude, latitude and the year built were some of the attributes exported for each property project.

Method

Once data was filtered and exported from the source, the data was inserted into Tableau as an excel file.
The latitude and longitude were placed in rows and columns in order to create a map in tableau for visualization.
Density of mark was used to show the density and a filter was applied for property type.
Second sheet was created with same parameters but instead of density circle marks were used to identify locations of each individual project (Under Construction Projects).
Third sheet was created with property type on x axis and proportion of each in each region in y axis. To show the proportions of each property type by region.
The three worksheets were used to compile an interactive dashboard for optimal visualization of the data.

Results

Density Map Showing Industrial Property type

All Under construction project locations

The results are quite intriguing as to where certain property type constriction dominant over the rest. Flex is greatest in Peel region, Health care in Toronto, Hospitality in Halton, Industrial in Peel, Multifamily in Toronto, Office in downtown Toronto, retail in York region, specialty in York region and sports and entertainment in Durham with new casino opening in Ajax.

The final dashboard can be seen below, however due to sharing restrictions, the dashboard can only be accessed if you have a Tableau account.

Conclusion

In conclusion, using under construction commercial real estate dashboard can have positive impact on multiple entities within the sector. Developers can use such geo visualizations to monitor ongoing projects and find new projects within opportunity zones. Brokerages can use this to find new leads, potential listings and manage exiting listings. Governments of all three levels, municipal, provincial and federal can use these dashboard to monitor health conditions of their constituency and make insightful policy changes based on facts.

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.

Time-Lapse of City of Toronto Precipitation and Beach E.Coli Count- July 2018

Kezia Weed- SA8905 Geovis Project, Fall 2021

Introduction

Toronto’s beaches are an incredible feature of the city, popular throughout warm months for water activities of, among other things, swimming, paddle boarding, and boating. While taking a plunge into Lake Ontario is a great way for residents to cool off, it is always important to be aware of the current water quality conditions. During the summer, the City of Toronto tests beaches for Escherichia coli (i.e., E.Coli) counts daily as a public health measure, posting results available on site and online. What many residents are unaware of, is that E.coli is a bacteria that lives naturally in the guts of warm blooded animals; only high concentrations of E.coli at beaches pose a danger of infections for swimmers. In Toronto, beaches are posted as unsafe to swim when E.Coli counts exceed 100 fecal coliforms per 100ml.

Water quality can be impacted by numerous factors including legacy contaminants (i.e., lead), industrial activity (i.e., direct effluent discharge), and by urban stormwater and sewage water. In this visualization, the purpose is to examine the impact of precipitation on fecal coliform counts. This visualization includes seven weather stations and 10 downstream beaches across the city, as to capture the effect of rainfall on water quality. As a general rule, cities believe residents should not swim within 24-48 hours of a rain storm as rain can mobilize urban contaminants into the surface flow, streams, and eventually beaches. This is especially important during extreme precipitation events in Toronto, when there is the risk of combined sewer overflows (CSOs); CSOs occur when the volume of water discharged exceeds wastewater treatment plant capacity, and must be directed into Lake Ontario untreated.

On this premise, this visualization displays both precipitation and beach water quality over the span of a month. This is to examine, if at all, there are clear impacts between precipitation and the observed fecal coliforms at beach sites.

Data

The base layers for this visualization are i) ‘Toronto Watercourses’ polyline shapefile and ‘Toronto Watershed’ polygon shapefile, downloaded from the TRCA; a ii) ‘Lake Ontario’ shapefile, downloaded from the United States Geological Survey (USGS) Open Data portal; and ii) a ‘City of Toronto boundary’ shapefile downloaded from City of Toronto Open Data.

Seven (7) weather stations with daily precipitation data were identified for the duration of July 2018. Five of the weather stations’ datasets were retrieved from Environment Canada’s (EC) ‘Historic Weather’ data catalogue, and two additional weather stations’ datasets were accessed from the City of Toronto Open Data portal, from the ‘2018 Precipitation Data’ file. The daily total fecal coliform tests (i.e., E.Coli concentration) were downloaded from the City of Toronto’s ‘Swimming Conditions History’ webpage.

All of the weather and total coliform datasets were placed into individual comma separated value (.csv) files, with a column for date, latitude, longitude and an entry for either precipitation or E.Coli concentrations.

Technology

All spatial datasets were inputted and visualized within the open-source geographic information system (GIS), QGIS 3.10. To achieve a time-series visualization, this study used the QGIS plugin, ‘TimeManager’ (developed by Anita Graser). TimeManager allows for the creation of timelapse maps with temporally stamped data.

Process

To create the initial map, add the basemap shapefiles of i) ‘Toronto Watercourses’, ii) ‘Toronto Watersheds’, iii) ‘City of Toronto boundary’, and iv) ‘Lake Ontario’.

Import the .csv files for each individual Toronto weather station using the ‘Add Delimited Text Layer’ option in the ‘Add Layer’ menu in QGIS 3.10. Within the import manager, the ‘X’ and ‘Y’ geometry values have to be selected for defining point geometry; in the ‘Geometry Definition’ section of the import manager, assign the ‘X’ value to the weather stations’ longitude and the ‘Y’ value to latitude. As the latitude and longitude coordinates were in decimals, minutes second, the geometry type was specified as ‘DMS’ in the coordinates box. If the data file has coordinates in decimal format, leave the coordinates box unchecked. Finally, select ‘Add’ and a point for the weather station which will be placed on the map. Repeat this process for each of the Toronto weather stations and Toronto beaches .csv files.

The next step is to create a spatial buffer for the precipitation levels that surround each weather station. In the tools pane, select ‘Vector’, then the geoprocessing toolbox (i.e., ‘Geoprocessing’), then select the buffer operation (i.e., ‘Buffer’). Using the weather station point as the start for the buffer, then define the width of the buffer. For this map the value selected was 5km. To limit overlap of stations. Repeat this process for each of the Toronto weather station points.

Once Toronto weather stations are buffered, proceed to the ‘properties’ pane for the new point and select the variable ‘precipitation’, then specify layer symbology under ‘graduated symbols’. Within ‘graduated symbols’, set the symbol ranges and select the colour ramp. In this example, buffers were each made to be 50% opacity.

To create the beach points, proceed to the layer’s ‘properties’ pane, and select ‘symbology’. Selecting ‘E.Coli’ as the display variable, specify layer symbology using ‘graduated symbols’, and then select a corresponding symbol. For the purpose of this map, four categories of fecal coliform concentration were used: 0-50 (Green), 51-100 (Yellow), 101- 200 (Red), and 201-999 (Starburst). Graduating visualization breaks in four categories was chosen to be able to see the changes, they do not directly correspond to beach advisory levels.

Map with buffered weather station points and beach data points

Once weather station points and buffers, and beach points were set up, open up the ‘Time Manager’ plugin from the workbench in QGIS. Add in each vector layer, ensuring that the date format of the points and buffers is in ‘yyyy-mm-dd’, otherwise it will not work. For the purpose of this visualization, days were selected as the ‘time format’

Finally, select export video. When exporting using TimeManager, it does not export as a compiled video; instead, TimeManager creates a different image for each day. While this isn’t ideal for a very large data set, it does automate map generation in a consistent layout form. Moreover, it is not possible to add map elements in the QGIS 3.10 ‘TimeManager’ plugin, therefore this must all be done as post-QGIS processing. In this case, Microsoft PowerPoint (v.2019) was selected, with the additional map elements (scale bar, legend, title, and north arrow) added at this point. A video was then compiled in Microsoft PowerPoint (v.2019) and uploaded to YouTube.

Results

Below is the final result, published as a video on YouTube.

Toronto Maple Leafs Game-Day Guide

Author: Olivia Kariunas

Geovisualization Project Assignment @RyersonGeo, SA8905, Fall 2021

Project Link: https://arcg.is/1Xr9i52

Background

The inspiration behind creating this geovisualization project stems from my own curiosity about Toronto’s tourism industry and love of the hometown hockey team. There have been numerous instances where I found myself stressed and anxious about planning a stay within Toronto due to the overwhelming number of options for every element of my stay. I wanted to create content in an interactive manner that would reduce the scope of options in terms of accommodations, restaurants, and other attractions in a user-friendly way. With a focus on attending a Toronto Maple Leafs game, I have created an interactive map that presents readers with hotels, restaurants and other attractions that are highly reviewed, along with additional descriptions that may provide useful to those going to these places for the first time. Each of these locations are located under 1 kilometer from the Scotiabank Arena to ensure that patrons will not require extensive transportation and can walk from venue to venue. Also, the intent behind the interactive map is to increase fan engagement by helping fans find a sense of community within the selected places and ease potential stressors of planning their stay. For a Toronto Maple Leafs fan, the fan experience starts before the game even begins.

Why Story Map?

Esri’s Story Map was chosen to conduct this project because it is a free user-friendly method that allows anyone with an Esri Online account to create beautiful stories to share with the world. By creating a free platform, any individual or business can harness the benefits of content creation for their own personal pleasure or for their small business. Furthermore, the Shortlist layout was chosen to include images and descriptions about multiple locations for the Story Map to give readers visual cues of the locations being suggested. The major goal behind using this technology is to ensure that individuals in any capacity can access and utilize this platform by making it accessible and easy to understand.

Data

To obtain the data for the specific locations of the hotels, restaurants, and other attractions, I inspected various travel websites for their top 10 recommendations. From these recommendations, I selected commonalities among the sites and included other highly recommended venues to incorporate diversity among the selection. For the selected hotels, I attempted to include various category levels to accommodate different budgets of those attending the Leafs game. Additionally, all attractions chosen do require an additional purchase of tickets or admission, but vary in price point as well.

Creating Your Story Map

Start the Story Map Shortlist Builder using a free ArcGIS public account on ArcGIS Online.

Create a title for your interactive map under the “What do you want to call your Shortlist?”. Try to be as creative, but concise, as possible!

The main screen will now appear. You can now see your title on the top left, as well as a subtitle and tabs below. To the right, there is a map that you can alter as you like. To add a place, click the “Add” button within the tab frame. This will allow you to create new places that you want to further describe.

A panel will appear where you can enter the name of the chosen destination, provide a picture, include text, and specify its location. You can include multiple images per tab using the “Import” feature. Once the location has been specified using the venue’s address, a marker will appear on the map. You are able to click and drag this marker to any destination that you choose. The colour of the marker correlates to the colour of the tab. Additionally, you can include links within the description area to redirect readers to the respective venue’s website.

Completed location post with title, image, and description.

Click the “+” button on the top right hand corner of the left side panel to add more destinations. The places that you add will show as thumbnails on the left side of the screen. Click the “Organize” button underneath the tab to reorder the places. You can order these in any way that seems logical for your project. Click “Done” when satisfied.

To create multiple tabs, click the “Add Tab” button. To edit a tab, click the “Edit Tab” button. This will allow you to change the colour of the tab and its title.

The Edit, Add, and Organize Tabs can be found to the right of the other tabs and above the map.

To save your work, press the “Save” button occasionally, so all of your hard work is preserved.

There are also optional elements that you can include as well. You can change the behaviour and appearance of your Shortlist by clicking the “Settings” button. You are able to change the various functions people can utilize on the map. This includes implementing a “Location Button” and “Feature Finder” where readers can see their own location on the map and find specific locations on the map, respectively. You are also able change the colour scheme and header information by clicking on their tab options. Hit “Apply” when satisfied.

To share your Shortlist click the “Save” button and then click the “Share” button. You can share publicly or just within your organization. Additionally, you can share using a url link or even embed the Story Map within a website.

Limitations & Future Work

The main limitation of this project was selecting what venues to include. Toronto is a lively city with an overwhelming amount of options for visitors to choose from, resulting in many places being overlooked or unaccounted for. Overall, the businesses chosen represent a standard set of places for those who are unfamiliar with the city. To include a more diverse set of offerings, an addition to the current project, or an entirely new project, can be created to include places that provide more niche products/services. Furthermore, a large portion of the venues chosen were selected from travel/tourism advisory websites where the businesses on the sites may pay a fee to be included, thus limiting the amount of exposure other businesses may have.

Overall Thoughts

Story Map was simple to understand and the platform was aesthetically pleasing. My only reservations about this program is the limited amount of stylization control in terms of the text and other design elements. I would most likely use this platform again, but may attempt to find a technology that allows for more control over the overall appearance and settings of the geovisualization.

Thank you for reading my post. Have fun creating!

Building digitization using Artificial Intelligence – an Open Source approach

By Nikita Markevich

Geovisualization Project Assignment, SA8905, Fall 2021

INTRO

With the development of automation and machine learning, a new approach in raw data acquisition has been opened for people to try. QGIS is a popular open-source GIS software that allows the creation of custom plugins for all sorts of geoprocessing. One such plugin is called Mapflow, developed by Russian-based company GEOAlert. Mapflow is an easy-to-use plugin to retrieve ground data from satellite imagery such as buildings, roads, construction zones, and forest canopies. This blog will introduce how to use Mapflow through a browser environment. To learn how to use the plugin, please refer to the Esri Story Maps tutorial through this link: https://storymaps.arcgis.com/stories/dfd88d7170c74f33a4dd5f7583cdc414

The difference between the use of Mapflow in the browser and through the plugin is that browser only allows detection from web-based satellite services such as Mapbox, or custom imagery through URL, while in the plugin, custom satellite imagery can be processed straight from the user’s device. The major advantage of the browser approach is that the process is using remote servers which is faster than the plugin process.

Mapflow website project page

USER INTERFACE

Mapflow online service uses free to try system by giving 500 free credits when opening an account. Each process requires credits based on the size of the data area that the user wishes to process. If the user runs out of credits, it is possible to top up the balance in the top right corner for the price of 100 CAD per 1000 points.

Let’s explore the project page. The project is organized in steps where the user can choose the data source, the type of AI Model that the user wishes to run, and post-processing operation for additional data gathering. AI Models that are available in the browser copy the models which are available in the QGIS plugin. AI can provide digitization for buildings, high-density housing, forests, roads, construction, and agricultural fields.

In the data source tab, a user can either use the embedded draw tool to choose the area for processing or upload polygon data in GEOJSON format. The draw rectangle tool is very intuitive in its use and as soon as it’s drawn, the website provides the area’s size in squared kilometers. This number is used by the website to determine how many credits are required to process the area. The larger the area, the more credits it costs to process.

DATA

The area of interest for this example would be focused on the same area as was used in the plugin tutorial in Esri Story Maps: the city of Ciego De Avilo in Cuba. The drawn rectangle over the city and closest suburbs estimated the area to be 45.31 squared kilometers. Originally the area was raised to my attention when I was doing some research project for the company I work for to explore the possibility of constructing fiber service in the Caribbean region. While searching for building and road data through open sources such as OpenStreetMap, I realized that some Caribbean countries and especially Cuba is missing geographic data that is required to create a fiber map model. After exploring several options, the plugin Mapflow proved to be most useful to generate geodata from available free commercial satellite imageries.

PROCESS

The Chosen area is now inputted in our project. The next steps would be to choose the model and post-processing data. We will choose a buildings model to test the speed of the browser process and compare it with the plugin process. The big perk of the browser tool is the post-processing options. One such option is automatic polygon simplification, which would simplify the results of the model. In the plugin version, the results of the model outputted some building polygons in broken shapes or fuzzy polygons. That would create additional work post-processing polygons manually. The browser tool offers that option for free.

The area of interest costs 227 credits to be processed, which means that every 100 squared kilometers processed costs 500 points.

As soon as the Run processing button is pressed, the final step is to wait for the process to finish and download the processed data. The process finished in 32 minutes. That is 15 minutes faster than in the plugin process, which was 47 minutes.

After the process is finished, the user can view the results in the browser and download the file in the GEOJSON format.

The process assigns id numbers to each shape as well as shape types, such as rectangle, grid snap, or l-shape. This information can help with further post-processing and solve any automation mistakes.

LIMITATIONS AND FUTURE WORK

The most important limitation of this tool is its cost, however, if the user decides to process an area larger than 100 square kilometers, one can create multiple accounts and use free credits each time. Secondly, the processed results sometimes output shapes that are very questionable in their nature. Some polygons merged multiple buildings into ones, others detected buildings partially, in other cases the orientations of polygons are off. This can be fixed in the manual post-processing by GIS professionals.

In the future, this tool can be potentially be used to populate the OpenStreetMap dataset with the building polygons and roads data. Open Source data is very important for many gis users, and AI automation is the perfect companion that makes the work of GIS enthusiasts much easier by streamlining the most tedious processes in geographic analysis.

Introducing YouthMappers

Author: Daniel Council

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2021

Project Link: https://arcg.is/15zmWP0

Background

During my time in undergrad, I became involved with an international network of student mappers called YouthMappers. Through virtual internships and engagement with the chapter at my university, I started to become an active member of the network. For starters, one of the main goals of YouthMappers is to create open data for areas of the world that are lacking readily available spatial data.

The concept of open data is similar to Wikipedia, it can be provided by anyone. The primary method of open data collection used is OpenStreetMap, which is an open source platform that anyone can edit and upload spatial information onto, such as roads and buildings, for example. Many companies, organizations, and websites use data found on OpenStreetMap. The popular mobile-phone game, Pokémon Go, sources its map data from OpenStreetMap. However, arguably the most beneficial aspect of open data is that it is free, readily available, and accessible to anyone.

There are currently 291 YouthMappers chapters located throughout 62 countries around the globe. My chapter was located in Muncie, Indiana, at Ball State University. I interned with YouthMappers to research how open data is being used in Belize, and I also looked into how the Belizean government views open data as opposed to official sources of information. Additionally, I worked with the YouthMappers Validation Hub, which works to validate mapping projects conducted by YouthMappers chapters.

Project Description

For my geovisualization project, I was inspired by my involvement with YouthMappers. I wanted to introduce the organization to our class using technology provided by Esri. I often work with Dashboards, web maps, and Story Maps, but I was interested in trying out one of the other apps that Esri hosts in order to learn a new tool. I came across Experience Builder in ArcGIS Online, and was interested in how it can almost be used as a tool for creating a website, one that can be viewed across any type of device.

While there is a lot of overlap in functionality between Experience Builder, Dashboards, and Story Maps, Experience Builder allows for increased customization. There is no coding necessary, however. In fact, the user interface for creating an Experience is quite user friendly once you learn the main concepts. Within Experience Builder, you can even integrate and link other Esri applications like Survey123 or Dashboards, a functionality not available elsewhere. Experience Builder can be more comprehensive than Dashboards, which is mainly used to provide information on a singular, non-scrolling screen. With Experience Builder, you can create long, scrolling pages (which I did not personally do in my project). With this being said, Experience Builder is definitely the way to go if you’re looking to make something that is more in tune with a website.

The remainder of this blog post will serve as a tutorial for the basics of how to use Experience Builder to create a web page for your organization. The approach I took was fairly simple, as I wanted to be able to disseminate the key information with as few pages and tabs as possible, and also have everything fit on a singular screen to prevent the need for endless scrolling. I only included three tabs to display information, which are described below. YouthMappers already has their own website, so my project is more of a condensed and interactive version that can be viewed in a short amount of time, and provides a general introduction to people who may be unfamiliar with the network.

Three Tabs used to Separate Information

About: Used to introduce the organization and give a visualization of how widespread it is. The map I used is interactive and allows for user-friendly navigation and custom pop-ups for each point on the map.

Our Work: Gives a real-life example of a project conducted by the organization, and shows the benefits and impact this project makes. Giving an example helps the viewer understand how the organization operates. A visual of the completed or in-progress project can further provide something almost tangible.

Get Involved: Provides a way to viewers to become a member or learn more about the organization if they wish. Gives a link to the more detailed organization website.

Experience Builder: The Basics

Now, for using Experience Builder itself, there are a few important concepts to learn before beginning. While the app provides a number of pre-made templates, I would recommend starting with a blank project. I tried starting with a template but personally found it too overwhelming. I enjoyed the process of learning how Experience Builder works from scratch and found it easier than trying to integrate my ideas with something that was already formatted in a specific way.

Pages: In my project, there are three pages, each being linked to the tabs mentioned above. Pages are almost like layers on a map, each one contains different components and displays different visualizations.

Widgets: Each page can contain a multitude of widgets. Different types of widgets are designated by the icon to the left of its name. For my Experience, I used maps, images, text, tabs, and charts, just to name a few. I also gave my widgets descriptive names that related to what they displayed. It helped me keep track of my widgets in an organized manner.

After adding your widgets, you can customize them to your liking. When a widget is activated, the “Style” tab appears on the right side of the screen. Here, one can alter the size, position, appearance, and other visual effects of each widget.

Overall, Experience Builder is a unique tool that combines the story-telling aspects of Story Maps, the geospatial technology of web maps, and the easy-to-navigate user interface of Dashboards. I would definitely use this tool again for future use, as I can now visualize more ways it can be utilized.

Tracking the COVID-19 Pandemic in Toronto with R and Leaflet

By: Tavis Buckland

Geovisualization Project Assignment, SA8905, Fall 2020

Github Repository: https://github.com/Bucklandta/TorontoCovid19Cases.git

INTRO

Over the course of the pandemic, the City of Toronto has implemented a COVID-19 webpage focused on providing summary statistics on the current extent of COVID-19 cases in the city. Since the beginning of the pandemic, this webpage has greatly improved, yet it still lacks the functionality to analyze spatio-temporal trends in case counts. Despite not providing this functionality directly, the City has released the raw data for each reported case of COVID-19 since the beginning of the pandemic . Using RStudio with the leaflet and shiny libraries, a tool was designed to allow for the automated collection, cleaning and mapping of this raw case data.

DATA

The raw case data was downloaded from the Toronto Open Data Portal in R, and added to a data frame using read.csv. As shown in the image below, this data contained the neighbourhood name and episode date for each individual reported case. As of Nov. 30th, 2020, this contained over 38,000 reported cases. Geometries and 2016 population counts for the City of Toronto neighbourhoods were also gathered from the Toronto Open Data Portal.

PREPARING THE DATA

After gathering the necessary inputs, an extensive amount of cleaning was required to allow the case data to be aggregated to Toronto’s 140 neighbourhoods and this process had to be repeatable for each new instance of the COVID-19 case data that was downloaded. Hyphens, spaces and other minor inconsistencies between the case and neighbourhood data were solved. Approximately 2.5% of all covid cases in this dataset were also missing a neighbourhood name to join on. Instead of discarding these cases, a ‘Missing cases’ neighbourhood was developed to hold them. The number of cases for each neighbourhood by day was then counted and transposed into a new data table. From there, using ‘rowSum’, the cumulative number of cases in each neighbourhood was obtained.

Example of some of the code used to clean the dataset and calculate cumulative cases

Unfortunately, in its current state, the R code will only gather the most recent case data and calculate cumulative cases by neighbourhood. Based on how the data was restructured, calculating cumulative cases for each day since the beginning of the pandemic was not achieved.

CREATING A SHINY APP USING LEAFLET

Using leaflet all this data was brought together into an interactive map. Raw case counts were rated per 100,000 and classified into quintiles. The two screenshots below show the output and popup functionality added to the leaflet map.

In its current state, the map is only produced on a local instance and requires RStudio to run. A number of challenges were faced when attempting to deploy this map application, and unfortunately, the map was not able to be hosted through the shiny apps cloud-server. As an alternative, the map code has been made available through a GitHub repository at the top of this blog post. This repository also includes a stand-alone HTML file with an interactive map.

Screenshot of HTML map produced by R Shiny App and Leaflet. Popups display neighbourhood names, population, raw count, and rate per 100,000 for the most recent case data.

LIMITATIONS

There are a couple notable limitations to mention considering the data and methods used in this project. For one, the case data only supports aggregation to Toronto neighbourhoods or forward sortation areas (FSA). At this spatial scale, trends in case counts are summarized over very large areas and are not likely to accurately represent This includes the modifiable areal unit problem (MAUP), which describes the statistical biases that can emerge from aggregating real-world phenomena into arbitrary boundaries. The reported cases derived from Toronto Public Health (TPH) are likely subject to sampling bias and do not provide a complete record of the pandemic’s spread through Toronto. Among these limitations, I must also mention my limited experience building maps in R and deploying them onto the Shinyapps.io format.

FUTURE GOALS

With the power of R and its many libraries, there are a great many improvements to be made to this tool but I will note a few of the significant updates I would like to implement over the coming months. Foremost, is to use the ‘leaftime’ R package to add a timeline function, allowing map-users to analyze changes over time in reported neighbourhood cases. Adding the function to quickly extract the map’s data into a CSV file, directly from the map’s interface, is another immediate goal for this tool. This CSV could contain a snapshot of the data based on a particular time frame identified by a user. The last functionality planned for this map is the ability to modify the classification method used. Currently, the neighbourhoods are classified into quintiles based on cumulative case counts per 100,000. Using an extended library of leaflet, called ‘leafletproxy’, would allow map users greater control over map elements. It should be possible to allow users to define the number of classes and which method (i.e. natural breaks, standard deviation, etc.) directly from the map application.

Interactive Map and Border Travels

Given the chance to look at making geovisualisation, a pursuit began to bring in data on a scope which would need adjustments and interaction for understanding geography further and further, while still being able to begin the journey with an overview and general understanding of the topic at hand.

This blog post doesn’t unveil a hidden gem theme of border crossing, but demonstrates how an interactive map can share the insights which the user might seek, not being limited to the publisher’s extents or by printed information. Border crossing is selected as topic of interest to observe the navigation that may get chosen with borders, applying this user to a point of view that is similar to those crossing at these points themselves, by allowing them to look at the crossing options, and consider preferences.

To give the user this perspective, this meant beginning to locate and provide the crossing points. The border crossing selected was the US border between Canada and between Mexico, being a scope which could be engaged with the viewer and provide detail, instead of having to limit this data of surface transportation to a single specified scale and extent determined by the creator rather than the user.

Border crossings are a matter largely determined by geography, and are best understood in map rather than any other data representation, unlike attributes like sales data which may still be suitable in an aspatial sense, such as projected sales levels by line graph.

To get specific, the data came from the U.S. Bureau of Transportation Statistics, and was cleaned to be results from the beginning of January 2010 til the end of September 2020. The data was geocoded with multiple providers and selected upon consistency, however some locations were provided but their location could not be identified.

Seal of the U.S. Bureau of Transportation Statistics

To start allowing any insights for you, the viewer, the first data set to be appended to the map is of the border locations. These are points, and started to identify the distribution of crossing opportunities between the north American countries. If a point could not be appended to the location of the particular office that processed the border entries, then the record was assigned to the city which the office was located in. An appropriate base layer was imported from Mapbox to best display the background map information.

The changes in the range of border crossings were represented by shifts in colour gradient and symbol size. With all the points and their proportions plotted, patterns could begin to be provided as per the attached border attributes. These can illustrate the increases and decreases in entries, such as the crossings in California points being larger compared to entries in Montana.

But is there a measure as to how visited the state itself is, rather than at each entry point? Yes! Indeed there is. In addition to the crossing points themselves, the states which they belong to have also been given measurement. Each state with a crossing is represented on the map displaying a gradient for the value of average crossing which the state had experienced. We knew that California had entry points with more crossings than the points shown in Montana, but now we compare these states themselves, and see that California altogether still experienced more crossings at the border than Montana had, despite having fewer border entry points.

Could there be a way to milk just a bit more of this basic information? Yes. This is where the map begins to benefit from being interactive.

Each point and each state can be hovered over to show the calculated values they had, clarifying how much more or less one case had when compared to another. A state may have a similar gradient, an entry point may appear the same size, but to hover over them you can see which place the locations belong to, as well as the specific crossing value it has. Montana is a state with one of the most numerous crossing points, and experiencing similar crossing frequencies across these entries. To hover over the points we can discover that Sweetgrass, Montana is the most popular point along the Montana border.

In fact, this is how we discover another dimension which belongs to the data. Hovering over these cases we can see a list of transport modes that make up the total crossings, and that the sum was made up of transport by trucks, trains, automotives, busses, and pedestrians.

To discover more data available should simply mean more available to learn, and to only state the transport numbers without their visuals would not be the way to share an engaging spatial understanding. With these 5 extra aspects of the border crossings available, the map can be made to display the distributions of each particular mode.

Despite the points in Alaska typically being one of the least entered among the total border crossings, selecting the entries by train draws attention to Skagway, Alaska, being one of the most used border points for crossing into the US, even though it is not connected to the mainland. Of course, this mapped display paints a strong understanding from the visuals, as though this large entry experienced at Skagway, Alaska is related to the border crossings at Blaine, Washington, likely being the train connection between Alaska and Continental USA.

Mapping truck crossing levels (above), crossings are made going east and past the small city of Calexico. The Calexico East is seen having a road connection between the two boundaries facing a single direction, suggesting little interaction intended along the way

When mapping pedestrian crossings (above), these are much more popular in Calexico, the area which is likely big dense to support the operation of the airport shown in its region, and is displaying an interweaving connection of roads associated with an everyday usage

Overall, this is where the interactive mapping applies. The borders and their entry points have relations largely influenced by geography. The total pedestrian or personal vehicle crossings do well to describe how attractive the region may be on one side rather than another. Searching to discover where these locations become attractive, and even the underlying causes for the crossing to be selected, can be discovered in the map that is interactive for the user, looking at the grounds which the user chooses.

While this theme data layered on top highlights the topic, the base map can help explain the reasons behind it, and both are better understood when interactive. It isn’t necessary to answer one particular thought here as a static map may do, but instead to help address a number of speculative thoughts, enabling your exploration.