The History Of Chicago’s Homicides For The Last Two Decades

By: Charan Batth

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2021

Introduction

The crime rate for the city of Chicago is significantly higher than the US average. In 2016, Chicago was responsible for nearly half of the homicides increase in the US.

A time series interactive dashboard will be used to visualize and analyze the distribution of homicides across Chicago for the last two decades. We will create this dashboard using Tableau Desktop, which is an interactive data visualization and analytical tool. In addition to the dashboard, we will create two visualizations: treemap and line chart. Treemap will be used to visualize aggregated homicides across police districts. The line chart will visualize the number of homicides per month.

Data

The data used to produce the Interactive Dashboard was obtained from the Chicago Data Portal. The dataset consists of 7,424,694 crimes between 2001 to 2021. However, since our crime is focused on homicides, the data was filtered by setting the field Prime Type to be equal to HOMICIDE and then the data was downloaded as a CSV file.

I will go through the step-by-step process of creating the time-series interactive dashboard, the dashboard can be viewed here for reference.

Creating the Interactive Dashboard

To get started on creating the interactive dashboard and the visualizations. We will first import the data, since our dataset is in a CSV format we will select the Text File under the To a File Option. After opening the data, you will see a screen showing all the fields in the CSV file and on the bottom left beside Data Source, you will see a tab called Sheet 1 (highlighted in orange), we will click on it to begin the process of creating the dashboard. The Worksheet tabs will be used to create the map and visualizations and the Dashboard tab will be used to create the dashboard.

In order to create a dot density map showing homicides across Chicago, we need to plot the latitude and longitude coordinates for each homicide. We do this by dragging the Longitude field into the Columns tab and the Latitude field into the Rows tab. We then set both fields to Dimension, by right-clicking on the fields. A map will automatically be created however, there are two minor issues with the map, shown below.

Our map shows 1 null point (displayed on the bottom right of the map) and there is a random point in Missouri.

In order to fix these issues, we will first remove the null point by clicking on 1 null and selecting the Filter Data. To remove the random point located in Missouri, we will right-click on the point and select Exclude. This will remove the point from the map, and our map extent will automatically zoom to the Chicago area.

Creating Time-series Map

To create the time-series map, we will drag the Year field into the Pages card. This will create a time slider that will allow you to view the dot density map for any chosen year. The time slider also allows the user to animate the map, by clicking on the loop button and the animation can be paused at any time.

For our dot density map, we will show specific attributes for each homicide location on the map. This can be accomplished by dragging the fields into the Marks card. For our map, we will show the following fields: Block, Description, District, Location Description, and Date.

To make our map look aesthetic, we will change the theme of our map to Dark. This can be done by going to the header Map, hovering over to Background Map, and selecting Dark. To better visualize the locations of the data points, we will add zip code boundaries to the map. To do this, we will go to the header Map and from there we will choose Map Layers and then select the Zip Code Boundaries under the Map Layers pane (this will appear on the left side of the sheet). Lastly, we are going to change the colour and size of the data points. This can be done by going to the Marks card and selecting the Color and Size option.

Visualizations

Treemap

We will now create the visualizations to better understand the distribution of homicides in Chicago. To begin the process, we will create a new Worksheet and we will name it Treemap. To create a treemap, we will first drag the Year field into the Page card, as we are creating a time-series interactive map. Since we want to see how homicides vary across police districts, we will drag the District field into the Marks card. To show the homicides, we will drag the Primary Type field onto both the Color and Size options in the Marks card. We will then set the Primary Type field to Measure and choose Count, as we want to show aggregated homicides. The final step is to make our worksheet transparent, so we could add it to our interactive map. This is done by going to the header Format and selecting Shading. In the Formatting pane, we will set the Worksheet and Pane background colour to None.

Line Chart

We will create a new Worksheet and name it chart. Our data does not contain the month the incident occurred, but we have the Date when the incident occurred. So, in order to extract just the month, we will need to create a new field. This can be done by going to the Analysis header and choosing Create Calculated Field. We will give the field an appropriate name, change the name Calculation1 to MonthOfIncident. To extract the month we first need to truncate the Date field, as it contains both the date and time. We will use the LEFT function which allows us to truncate a string type specified by the length. The date consists of 10 characters (dd/mm/yyyy), so our query would be LEFT([Date], 10). Next, we need to extract the month from the truncated string, so we will use the built-in function, called MONTH, which returns a number representing the month. However, the MONTH function requires its parameter data type to be a date. So we need to convert our truncated string date to date, we can do this by applying the DATE function on the LEFT function and finally applying the MONTH function on the entire expression. Thus our expression for finding the month is:

Now, we can finally begin the process of creating the line chart. As we are making a time-series interactive map, we will also need to make a time-series line chart. So, we will drag the Year field into the Pages card, as this will be part of our time-series interactive map. Next, we will drag the MonthOfIncident field into the Columns tab and Primary Type into the Rows tab. Since we want to show the total number of homicides, we will set the Primary Type field to Measure and select Count. We will make this worksheet transparent as well, so we will go to the header Format and select Shading. In the Formatting pane, we will set the Worksheet and Pane background colour to None.

Creating the Dashboard

To create our dashboard, we will click on the Dashboard tab, right beside the Worksheet tab. In the dashboard, we can add all the worksheets we have created. We will first add the interactive map followed by the visualizations. To display the visualizations on top of the map, we need to make them float. So, we will select one of the visualizations and hover over to More Options (shown as a downward arrow) and click on Floating, repeat this process for the other visualization. You can also change the size of the dashboard by going to the Size pane, the default size is Desktop Browser (1000 x 800), we will change it to Generic Desktop (1366 x 788). Last but not least, we will publish this dashboard, by going to the Server -> Tableau Public -> Save to Tableau Public As. Tableau Public allows anyone to view the dashboard and allow anyone to download it and specific permissions for the dashboard can be applied.

Limitations and Future Goals

One of the main limitations that occurred during the process of creating the dashboard was gathering the data. First, I had downloaded the entire CSV file containing all different types of crimes. However, when I filtered the Primary Type to HOMICIDE in the Filters card, a huge amount of data for homicides was missing. So, I then decided to directly connect the dataset to Tableau using ODATA Server. It took me a couple hours to connect to the server, just to run into the same issue. I then tried exporting the data through SODA API from the portal, I was able to find raw data for homicides however, it contained partial data. After a while, I figured out I had to directly filter the table in the Chicago Data Portal in order to download the entire data for Chicago homicides.

Another limitation I faced with the data was creating the visualizations. Originally I intended on creating a highlight table to show how homicides varied across police districts and community areas. However, due to the data having null values for community areas, the visualization couldn’t be created. Furthermore, I was only able to create basic visualizations, as the data did not have any interesting variables to help analyze the homicide distribution. For instance, if each homicide incident included a Zip Code, it could have been used to explain the spatial pattern much better rather than using police districts to show how homicides vary across it.

If I was to expand on this project, I would try to incorporate all different crime incidents from 2001-2021 to see Chicago’s overall crime history. In addition to this, I would find demographic data for Chicago such as population, education, and average family income to help understand the spatial pattern for the distribution of crimes.

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.

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.

COVID-19 in Toronto: A Tale of Two Age Groups

By Meira Greenbaum

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2020

Story Map Link

Introduction

The COVID-19 pandemic has affected every age group in Toronto, but not equally (breakdown here). As of November 2020, the 20-29 age group accounts for nearly 20% of cases, which is the highest proportion compared to the other groups. The 70+ age group accounts for 15.4% of all cases. During the first wave, seniors were affected the most, as there were outbreaks in long-term care homes across the city. By the end of summer and early fall, the probability of a second wave was certain, and it was clear that an increasing number of cases were attributed to younger people, specifically those 20-29 years old. Data from after October 6th was not available at the time this project began, but since then Toronto has seen another outbreak in long-term care homes and an increasing number of cases each week. This story map will investigate the spatial distribution and patterns of COVID-19 cases in the city’s neighbourhoods using ArcGIS Pro and Tableau. Based on the findings, specific neighbourhoods with high rates can be analyzed further.

Why these age groups?

Although other age groups have seen spikes during the pandemic, the trends of those cases have been more even. Both the 20-29 and 70+ groups have seen significant increases and decreases between February and November. Seniors are more likely to develop extreme symptoms from COVID-19, which is why it is important to focus on identifying neighbourhoods with higher rates of seniors. 20-29 is an important age group to track because increases within that group are more unique to the second wave and there is a clear cluster of neighbourhoods with high rates.

Data and Methods

The COVID-19 data for Toronto was provided by the Geo-Health Research Group. Each sheet within the Excel file contained a different age group and the number of cases each neighbourhood had per week from January to early October. The format of the data had to be arranged differently for Tableau and ArcGIS Pro. I was able to table join the original excel sheet with the columns I needed (rates during the week of April 14th and October 6th for the specific age groups) to a Toronto neighbourhood shapefile in Pro and map the rates. The maps were then exported as individual web layers to ArcGIS Online, where the pop-ups were formatted. After this was done, the maps were added to the Story Map. This was a simple process because I was still working within the ArcGIS suite so the maps could be transported from Pro to Online seamlessly.

For animations with a time and date component, Tableau requires the data to be vertical (i.e. had to be transposed). This is an example of what the transformation looks like (not the actual values):

A time placeholder was added beside the date (T00:00:00Z) and the excel file was imported into Tableau. The TotalRated variable was numeric, and put in the “Columns” section. Neighbourhoods was a string column and dragged to the “Colour” and “Label” boxes so the names of each neighbourhood would show while playing the animation. The row column was more complicated because it required the calculated field as follows:

TotalRatedRanking is the new calculation name. This produced a new numeric variable which was placed in the “Rows” box.

If TotalRatedRanking is right clicked, various options will pop-up. To ensure the animation was formatted correctly, the “Discrete” option had to be chosen as well as “Compute Using —> Neighbourhoods.” The data looked like the screenshot below, with an option to play the animation in the bottom right corner. This process was repeated for the other two animations.

Unfortunately, this workbook could not be imported directly into Tableau Public (where there would be a link to embed in the Story Map) because I was using the full version of Tableau. To work around this issue, I had to re-create the visualization in Tableau Public (does not support animation), and then I could add the animation separately when the workbook was uploaded to my Tableau Public account. These animations had to be embedded into the Story Map, which does have an “Embed” option for external links. To do this, the “Share” button on Tableau Public had to be clicked and a link appeared. But when embedded in the Story Map, the animation is not shown because the link is not formatted correctly. To fix this, the link had to be altered manually (a quick Google search helped me solve it):

Limitations and Future Work

Creating an animation showing the rate of cases over time in each neighbourhood (for whichever age group or other category in the excel spreadsheet) may have been beneficial. An animation in ArcGIS Pro would have been cool (just not enough time to learn about how ArcGIS animation works), and this is an avenue that could be explored further. The compromise was to focus on certain age groups, although patterns between the start (April) and end (October) points are less obvious. It would also be interesting to explore other variables in the spreadsheet, such as community spread and hospitalizations per neighbourhood. I tried using kepler.gl, which is a powerful data visualization tool developed by Uber, to create an animation from January to October for all cases, and this worked for the most part (video at the end of the Story Map). The neighbourhoods were represented as dots (not polygons), which is not very intuitive for the viewer because the shape of the neighbourhood cannot be seen. Polygons can be imported into kepler.gl but only as a geojson and I am unfamiliar with that file format.

Where to Grow?

Assessing urban agriculture potentiality in the City of Edmonton

By Yichun Du
Geovisualization Project, @RyersonGeo, SA8905, Fall 2020

Background

North America is one of the most urbanized areas in the world, according to the United Nations, there are about 82% of the population living in the cities today. It brings various issues, one of them is the sustainability of food supply. Currently, the foods we consume are usually shipped domestically and internationally. Only a few of them are locally supplied. It is neither sustainable nor environmentally-friendly, as a large amount of energy is burned through the logistics of the food supply chain. In order to address this issue, many cities are introducing and encouraging urban agriculture to citizens.

Urban agriculture is usually summarized as food production activities occurred in urban areas (Colasanti et al. 2012). Zezza & Tasciotti (2010) identified that urban farming and related sectors have employment of around 200 million workforces, and it provides food products to more than 800 million residents. Also, urban agriculture can bring benefits to the city in the aspects of economics, social, citizen health, and ecology. Besides, implementing urban agriculture can help the city to reinvent itself in an attracting, sustainable, and liveable way.

The City of Edmonton is North America’s northernmost city (at about 52.5° N) with a population of 0.932 million (2016 Census). However, apart from some very general municipal strategies (a general vision for food and urban agriculture in The Way We Grow MDP, and a more detailed document fresh: Edmonton’s Food & Urban Agriculture Strategy introduced in 2012), there is very little study on the urban agriculture suitability for the City of Edmonton. which gives the incentive to develop this study. The Geo-Visualization of the outcome can inform Edmontonians where to sow the seeds, and also tell them that the place they live has great potential in growing food.

Concept

Edmonton is located on the Canadian Prairie, which leads to very minor topographic changes in the city, but very cold and snowy winters. It limits food production in snow-free seasons, but provides flat ground for agriculture. To conduct an assessment of urban agriculture potentiality for the city, I focused on two themes: ground and rooftop.

The ground part is for assessing the potentiality of food production directly taking place on the ground, including the backyard of a house. The general concept is to utilize the existing land-use that supports urban agriculture activities, however, it should be far away from pollution, and to avoid negative externalities. That is to say, current agriculture zoning, parkland, and vacant lots are favoured. Top-up on that, soil nutrient level will be taken into consideration. Then, the constraints will be the close distance to the source of pollution. Meanwhile, urban agriculture activities can bring potential contaminations, such as water pollutants to the water bodies. So the activity should be at a distance to water bodies.

The rooftop part is for assessing places such as the rooftop of a large building, balconies of a suite in a condo building, or any other places in an artificial structure. The goal of implementing urban agriculture at rooftops is to encourage people to participate, and to focus on proximity to markets, which is people’s dining tables. However, pollution from the surrounding environment should be avoided.

The project will present the scores at the neighbourhood level in both themes of Ground and Rooftop that shows the potentiality of urban agriculture in the City of Edmonton.

Data Source

Based on the general concepts mentioned above, the following data are chosen for conducting the analysis. Majority of the data are retrieved from the City of Edmonton’s Open Data Portal. The waterbody shapefile is obtained from the Boundary files at Stats Canada’s 2016 Census.

Methodology

The preliminary part of this project is done at ArcMap. Then, the visualization part is proceeded using Tableau.

The general methodology can be summarized in the following workflows. The first workflow below is for the Ground potentiality. Majority of the work was done on ArcMap. After that, the final results were brought to Tableau for Geo-Visualization with the Rooftop part. The blue boxes are for presenting the original shapefiles. The Yellow boxes are for the Data Criteria. The pink boxes are displaying the secondary shapefiles that are constraints, the green boxes are showing the potential areas or the final results. Both of the pink and green boxes are generated through the white boxes (geoprocessing steps). The final results are processed with data normalization, and an average score was given. So the total score in one neighbourhood was normalized by the total area.

The second part is the Rooftop potentiality. It has a similar process of the Ground part in getting the results.

Also, a table for the weighting scheme of all the selected criteria is shown below. Constraints are assigned with a negative value, while potentials are assigned positive values. Also, the weights will be heavier for more constraints or potentials.

Results

Larger the number, higher the potential for conducting urban agriculture. The Ground has a maximum score of 3.8, while Rooftop has a maximum score of 4 in this analysis.

The results of the scatter plot below suggest that the majority of the neighbourhoods in Edmonton have the potential for urban agriculture. For the Ground theme, only a few of the industrial zones have a score of 0. All types of neighbourhoods are widespread in the score classes. However, the River valley System tends to be associated with medium to high scores. For the Rooftop theme, more than half of the neighbourhoods are in medium to high scores (>2) for the potentiality. Nearly all the mature neighbourhoods are associated with scores higher than 3. Only a few transportation and developing land-uses are having scores of 0.

Scatterplot for scores of Ground and Rooftop potentiality at neighbourhood level.

The next screenshot is the final output from the Tableau Dashboard. Audiences can click on any of the elements that represent a neighbourhood for an excluded view of that specific neighbourhood. For example, you can click one neighbourhood on the Ground map, then the same neighbourhood will be highlighted in the Rooftop map, as well as the point representing that neighbourhood in the scatterplot will be zoomed in with the corresponding score in the two themes. On the other hand, the Audience can select the point in the scatterplot, and the neighbourhood will be zoomed in in the two maps. Also, the audience can view the typology of the neighbourhood and figure out the associated scores for each typology of the neighbourhood by selecting the typology in the legend. Then, all the neighbourhoods belong to that typology will be displayed in the three views.

Tableau provides an interactive visualization of the urban agriculture potentiality in Edmonton at the neighbourhood level. Please click here for viewing the project.

Dashboard view from Tableau for the final output.

For example, I clicked Oliver neighborhood for Ground score (1.270), then the associated Rooftop score (2.010) and the detailed location of Oliver Neighbourhood is shown in the Rooftop view. Also, the scatterplot for both scores is provided below, with the neighbourhood typology of Central Core.

Example of selecting Oliver neighbourhood.

Limitation

There are some limitations regarding this project’s data source and methodology. If I have access to updated soil nutrition data, solar radiation data, precipitation data that related to the Ground theme, then I would have a better assessment model for a more ideal result regarding the potentiality of the Ground. Also, an inventory of the physical surface details can help to determine where the impermeable surfaces are. Similarly, if I have a comprehensive dataset of rooftop types, including the slope of the roofs and the individual use of the building, could help to eliminate the unsuitable roofs. Moreover, detailed zoning shapefile with potential land-use modification of community gardens, or backyard gardens would be beneficial to the future application of this project. As for the methodology improvement, the major concern is the weight assignments. Opinions from local experts or the authority can help to improve the model to fit the local context. Also, public consultation or survey can bring the general public to the project, which can form a bottom-up approach in transforming Edmonton into an urban agriculture-friendly place. As an expectation for the future development of this Geo-Visualization project, I would like to see more inputs in data source, as well as participation from the general public and local authorities.

To sum up, this assessment of urban agriculture potentiality in the City of Edmonton assigns all the neighbourhoods scores for Ground and Rooftop potentiality. With those scores, a perception is provided to Edmontonians on where to sow the seeds on the ground, and which neighbourhoods are in the best locations for urban agriculture.

Reference

Colasanti KJA, Hamm MW, Litjens CM. 2012. The City as an “Agricultural Powerhouse”? Perspectives on Expanding Urban Agriculture from Detroit, Michigan. Urban Geography. 33(3):348–369. doi:10.2747/0272-3638.33.3.348

Zezza A, Tasciotti L. 2010. Urban agriculture, poverty, and food security: Empirical evidence from a sample of developing countries. Food Policy. 35(4):265–273. doi:10.1016/j.foodpol.2010.04.007

United States Presidential Election Results: 1976-2016 in Tableau

By: Vincent Cuevas

Geovisualization Project Assignment, SA8905, Fall 2020

Project link can be found here.

Introduction

The United States presidential elections occur every four years and much attention is placed on the polarization of US politics based on voting for either of the major political parties, the Democratic Party and the Republican Party. This project aims to use visualization to show the results across many different elections over time to view how the American public is voting for these two parties.

Methodology and Data

Tableau was used for the data visualization due to its ability to integrate multiple data sheets and recognize spatial data to instantly create maps. It is also able to quickly generate different types of visualizations in cartographic maps, bar charts, line graphs, etc.

Data was collected from the University of California – Santa Barbara website the Presidency Project. The repository contains data from elections all the way back up to 1789. This visualization will go back to 1976 and view results up until 2016. Other data sources were considered for this visualization, namely MIT’s Election Lab dataset from 1976-2016. However, this dataset contained results for up to 66 different parties that votes were casted for from 1976 to 2016. Incorporating this level of detail would have shown inconsistent data fields across the different election years. Other political parties are omitted from this project due to the inconsistency of party entrants by year and the fact that Democrats and Republicans take up the vast majority of the national vote. The Presidency Project data was used as it provided simpler views of Democrat-Republican results.

Data Retrieval

The downside to using UCSB’s Presidency Project data is that it is not available as a clean data file!

*Screenshot taken from UCSB’s Presidency Project webpage for 1976 US Election Results.*

The data was collected from each individual data page into an Excel sheet. One small piece of data that was collected elsewhere was the national voter turnout data, which was taken from the United States Election Project website.

Voting Margin Choropleth Map

Once the data was formatted, only two sheets needed to be imported into Tableau. The first was the state level results, and the second being the national level results. The relationship between the two is held to together by a join on the state fields.

Tableau has a nice feature in that it instantly converts recognizable data fields into spatial data. In this case, the state field generates latitude and longitude points for each state. Drag the auto-generated Latitude and Longitude fields into Columns and Rows, and then drag state under Marks to get this.

*Screenshot of adding Tableau generated longitude and latitude data to a Tableau worksheet.*

For one of the main sheets, one of the maps will show a choropleth themed map that will show voting margin differences between the Democratic Party and the Republican Party. Polygon shapes are needed, which can be done by going to the drop-down menu in Marks and selecting Map. Next, the sheet will need to identify the difference between states that were Democrat vs. Republican. A variable ‘PartyWin’ was created for this and dragged under marks, and colours were changed to represent each party.

The final step requires creating ranges based on the data. Ranges cannot be created manually and require either some programming logic and/or the use of bins. Bins were created by right-clicking a variable ‘VictoryMargin (%)’. The size of each bin is essentially a pre-determined interval (20 was chosen). VictoryMargin(%) was dragged under Marks in order to get a red/blue separation from the colours from Party Win. The Colors were edited under VictoryMargin to get appropriate light/darker hues for each colour. The specific bins were also appropriately labelled based on 20 point intervals.

*Screenshot of Party Win, Victory Margin choropleth map in Tableau Worksheet view*

The screenshot shows that you can hover over the states and retrieve information on Party Win, the percentage of Democrat and Republican votes that year, as well as the Victory Margin. The top-left corner also has Year in the Pages area, which also for a time-series view for each page that contains Year.

Vote Size Dot Symbol Map

While margin of victory in each state illustrates the degree on if the state voted Democrat or Republican, we know that the total number of Democrat and Republican not equal when comparing voting populations across different states. Florida, for example has 9,420,039 total votes casted and had a 1.2% victory margin for the Republicans in 2016. Contrast that with District of Columbia in the same year, which had 311,268 total votes, but with a 86.8% victory margin for Democrats. For the next map, dot symbols are used to determine the vote size (based on the variable Total State Votes) for each state.

The same longitude and latitude generated map will be used from the choropleth map, only this time the dots and the surrounding Open Street basemap are kept intact. A similar approach is taken from the choropleth map using Party Win to differentiate between Republican and Democrat states. The Total State Votes variable is dragged into the size area under Marks to create different dots sizes based on the numbers here. Bins were created once again – this time with an interval break of 2.5 million votes per state. Ideally, there would be customized breaks as many states fall into the lower end of total votes such as District of Columbia. Once the labelled bins are edited, additional information for State, Total Democrat Votes and Total Democrat Votes were entered to view in the Tooltip.

*Screenshot of Dot Symbol map based on Number of State Votes in Tableau Worksheet view*

Electoral College Seats Bar

American politics has the phrase of “270 To Win“, based on needing 270 electoral seats as of 2020 to win enough seats for the presidency. As recently as 2016, the Democratic candidate Hillary Clinton won the popular vote over the Republican candidate Donald Trump. However, Trump won the majority of electoral seats and presidency based on winning votes in states with a greater total number of seats.

A bar showing the number of electoral seats won can highlight the difference between popular vote, and that greater margin of victory in a state matters less than having a greater number of state seats won. To create this bar the same setup is used having Party Win and State underneath the marks. This time, a SUM value of the number of seats is dragged to the Columns. The drop down list is then changed into a bar.

*Screenshot of Electoral College Seats bar graph in Tableau worksheet.*

Dashboard and Nationwide Data Points

Since this data will go into a dashboard, there is a need to think how these visualizations compliment each other. The maps themselves provide data while looking at a view of individual states. The dynamic bar shows the results of each state, though is better at informing the viewer the number of seats of won by each party, and the degree to how many more seats were won. The dynamic bar needs some context though, specifically the number of total seats won nationwide. This logically took the visualization for placing the maps at the middle/bottom, while moving the electoral college bar to the top, while also providing some key indicators for the overall election results.

The key data points included were the party names, party candidates, percentage of popular vote, total number of party votes, total number of electoral seats, as well as an indicator of if either the Democratic or Republican Party won. Secondary stats for the Other Party Vote (%), Total Number of Votes Casted, as well as Voter Turnout(%). Individual worksheets were created of each singular stat and were imported into the dashboard. Space was also used to include Alaska and Hawaii. While the main maps are dynamic in Tableau and allow for panning, having the initial view of these states limits the need to for the user to find those states. All of the imported data had ‘Year’ dragged into the pages area of the worksheet, allowing for a time-series view of all of the data points.

You can see what the time series from 1976 to 2016 looks like in a gif animation via this Google Drive link.

Insights

When looking at the results starting from 1976, an interesting point is that many Southern states were Democratic (with a big part due to the Democratic candidate Jimmy Carter being governor of Georgia) that are now Republican in 2016. 1980 to 1984 was the Ronald Reagan era, where the Californian governor was immensely popular throughout the country. Bill Clinton’s reign from in 1992 and 1996 followed in Carter’s footsteps with the Arkansas governor able to win seats in typically Republican states. Starting with the George W. Bush presidency win in 2000, current voting trends manage to stay very similar with Republican states being in the Midwest and Southern regions, while Democrats take up the votes in the Northeast and Pacific Coast. Many states around the Great Lakes such as Wisconsin, Michigan and Pennsylvania have traditionally been known as “swing states” in many elections with Donald Trump winning many of those states in 2016. When it comes to number of votes by state, two states with larger populations (California, New York) have typically been Democratic in recent years leading to a large amount of total votes for Democrats. However, the importance of total votes is minimized compared to the number of electoral seats gained.

Future Considerations and Limitations

With the Democrats taking back many of those swing states in the most recent election, inputting the 2020 election data would highlight where Democrats were successful in 2020 vs. in 2016. Another consideration would be to add the results since 1854, when the Republican Party was first formed as the major opposition to the Democratic Party.

Two data limitations within Tableau are the use of percentages, and the lack of projections. Tableau can show data in percentages, but only as a default if it is part of a Row % or Column % total. The data file was structured in a way where this was not possible, meaning that whole numbers were used with (%) labelled wherever necessary. Tableau also is not able to project in a geographic coordinate system without necessary conversions. For the purposes of this map, the default Web Mercator layout was used. One previous iteration of this map was also done as a cartogram hex map. However, a hex map may be better in a static map as the sizing and zooming is much more forgiving when using the default basemap.

A Pandemic in Review: a Trajectory of the Novel Coronavirus

Author: Swetha Salian

Geovisualization Project Assignment @SA8905, Fall 2020

Introduction to Covid-19

Covid-19 is a topic at the top of many of our minds right now, and has been the subject of discussion all around the world. There are various sources of information out there, and as with most current issues, while sources of legitimate information exist, there is also a great deal of misinformation that may be disseminated. This has lead me to investigate the topic further, and to explore the patterns of the disease, in an effort to understand what has transpired in the past year and where we may be headed, as we enter into the second year of this pandemic.

Let’s begin with where it started, what the trajectory has looked like over the past year, and where it is currently as the year is coming to a close. Covid-19 is a disease caused by the new Coronavirus called SARS-CoV-2. The first report was of ‘viral pneumonia’ in Wuhan, China on December 31, 2019 and spread to all the continents except Antarctica, causing widespread infections and deaths. Investigations are ongoing, but as with other coronaviruses, it is believed to be spread by large respiratory droplets containing the virus through person-person contact. In January 2020, the total number of cases across the globe numbered 37,907 and within five months, by June 2020, the number rose to 10,182,385. We currently sit at over 6 million cases across 202 countries and territories, as of November 2020. The numbers still appear to be on a rise even with a number of countries taking various initiatives and measures in an effort to curb to spread of the disease. The data, however, shows that the death rate has been declining in the past few weeks, with a total of 1,439,784 deaths globally as of today. This is a ratio of approximately 2% of cumulative deaths to the total number of cases.

Using Tableau desktop 2019.2, I created a time lapse map of weekly reported COVID-19 cases from January 1 to November 15. Additionally, there is a graph displaying weekly reported deaths for the same date range as mentioned earlier.

Link to my Tableau Public map: https://public.tableau.com/profile/swetha8500#!/vizhome/Salian_Swetha_Geoviz/Dashboard1

Data

I chose to acquire data from WHO (World Health Organization) because of the reputable research and their outreach globally. The global literature cited in the WHO COVID-19 database is updated daily from searches of bibliographic databases, hand searching, and the addition of other expert-referred scientific articles.

The data for this project is a .csv file that has a list of new & cumulative cases, new and cumulative deaths, sorted by country and reported date from January 1 through November 15. This list consists of data from 236 countries, territories and areas and a total of 72966 data entries for the year. For my analysis, I had a time lapse map of cases for the year, for which I used Cumulative_cases column. For the graphs representing weekly death count as well as top 10 countries by death count, I used New_deaths column.

Creating a Dashboard in Tableau Desktop

Tableau is a data visualization software which is fairly easy to use with minimum coding skills. It is also a great tool for importing large data and has the option for a variety of data to be imported as shown in the image below.

The .csv file imported opens up on the Data Source tab. There are options to open a New Worksheet and this is where we start creating all the visualizations separately and the last step would be to bring them all into a Dashboard tab.

In the side bar displayed on the left, there are Dimensions and Measures. Tableau is intelligent to generate longitude and latitude by country names. Rows and Columns are automatically filled in with coordinates when Country is added. In the Pages section, drag Date reported and this can be filtered by how you want to display the data, I chose weekly reported. In Marks section, drag and drop Category from Dimensions into Color and Cumulative Cases into Size and change the measure to sum.

By adding Date reported to Pages, it generates a Time Slider, which enables you to automatically play, choose a particular date and also set the speed setting to slow, medium or fast. The Category value generated a range for the number of cases reported weekly, which is what is shown as the changing colors on the map. Highlight country gives you an option to search for a particular country you want to view data for.

Create a new Dashboard and import the sheets that you have worked on and create a visual story. you have the option to add text, borders, background color, etc. to enhance the data.

As shown below, this is the static representation of the dashboard, which displays the weekly reported cases on the map and weekly reported deaths on the graph.

To publish to an online public portal follow the steps as shown below.

Limitations

As I was collecting data from the World Health Organization, I realized I couldn’t find comprehensive data on age groups and gender for cases or deaths. However, with the data I had, I was able to find a narrative for my story.

I had a hiccup while I was trying to publish to Tableau public from desktop. After creating an account online, I was getting an error on the desktop as shown below.

The solution to this is to go to the Data menu, scroll down to your data source, .csv files name in my case, and select Use Extract. Extracts are saved subsets of data that you can use to improve performance or to take advantage of Tableau functionality not available or supported in your original data. When you create an extract of your data, you can reduce the total amount of data by using filters and configuring other limits

Modelling Ontario Butterfly Populations using Citizen Science

Author Name: Emily Alvarez

Data Source: Toronto Entomologists Association (TEA), Statistics Canada

Project Link:

https://public.tableau.com/profile/emily6079#!/vizhome/ModellingOntarioButterflyPopulationsusingCitizenScience/Butterfly_Dashboard?publish=yes

Background:

Over the summer, I spotted multiple butterflies and caterpillars in my garden and became curious about what species may be present in my area and how that might change over time. Originally, I wanted to look at pollinators in general and their populations in Canada, but the data was not available for this. I reached out to the Toronto Entomologists Association (TEA) and fortunately, there was an abundant amount of butterfly population data gathered for the Ontario Butterfly Atlas. This atlas data comes from eButterfly records, iNaturalist records and BAMONA records, as well as records submitted by the public directly to TEA, therefore this data is collected by anyone who wants to submit observations. The organization had an interactive web-map (Figure 1), but this data still had more potential to be designed in a way that can engage both butterfly enthusiasts and the general public.

Figure 1: Ontario Butterfly Atlas Interactive Web Map

Technology

I chose Tableau as the platform to model this data on because it works efficiently with complex databases and large datasets. It is easy to sort and filter the data as well as perform operations (SUM, COUNT) as this was needed for some components of the dashboard. I have used Tableau in the past for simple data visualization but never for spatial data so I felt that using Tableau could be a learning experience as well as improving my skills on a software that I have used in the past.

Data & Methods:

I consulted with a contact at TEA who provided me with context on the data such as how it is gathered, missing gaps, and the annual seasonal summary on the data. Based on this information and after reviewing the dataset, I felt that there were 3 main components I could model about butterfly species in Ontario. Their location, number of yearly observations and their flight periods for adult populations. Because there was so much data, I focused on 2019 for the locational data and flight periods. There were some inconsistencies with how some of the data was recorded, mostly for number of adults observed since this was not always recorded as a numeric value, therefore any rows that did not have a numeric value were omitted from the dataset.

I chose to model the location of the species by census division because these divisions are not too small in area but are also general enough that it is easy to find the user’s location if they reside in Ontario. This resulted in a spatial join between the observation’s coordinates and the provincial census divisions’ geometry which allowed for a calculation of total sum of adults observed per census division which could also be filtered by species (Figure 2).

Figure 2: Census Division Map of Adult Butterfly Species

I modelled flight periods by month of observation of adult species because this seemed like an efficient way for the user to find when species are in their flight periods (Figure 3). Some enthusiasts may prefer this data to be modelled by month-thirds instead, but I felt that because I wanted this dashboard to be for both butterfly enthusiasts and the general public, I thought modelling by month may be easier for the user to interpret. I decided to also show this by census division because the circle size helps indicate where observations are most popular and how that compares to other census divisions. The user also has a choice to sort by census division and only visualize the flight period for that particular census division.

I modelled yearly observations starting from 2010 because submitted observations began to increase during this time due to more accessibility to online services for submissions, although data exists from the 1800s (Figure 4). This data also could only be filtered by species and not census division because this dataset with all of the observations is too big for the spatial join and caused issues with data extraction that Tableau requires for workbooks to post online.

Figure 4: Yearly Observations for all Census Divisions

Limitations and Future Work:

One of the biggest limitations to this dataset is the lack of observations in the northern regions compared to the southern. Because there is a lower population and less accessibility to a lot of areas, there are few submitted observations here, therefore the dataset does not capture the whole picture of Ontario.
Another limitation is that because this is citizen science-based data, there is some inconsistency with some data entry, as an example, the Adult populations were not always recorded numerically but sometimes with text or unclear values such as “a few, many, >100” which resulted in these observations not being modelled because they could not be properly quantified.
Another limitation is that the yearly observations cannot be sorted by census division. Because this contains such a large dataset, to conduct the spatial join with the census division polygons caused issues with data extraction and publishing the workbook. Therefore, this component can only be sorted by species.
The last biggest limitation to the dashboard is the way flight periods are modelled. Butterfly enthusiasts may prefer to look at flight periods within a smaller scale than months and prefer month-thirds. A future addition to this dashboard could include a toggle that allows you to switch between looking at flight period by month or month-thirds instead.

How Does Canada Generate Electricity?

by Arthur Tong

GeoVisualization Project @RyersonGeo, SA8905, FALL 2020

Project Weblink (Click Here)

INTRODUCTION

Getting electricity to a country’s homes, different types of buildings and industries is an extremely challenging task, especially for countries that are enourmous in land area; transporting power over long distances are much more difficult. Up to now, the produced electrical energy is either very inconvenient to store or expensive, and with the increasing demand over the years in Canada, balancing betwen two in real time is crucial.

The way how electricity is generated solely depends on what kind of technologies and fuels are avaiable by that area. According to Natural Resources Canada (2020), “the most important energy source in Canada is moving water , which accounts for 59.3% of electricty supply, making it the second largest producer of hydroelectricity in the world with over 378 tearwatt hours in 2014.”

The goal of this interactive map project is to view most of the power plants in Canada and their respective sources and generating capacties (MW), which are proportional to the size of the circles shown in the project weblink above.

METHODOLOGY

In this section, I will be introducing the methdology for conducting this project. I would first describe how the data was collected, then followed by steps needed to produce the final dashboard with Tableau Public.

Data Collection

For the purpose of this study, I would need to retrieve pin-point (latitude/longitude) location of all types of power plants across Canada: from primary energy like nuclear energy and the renewables, to secondary energy that are produced from primary energy commodities like coal, natural gas and diesel. I tried looking up on various sources like Open Government Portal, but most of the open data they provide does not necessarily contain the power plants’ exact location.

Therefore, I had to manually pin-point all the data from external sources, mostly based on these two websites Global Energy Observatory (GEO) and The Wind Power. Other projects were identified by looking up on either the publicly/privately owned electricity utility company’s websites for all the provinces, for example BC Hydro, Ontario Hydro, TransAlta, etc, and their relative coordinates were retrieved using google maps. A similar interactive map “Electricity Generating Stations in British Columbia Map” has been done by researchers from University of Victoria, which provided most of the data for British Columbia and framework on what other relevant data I would like to include for my other provinces (as shown in the figure below).

Figure 1: Snapshot of the columns included for the dataset.

In addition, all 13 provinces were accounted and a total of 612 points were collected manually.

Construction of Tableau Dashboard

Tableau Public is the software used for this project. First, load in the excel data into Tableau through Data->Open New Data Source-> Microsoft Excel. Here, make sure the latitude and longitude columns were assigned a Geographic role as shown in the snapshot below, so they could be used to map the data.

Figure 2: Snapshot showcasing the Geographic roles assigned to the Latitude and Longitude columns.

From the new worksheet screen, sections on the left corresponds to the columns of the table. Drag the non-generated latitude and longitude to columns and rows and choose the ‘symbol map’ under ‘show me’ on top right. If the ‘unknown locations’ tab pop-up from the bottom right, it means that Tableau was not able to automatically align the name of the provinces given to the column to their database, which can be simply fixed by clicking that tab and manually edit the unknown locations. After dragging in essential elements you want to present, it would look something like this as shown in the figure below. In addition, the base map can also be changed into a dark theme under Map->Background Maps.

Figure 3: Taleau Interactive Map Layout. ‘Source’ is presented by differnet colours while their ‘capacity’ is presented by the sizes of the circles.

Moving on, to create a bar/pie chart, hover the bar on the left to choose which graph would best visualize the data you are trying present, then drag essential data into columns/rows.

Figure 4: Bar graph showing “Total capacity by all provinces”.

Last but not least, add a new ‘dashboard’ sheet and drag in all the maps/graphs into the dashboard to be the final product. Organizing the layout in the dashboard could be frustrating without the proper frame, you may also consider making elements like the filters and smaller graphs into a ‘float’ item by right clicking it, so that those ‘floating’ items could be placed on top of other elements on the dashbaord; in this case, I made the bar graph ‘floating’ so it is layed on top of the interactive map.

RESULTS & LIMITATIONS

Hydroelectricty do contribute to 56.67% of electricity generation across the country, followed by natural gas (12.39%) and nuclear energy (11.29%). However, a lot of electricity generation in Alberta are still based on coal, which takes up to 46.21% of the total capacity in that province.

Since all the data were collected manually, they may not be 100% accurate but the idea is to have a sense on where approximately it is located. For example, one single wind farm containing ten wind turbines may consist a large space across the mountain/field, the data collected was based on one wind turbine instead of plotting all ten of them.

Moreover, less developed provinces like the Northwest Territories has a very low amount of electricity generated due to its lower population (one diesel power plant per small town located using google satellite), there could have been more power plants around the area.

In conclusion, precise and consistent data is lacking for all the provinces from open data source portal, creating a potential for future similar studies carried out if more data is allowed. A time line perspective could also be added to this interactive map as well, so as users drag along the bar they can see the change in different types of powerplants that were being built in different locations.

A Glimpse of Short Term Rentals in Calgary Using Tableau

by Bryan Willis
Geovis Project Assignment @RyersonGeo, SA8905, Fall 2020

Project link – https://thebryanwillis.github.io/CalgaryShortTermRentals.html

Background

Over the years, many homeowners have decided to turn their place of residence into short term rentals, allowing their place of residence to be rented out for short periods of time. Short term rentals have also seen an increase in popularity due to their better pricing when compared with hotels and the unique neighbourhood characteristics it provides. Although Calgary has not seen the increase of short term rentals as dramatics as that of Toronto and Vancouver, Calgary has continued to see growth in the short term rental supply. The City of Calgary defines a short term rental as a place of residence that provides temporary accommodation and lodging for up to 30 days and all short term rentals in Calgary must legally obtain a business license to run.

This interactive dashboard will aim to highlight some key components related to short term rentals in Calgary such as the locations, the license status, the composition of the housing type and licenses per month

Data

The data used in this dashboard is based off of the Short Term Rentals data set which was acquired through the City of Calgary’s Open Data Portal.

Methods

Data Cleaning – After downloading the data from the open data portal, the data needed to be cleaned for it to properly display the attributes we want. All rows containing NULL values were removed from the data set via MS Excel.
Map Production – After importing the cleaned data into Tableau, we should quickly be able to create our map that shows where the locations of the short term rentals are. To do this, drag both the auto generated into the middle of the sheet which should automatically generate a map with the location points. To differentiate LICENSED and CANCELLED points, drag the License Status column into the ‘Color’ box.

Monthly Line Graph – To produce the line graph that shows the number of licenses produced by month, drag into the COLUMN section at the top and right click on it and select MONTH. For the ROWS section, again use but right click on it after dragging and select MEASURE and COUNT. Lastly, drag License Status into the ‘Color’ box.

City Quadrant Table – To create this table, we first need to create a new column value for the city quadrant. Right click the white space under ‘Tables’ and click on ‘Create Calculated Field’ which will bring up a new window. In the new window input RIGHT([Address],2) into the blank space. This code will create a new field with the last two letters in the Address field which is the quadrant. Once this field is created, drag it into the ROW section and drag it again into the ROW but this time right clicking it and clicking on Measure and then Count. Finish off by dragging License Status to the ‘Color’ box.

Dwelling Type Pie Chart – For the pie chart, first right click on the ROW section and click ‘New Calculation’. In the box, type in avg(0) to create a new ‘Mark’. There should now be an AGG(avg(0)) section under “Marks’, make sure the dropdown is selected at ‘Pie’. Then drag the Type of Residence column into the ‘Angle’ and ‘Color’ boxes. To further compute the percentage for each dwelling type, right click on the angle tab with the Type of Residence column in it then go the ‘Quick Table calculation’ and finally ‘Percent of Total’ .

Dashboard Creation – Once the above steps are complete, a dashboard can be made with the pieces by combining all 4 sheets in the Dashboard tab.

*Finalized dashboard with the 4 created components*

Limitations

The main limitations in this project comes from the data. Older licensing data is removed from the data set when the data set is updated daily by city staff. This presents the problem of not being able to compare full year to date data. As seen in the data set used in the dashboard, majority of the January data has already been removed from the data set with the except of January 26, 2020. Additionally, there were also quite a few entries in the data set that had null addresses which made it impossible to pinpoint where those addresses were. Lastly, as this data set is for 2020, the COVID-19 pandemic might have disrupted the amount of short term rentals being licensed due to both the city shifting priorities as well as more people staying home resulting in less vacant homes available for short term rentals.