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.

Health Care Access in the City of Toronto

By: Shabnam Sepehri

Geo-Visualization Project: SA8905, Fall 2020

Project Link: Final Map

Context

There are many factors that contribute to an individual’s access to health care. Statistics Canada has defined the ‘Social determinants of health and health inequalities’ as the 12 major factors that affect access. First on the list is income and social status and near the bottom at number 11 is race and ethnicity. For this project, I was curios to see how these two variables are distributed across the census tracts in the City of Toronto; and if there are any overlaps with the locations of healthcare institutions. The software of choice will be CARTO, which is a Service cloud computing platform that enables the visualization and analysis of geographic data.

Data Acquisition

CHASS Data Center: used to collect census data by census tract (2016): Total population, total visible minority population, total aboriginal identity population, and median total income;
Statistics Canada: used to obtain census tract boundary files;
City of Toronto Open Data: Address Point files;
Geospatial Map & Data Centre: used to collect physicians data – Enhanced Points of Interest 3.1 (City of Toronto)
ArcMap (digitize): used to digitize hospital locations in the City of Toronto

Process

After the data was acquired, the following steps were taken in ArcMap to organize the data before importing it into Carto. First, the census variables were joined to the census boundary shapefile. Then, a new column was created to calculate the sum of visible minority and aboriginal identity population density per 1,000 people.

Next, the hospital locations were digitized using the ‘Editor Toolbar’. following that, the physicians locations were geocoded using the using the address repository acquired from Toronto Open Source data. Lastly, , the non-spatial data (e.g. total median income) were joined to the spatial data (census tract boundaries) to enable the layer visualization. After all the necessary formatting was done, the data was uploaded onto Carto.

Once on Carto, I realized that the software allows the user to carry out different spatial functions such as geocoding. It also allows you to edit your dataset using SQL queries. This function is really useful in facilitating the data editing process and helps to reduce the back and forth between different mapping software’s.

Carto allows you to import a total of four layers in your map. The hospital locations, physician offices, and the census tracts were added as the four layers, with census tract uploaded twice to show the two different census variables. The census variables were visualized as a choropleth maps, and the health institutions were visualized as points on top of the choropleth layers.

Interactivity

The interactive aspect of this map is mainly the users ability to switch between layers and toggle each map component separately. Moreover, the ‘pop-up’ option was utilized for the hospital points to show the name and address of each location. Similarly, pop-ups were created for the choropleth maps to show the median income and population density of each individual census tract. Lastly, the widget feature was used to create histograms to showcase the distribution of the two census variables among the census tracts. This feature allows the user to select for the tracts in different categories and zoom into those specific tracts on the map. For instance, someone may want to look at tracts with the highest median income and tracts with an average aboriginal and visible minority population density. Lastly, the choropleth layers are turned off and may be switched on as per the user’s interest.

Insight

The map shows that census tracts where the median income is relatively high, tend to have a low distribution of aboriginal and visible minority population density. The distribution of hospitals appear to be uniform throughout the city with a few more concentrated in the downtown core. Conversely, the physician offices appear to be more concentrated in tracts with higher income or close to the downtown core. That being said, this does not mean that higher income groups have better access than lower income groups. However, the map does identify areas where there is a low number of physician offices, and most often, these areas tend to be classified as having a low to medium income. There are of course other variables that must be considered when identifying access, however, due to the limiting number of layers this option was not feasible for this project. Overall, this map can be used to identify ideal locations for future health facilities and to identify groups that have limited access to these resources.

Limitations & Future Work

Initially, I wanted to include more variables in the map; the goal was to map median income, visible minority & Aboriginal population density, education attainment, and employment conditions. However, Carto only allows for the addition of four variables. This limited the diversity of the visualized variables. Ideally, exploring other geo-visualization software such as Tableau, ArcGIS Online, or the Esri Dashboard would aid in creating a more nuanced map.

Ideally, I would also want to map the change of these variables over time. For instance, to show whether the distribution of median income, and visible minority & Aboriginal population density per census tract has always been the same or if there are slight changes in pattern. It would be interesting to capture which census tracts had experienced better access due to changes in health determinants over time.

Finding Your Ideal Toronto Neighbourhood

By Marian Mendoza
Geovisualization Project, @RyersonGeo, SA8905, Fall 2020

Project link: here. Best viewed in full screen.

Background

Toronto is a diverse, exciting city with plenty to offer. When moving to a new city or finding a new neighbourhood to live in, it can be challenging to decide on what neighbourhood is best for you. This tool helps users filter their desired criteria based on a number of selected features.

Data

Data for this project is collected from the following sources:

City of Toronto Open Data Portal for neighbourhood and green space shapefiles
Walkscore.com for Walk, transit, and bike scores from Walkscore.com
Metrolinx for subway stations (filtered from shapefile of Regional Transit Network).
Google Places API for museum/gallery, and mall (major shopping centres and district malls)

Methods

1. Collecting Google Places API data.
Querying the Google Places API returns a maximum of 60 results (as per Google’s Terms and Conditions). This query returns a list of results with extra features in a .json format. The desired results, specifically the name, point location, and address, are then reformatted into a usable .csv to be used in ArcGIS.

2. Data cleaning
All files were cleaned to retain only relevant fields for this geovisualization. Some neighbourhood names had changed since the walkscore.com data was published, so this was cross referenced with the City of Toronto’s current neighbourhood names. Further, Google Places API returned some misclassified or duplicated results that had to be removed from the list.

3. Map preparation on ArcGIS Pro.
– Walk/bike/transit scores were joined to the neighbourhood shapefile using the common area name.
– All other features were counted in each neighbourhood using “Summarize Within.” – For green_space, this returned the total area of green space in a neighbourhood. This was then computed as a percentage of the total area, producing the proportion of green space for neighbourhood. Next, the proportions were classified into 6 quantiles of “green levels” in the city.

4. Creating Widgets on ArcGIS Online Web App Builder + Using the Web App

All layers were uploaded to ArcGIS online and used as the web map for the web app builder. Several widgets were created to enable a user-interactive experience. Users are welcomed by a splash screen that explains how to use the app’s key functions.

FILTER. Since all features were joined to the neighbourhood layer, I created a filter widget that allows users to input values for any of the features. Most of these queries are set to “at least” since people who want higher values would be more selective. People who are not as particular about a feature can keep it at a low setting.

QUERIES. Several pre-set queries allow the user to see neighbourhoods with the top features in each category. The user can also engage further with the map layers by toggling on/off additional relevant features. For example, by querying “top transit scores” the user can then turn on the SUBWAY STATION layer and see where stations are in the city. They can also turn on “transit score” and see a choropleth map of transit scores across the city. This enables a richer understanding of the results of the queries.

Query for neighbourhoods with top transit scores, in descending order

CHARTS. The charts widget enables users to see a graphical representation of some of the features (scores and green space) and compare neighbourhoods.

Users have the option to use a spatial filter to limit the chart display to only some neighbourhoods. In this case, I zoomed into southwestern Toronto. The result is a bar graph where you can quickly compare the values for neighbourhoods in the set extent.

Chart comparing green space % in southwestern Toronto

Additional features in the web app:

SEARCH. Users can search for a place or an address and engage with any of the layers to see the features of that point’s neighbourhood.

TRANSPARENCY. All layers have modifiable transparency. Users can layer several features and choropleth maps and identify neighbourhoods that may be most ideal based on the polygon’s saturation. For example, layering both walk score and transit score would show the darkest areas to be the most walkable and transit friendly.

ATTRIBUTE TABLE. All layers’ attribute tables are viewable for a user who would be interested in seeing the full details of each layer and use functions like “sort descending.” This is accessible in the “more details” menu for each layer, as well as the black tab in the bottom centre of the screen.

Limitations

Originally, real estate data (such as rental prices) were to be included, but open data from the Canada Mortgage and Housing Corporation were not clean and complete for each Toronto neighbourhood. Additionally, restaurant, café, schools, and libraries were to be included, as these are some attractive neighbourhood features. However, due to Google Places API restriction of 60 requests, I decided to use smaller data. Alternatively, I could have used Nominatim API to pull more search requests from OpenStreetMap, but with time constraints I kept the scope of the project small.

There are limitations in using the widgets of ArcGIS Web AppBuilder. I would have liked the queries to display the “top results” with the colours assigned from the choropleth source layer. Instead, all results of a query are displayed with the same symbology. There is no option to group layers on ArcGIS Online; ideally, there would be groups for “feature layers” (subway station, mall, green space) and “classified layers” (walk score, green space %) that would help the user navigate the layers more simply.

Further development

Halfway through completing this project, I learned that Toronto Life magazine had created a similar tool. However, their tool scores each neighbourhood on an index and is interactive by using sliders to input the users ranking. The Toronto Life tool does not allow the user to see the details of the index score for each neighbourhood, such as, identifying the types and locations of shopping experiences in the neighbourhood.

The Toronto Life tool gave me ideas on how to improve my tool. With more time and experience, I would create the app using Javascript to avoid the limitations of ArcGIS Online’s widget functionalities. I would expand on the filter function and allow the user to weight each feature, then return the results ranked by the best neighbourhood based on the criteria. Further, additional neighbourhood qualities not included in this project, such as housing affordability, building types, restaurants, and job opportunities, are complex datasets that would improve the comprehensiveness of this tool. I would use open data from OpenStreetMap to include features with more than 60 records. I would also improve the complexity of each feature’s relationship with other features (such as weighing a feature’s attractiveness to a neighbourhood by assessing its walking/transit/driving distance).

With more resources and access to clean and complete datasets, this tool can be expanded for broader use. Casual users can benefit from this tool, but it can also be used more precisely to complement real estate or housing research.

Visualizing Atlantic Tropical Storm Activity

by Christopher Rudolph

Hurricane Florence | NASA — *Fig 1. Hurricane Florence as recorded by NASA*

Tropical storms are a category of weather events that create wind and rainfall conditions of varying intensity. These conditions can have high destructive potential depending on intensity, with these storms being classified from tropical depression at the weakest, to hurricane at the most intense. They occur between 5- and 20-degrees latitude when low atmospheric pressure systems cross warm ocean surface temperatures. Depending on conditions, winds can develop from as low as 23 mph to over 157 mph. When these storms meet land, they will often cause property damage and threaten lives due to flooding and wind force before dissipating.

The most dangerous of these storms are classified as hurricanes, which are characterized by exceedingly high wind speeds. Hurricanes are famed across the south-eastern United States for the devastating effects they can have when they reach land such as 2005’s Hurricane Katrina with over $125 billion in damage and over 1800 deaths or 2012’s Hurricane Sandy with $70 Billion in damage and 233 deaths. Due to this, the study and prediction of tropical storm development has remained continually relevant.

Why track tropical storms?

Many of the processes surrounding hurricane development are poorly understood, such as ocean and atmospheric circulation. To better understand these events, efforts have been made to form detailed histories of past tropical storm conditions. The National Oceanic and Atmospheric Administration (NOAA) has created detailed records of tropical storms as far back as the mid 1800’s.

The atmosphere and ocean are 2 of the largest carbon and thermal sinks on Earth. With anthropogenic climate change changing the conditions of these two bodies, there is concern that tropical storm development will change with it, potentially with intensification of these destructive events. A search for periods analogous to forecasted future conditions has emerged in an attempt to predict how tropical storm conditions may change. Paleotempestology is a scientific field that has sought to extend tropical storm records past modern monitoring technology using geological proxies and historical documentary records.

This visualization will represent the frequency of tropical storm activity in the Atlantic as a heat map. Kernel Density values are assigned based on proximity to tropical storm path activity. The higher the value, the more tropical storm activity seen in proximity to the location. Kernel density will be visualized on a 10-year basis, helping to visualize how storm activity over time and the frequency at which these storms may impact coastal communities.

Visualization

Fig. 2 Visualization of tropical storm activity density in the west Atlantic.

Data and Platform

For this project, tropical storm data is visualized using the International Best Track Archive for Climate Stewardship (IBTrACS), a tropical cyclone best track data collection published by NOAA.

ARCGis was selected as the platform that would be used for the visualization. The software was familiar and effective for doing the project’s geoprocessing, and looked promising for the visualization product. ARCGis features robust geoprocessing tools for creating the visualization, and has an animation feature that can produce the video format and implement overlay features such as a timeline and text. As the project developed, the animation tool would be abandoned however in favor of Windows Video Editor for the video as discussed later.

Methods

With data available in shapefile form, importing NOAA’s data into ARCGis was simple. The data on display upon importation is overwhelming with over 120 thousand records displayed as travel paths. Performance is low and there is little to no context to what is being viewed.

Fig. 3 – A map of all tropical storm tracks recorded

Using density geoprocessing and the filtering of data range through time, this will be transformed into something interpretable.

Time

Time was the first filter implemented. In the properties of the layer, time was enabled. Each row has corresponding time fields. In this case, year was used. Implementing this introduced an adjustable filter to the map area in the top right. This slider could be adjusted to narrow down the range.

Fig. 4 – Layer Time properties and the resulting time range filter

While handy on the fly, more precise results for filtering time is found within the Map Time tab, with precision controls available there.

Creating the density view

For creating heatmaps typically the heatmap symbology option is used to create effective density views with time enabled filtering. For this visualization, this approach was not available as the approach was incompatible with the line datatype used. To create a density map, geoprocessing would need to be done using the density toolset. The kernel density toolset was selected. This tool uses a bivariate kernel function for form a weight range surrounding each point. These ranges are then summed to form cell density values for each raster grid point, resulting in a heatmap.

This approach carried some issues for implementation however. In the process of geoprocessing, the tool doesn’t take into account or assign any time data to the output. This meant that the processed layer couldn’t be effectively filtered for the visualization. To work around this, the data was broken into layers by desired year range, then processed, creating a layer for each time window. These layers could still be used to make keyframes and scenes for the animation, though this solution would have some added housekeeping in displaying certain details such as time and legend within the video

Format

As mentioned earlier, the ARCGis animation tools were planned for use as the delivery format. Working with the results generated so far would prove problematic however. The animation tool is focused on applications involving changes of view and time. Given the needs and constraints of the solutions taken for this project, neither of these would be active components of this visualization, and would complicate the creation of the animation. Issues with preview playback, overlays and exports further complicated this. Given the relatively simple needs, a different approach using other software was selected.

In researching this topic, much forum discussion was found surrounding similar projects. Consensus seemed to be that for a visualization using static views such as this, exporting to an external main-stream video-processing platform would be most effective. To do this, each time view would need to be honed and exported as images through a layout. These layouts would then be arranged into a video with windows video editor.

Elements such as legend, title and attribution that had been causing issues under the animation tool were added to a layout. They automatically updated relevant information as layers were swapped within the layout view. Each layer in turn were exported as layouts representing each year range. Once these images were created, they were imported into windows video editor where they were composed into a timeline. Each layout was given period of 3 seconds before it would transition to the next layout. The video was then exported in 1080p and published to Youtube. Once hosted on Youtube, it can be easily embedded into a site like above or shared via link.

Fig. 5 – Video editing in Windows Video Editor

Future Work

There are different factors and semi-regular phenomenon that have impacts on tropical storm development. Events such as El Nino and the Pacific Decadal Oscillation are recurring events that could enhance. Relating the timeline of these events as well as ocean surface temperature could help interpret trends within this visualization. Creating a methodology behind time ranges displayed also could have enhanced this visualization. For example, breaking this visualization into phases of El Nino-Southern Oscillation rather than even time windows may have presented a lot of value to this sort of visualization.

Toronto’s Waterfront Parking Lot Transformation

Author Name: Vera Usherovich

StoryMap Project link: https://arcg.is/004vSb

SA 8905 Fall 2020

Introduction:

During one of my study breaks, I was looking at aerial photographs of Toronto’s Waterfront. One thing in particular caught my attention; the parking lots. I did not grow up in Toronto and had no idea how drastically different the waterfront area looked like. I kept on opening up images from various years and comparing the changes. The Waterfront area was different; at first the roundhouses disappeared and followed by parking lots and industrial warehouses. This is the short answer to what inspired this StoryMap. I wanted to see how the surface of our city changed over time, specifically the role of parking lots.

Key Findings

There has been a 32 % reduction in surfaces dedicated to parking lots between 2003 and 2019.
Even though there are fewer parking lots, there is a similar proportion of parking lot size surfaced between 2003-2019.
Many of the parking lots in the entertainment district turned into condos.

About the StoryMap

Data

For this project, I used areal photographs from the City of Toronto, works and Emergency Services. I chose 2003 and 2019 as my years to compare.

Platform and Method

The digitization process was done through Esri’s software, ArcMap. I then exported the layers into gis online and made a map. this map was embedded into the StoryMap with adjustment to the layers. Additionally, I cross-referenced information with google maps, to identify what has replaced the parking lots (broke into 4 categories: residential, commercial, public, and other).

Limitations

Note: The data showcased in this story and maps is based on manual aerial photograph digitization. Some features might have been inadvertently missed or incorrectly categorized.

Future Work

This can be done for a wider range of years. Also, a more comprehensive classification of what is no longer a parking lot could be described in greater detail.

Viewing Coffee Chains in Canada and mainland USA from Space

By Thushal Karunamuni

SA8905 – Geo-visualization Project

Link to map animation: Youtube Video

“Space: the final frontier,” a famous quote from the ever so classic and captivating TV Series, “Star Trek” was the inspiration for this map animation. This favourite TV series that I had been watching since the age of 8 has made me strive to create a globe with attributes mapped to it. In many episodes, as the Star Trek Enterprise approached a planet, the onboard Starfleet crew always reported to the Commander, Captain James T. Kirk about their findings by sometimes mapping and investigating observations on the planet before teleporting themselves to explore the planet. Hence, this time, I wanted to map popular coffee chains such as Tim Hortons and Starbucks in Canada and the mainland US just to get a glimpse of the spatial distribution of coffee stores. Now, I imagined myself to be in the fictional Starfleet to report my findings to the commander if he was interested in landing on Earth to try out some coffee. Thus, this blog dwells into the use of Adobe After Effects to make planet Earth with mapped point data of coffee locations. Interestingly, this is the same software used by VFX artists like Andrew Kramer to make Star Trek Into Darkness’s movie titles.

Tutorial

The animated map creation was extremely fun. The first challenge was to collect data from ESRI’s ArcGIS Business Analyst for each coffee chain using the ‘Business and Facilities Search.’ This grabs all the data you would need for the project. Then, the data would have to be saved online and downloaded to ArcGIS Pro. Ensure that you are using the Plate Carée projection. Save each individual coffee chain as an AIX file as that can be opened with Adobe Illustrator. Further, open the AIX file in Illustrator (make sure to have ArcGIS for Adobe CC) with an ArcGIS account and load the map data and point files. You may create a blur or brightness that is then exported as a jpeg file. The time consuming part of the project is when it comes to using Adobe After Effects. This is the core of the project where the video file is developed. Install the Video Copilot Orb plug-in into After Effects and the earth textures as this will be essential to creating the video file. The website consists of a tutorial that can be followed to map the data onto the globe. However, what the tutorial doesn’t teach is to map many attributes onto the globe. The trick is simple. Basically, many globes each containing the attribute of interest as seen in Figure 2. After each timeframe, ensure that you place the globe and that it appears on the map animation but the viewer wouldn’t know that you made multiple globes! Furthermore, Figure 3 shows that you need to insert the jpeg file from Illustrator into the illumination layer that maps the coffee layer point data for you. The video can be timed according to the audience or based on how much time you have to present. I kept my video under 2 minutes. Once you are satisfied with the output of your video, export the video using Adobe Media Encoder and export as a Youtube 1080p video. Last, import the video into Adobe Premiere Pro.

Adobe Premiere Pro is a video editing software used by many vloggers and film professionals to produce digital media online. You may add specialized glitch texts that I found on Motion Array. Don’t forget to add some music too! Since I’m uploading this video to my Youtube channel, I would have to use music that is not copyrighted content just to be safe.

Limitations

If I had all the equipment available to me to make this project as best as possible, it would be a PC with lots of RAM and a good graphics card. This way I could preview the video in detail before publishing. I’m limited by low resolution to preview the video. Moreover, with the ever changing digital media that uses VR technology, I would like to use 3D friendly plugins that lets the viewer watch the video using 3D glasses which would be spectacular. I hope you enjoy my video!

Toronto Raptors 2019-2020 Atlas

Atlas Web App Link

Geovisualization Project, @RyersonGeo, SA8905, Fall 2020

By Nicolas Karwowski,

Background

Sports have always been a common ground that brings together people of different ethnic and cultural backgrounds. However, regardless of the sport at hand, issues of racism have always accompanied athletic competition. With the reignition of the Black Lives Matter movement in 2020, sports teams and their players had the chance to take a stand against racism in a bigger way than ever before. In my household, we’ve followed the Toronto Raptors basketball team for many years. With this following, we have come to learn the stories of not only our own team’s players but also those of their competitors. I can reliably say that I have spent many hours learning about the hardships and the achievements of athletes from across the globe. While I do not regret spending so much time learning about the diversity of this sport, I understand that others may not have this time. This is why I have chosen to create a geovisualization that represents the complexity of an athlete’s journey to the top and the diversity of my favourite televised sport.

Data

Thankfully most widely known athletes have easily available information about their journey to the NBA. This usually includes the place they were born, the various schools they attended to hone their skills, and the professional teams they may have played for before reaching the big league. While in-depth bios exist for each team’s core roster on NBA.com, Wikipedia gives concise summaries of player’s geographic movements throughout their lives. As seen on the right, these summaries can include the player’s date of birth, place of birth, the high schools they attended and their respective locations, the college(s) they attended as well as NBA and other professional teams they might have played on. This information would be vital in the creation of points summarizing the player’s journey to athletic stardom. The main limitation of this information source is that like with other Wikipedia pages, the information could be incorrectly added, sometimes with malicious intent. Lesser known players may also have missing information. Since some players had up to 10 different locations credited in their summaries, I chose to include only ten players from the Raptor’s championship-winning 2019-2020 roster. The mention of the roster’s season is of importance as players come and go as the years go on.

Methods

For each player in the visualization, I created a new point feature class in ArcGIS Pro. I entered the points in chronological order so that when I had to connect them later with arrows, the arrows would indicate their path from place to place. Due to the number of points, their accuracy was usually only tied to the listed city’s geographical location. With all the feature classes created, I then added a few new attribute columns so that points in the final visualization could include context.

These attributes included the name of the city, the name of the country the city was located in, the type of location with regards to the player’s life (Place of Birth, School, Professional Team, NBA Team) and the player associated with that point. With this completed, I could link each of the points within feature classes using the Points To Line tool in ArcGIS. The subsequent use of the Split Line at Vertice tools was then done so that arrows could be created between points and not just at the end of the line. Using the Feature Class to Shapefile tool, I was able to export all 20 of the shapefiles, half of which were points and the other half being lines, to ArcGIS Online.

While ArcGIS Online lacks much of the symbology customization available in the desktop version, I made do with a set of simple icons. A green circle would represent the player’s place of birth, schools would be shown as blue diamonds, orange squares symbolized professional basketball and purple stars depicted NBA teams. This type of symbology allows users to understand a player’s journey to the NBA including their ups and downs.

In the final geovisualization web app, users have the ability to customize the map to whatever level they would like. If a user wishes to see where all players are born, they have the ability to turn off all layers except for the Place Of Birth layer. Clicking on any of the remaining green circles creates a pop-up that gives details on what city is there, who is born there and when they were born (as seen above). Alternatively, any and all player layers can be hidden if a user would like to focus on a single-player (as seen below).

Future work

The project as it stands only encapsulates a minuscule sample of all athletes in the NBA. Ideally, this geovisualization would enable to not only view all the Toronto Raptors players but all the players that have ever played in the NBA. When envisioning the perfect visualization, I imagine a crowdsourced app that allows anybody to add their favourite players to the app. Unfortunately, this app is non-editable and many of the steps involved required non-user-friendly applications. This visualization is also deeply limited by ArcGIS Online’s lack of symbology and UI options. These limitations include but are not limited to a search option for the layers manager as well as a grouping option for the symbology tab. Additionally, it would be very interesting to add a timeline feature in the app which allows people to see how the world of basketball has changed over the last few decades in a geographical sense.

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.