Tag: Tableau

100 Years of Wildfires in California – Tableau Dashboard Time Series

Shanice Rodrigues

GeoVis Project Assignment @RyersonGeo, SA8905, Fall 2020

Natural phenomenon can be challenging to map as they are dynamic through time and space. However, one solution is dynamic visualization itself through time series maps, which offered on Tableau. Through this application, an interactive dashboard can be created which can relay your data in various ways, including time series maps, graphs, text and graphics. If you are interested in creating a dashboard in Tableau with interactive time series and visuals, keep reading.

In this example, we will be creating a timeseries dashboard for the distribution of California’s wildfires over time. The overall dashboard can be viewed on Tableau Public HERE.

First, let’s go over the history of these wildfires which will present an interesting context for what we observe from these fires over time.

History of Wildfires

There is a rich, complicated history between civilization and wildfires. While indigenous communities found fires to be productive in producing soils rich in fertile ash ideal for crops, colonizers dismissed all fires as destructive phenomenon that needed to be extinguished. Especially with the massive fires in the early 1900s causing many fatalities, such as that in the Rocky Mountains that killed 85 people. The United States Forest Service (USFS) decided in implementing a severe fire suppression policy, requiring fires of 10 acres or less to be put out beginning in 1926, and then all fires to be put out by 10 A.M. the next day in 1935. It is expected that from the immediate extinction of fires in the early to mid-1900s, natural fire fuels such as forest debris continued to build up. This is likely the cause of massive fires that appeared in the late 1900s and persist to the current age which continue to be both difficult and expensive to manage. This pattern is obvious, as shown on the bar graph below for the number of fires and acres burned over the years (1919-2019).

Dashboard Creation

Data Importation

Many types of spatial files can be imported into Tableau such as shapefiles and KML files to create point, line or polygon maps. For our purposes, we will be extracting wildfire perimeter data from the Fire and Resource Assessment Program (FRAP) as linked here or on ArcGIS here.  This data displays fire perimeters dating back to 1878 up till the last calendar year, 2019 in California. Informative attribute data such as fire alarm dates, fire extinction dates, causes of fire and acre size of fires are included. While there is a file on prescribed burns, we will only be looking at the wildfire history file. The data imported into Tableau as a ‘Spatial file” where the perimeter polygons are automatically labelled as a geometry column by Tableau.

Timeseries

The data table is shown on the “Data Source” tab, where the table can be sorted by fields, edited or even joined to other data tables. The “Sheet” tabs are used to produce the maps or graphs individually that can all be added in the “Dashboard” table. First, we will create the wildfire time series for California. Conveniently, Tableau categorizes table columns by their data types, such as date, geometry, string text or integers. We can add column “Year” to the “Pages” card from which Tableau will use as the temporal reference for the time series.

The following timeseries toolbar will appear, where wildfire polygons will appear on the map depending on the year they occurred and is defined by the following scroll bar. The map can be shown as a looped animation with different speeds.

Additionally, the “Geometry” field can be added to the “Marks” card which are the wildfire perimeter polygons. Tableau has also generated “Longitude” and “Latitude” that are the total spatial extent of the wildfire geometries and can be added to the “Columns” and “Rows” tab.

In the upper-right “Show Me” table, the map icon can be selected to generate the base map.

Proportionally Sized Point Features

Multiple features can be added to this map to improve the visualization. First, the polygon areas appear to be very small and hard to see on the map above therefore it may be more effective to display them as point locations. In the “Marks” card, use the dropdown and select the ‘Shape” tab.

From the shape tab, there are multiple symbols to select from, or symbols can be uploaded from your computer into Tableau. Here, we chose a glowing point symbol to represent wildfire locations.

Additionally, to add more information to the points, such as proportional symbol sizes according to area burned (GIS ACRES field) by each fire. A new calculated field will have to be created for the point size magnitudes as shown:

The field is named “Area Burned (acres)” and is brought to the power of 10 so that the differences in magnitude between the wildfire points are noticeable and large enough on the map to be spotted, even at the lowest magnitude.

Tool Tip

Another informative feature to add to the points is the “Tool Tip,” or the attribute box about the feature that a reader has scrolled over. Often, attribute fields are already available in the data table to use in the tool tip such as fire names or the year of the fire. However, some fields need to be calculated such as the length of each wildfire. This can be calculated from the analysis tab as shown:

For the new field named “Fire Life Length (Days)” the following script was used:

Essentially this script finds the difference between the alarm date (when the fire started) and the contained date (when the fire ended) in unit “days.”  

For instance, here are some important attributes about each wildfire point that was added to the tool tip.

As shown, limitless options of formatting such as font, text size, and hovering options can be applied to the tool tip.

Graphics and Visualizations

The next aspects of the dashboard to incorporate would be the graphs to better inform the reader on the statistics of wildfire history. For the first graph, it will not only show the number of fires annually, but the acres burned as this will show the sizes of the fires.

Similarly to the map, the appropriate data fields need to be added to the columns and rows to generate a graph. Here the alarm date (start of the fire) is added to the x-axis, whereas the number of fires and Gis Acres (acres burned) was added to the y-axis and are filtered by “year.”

The field for the number of fires was a new field calculated with the following script:

Essentially, every row with a unique fire name is counted for every year under the “Alarm_Date” field to count the number of fires per year.

Another graph to be added to this dashboard is to inform the reader about the causes of fires and if they vary temporally. Tableau offers many novel ways of displaying mundane data into interesting visualizations that are both informative and appealing. Below is an example of a clustering graph, showing number of fires by cause against months over the entire timeseries. A colour gradient was added to provide more emphasis on causes that result in the most fires, displaying a bright yellow against less popular causes displayed with crimson red.

Similarly to the map, the “(Alarm_Date)” was added to the “Filters” card, however since we want to look at the average of causes per month rather than year, we can use the dropdown to change the date of interest to “MONTH.”

We also want to add the “Number of Fires” field to the “Marks” card to quantify how many fires are attributed to each cause. As shown, the same field can be added twice, such as one to edit its size attribute and one to edit its colour gradient attribute.

Putting it All Together

Finally, in the “Dashboard” tab, all these pages below of the timeseries map and graphs can be dragged and dropped into the viewer. The left toolbar can be used to import sheets into, change the extent size of the dashboard, as well as add/edit graphics and text.

Hopefully you’ve learned some of the basics of map and statistical visualizations that can be done in Tableau using this tutorial. If you’re interested in the history, recommendations and limits of this visualization, it is continued below.

Data Limitations and Recommendations

Firstly, with the wildfire data itself there are shortcomings, particularly that fires may have not been well documented prior to the mid-1900s due to the lack of observational technology. Additionally, only large fires were detected by surveyors whereas smaller fires were left unreported. With today’s technology in satellite imagery and LiDAR, fires of all sizes can be detected therefore it may appear that more fires of all sizes happen frequently in the modern age than prior. Besides the data, there are limitations with Tableau itself. First, the spatial data are all transformed to the spatial reference system WGS84 (EPSG:4326) when imported into Tableau and there can be inaccuracies of the spatial data through the system conversion. Therefore, it would be helpful for Tableau to utilize other reference systems and provide the user the choice to convert systems or not. Another limitation is with the proportional symbols for wildfires. The proportional symbol field had to be calculated and used had to be put to the “power of 10” to show up on the map, with no legend of the size range produced. It would be easier for Tableau to have a ‘Proportional Symbol” added onto the “Size” tab as this is a basic parameter required for many maps and would communicate the data easier to the reader. Hopefully Tableau can resolve these technical limitations to making mapping a more exclusive format that will work in visualizing many dataset types.

With gaps in wildfire history data for California, many recommendations can be made. While this visualization looked at the general number of fires per month by cause, it would be interesting to go in depth with climate or weather data, such as if there are an increasing number of thunderstorms or warmer summers that are sparking more fires in the 200s than the 1900s. Additionally, visualizing wildfire distributions with urban sprawl, such as if fires in range of urban centers or are more commonly in the range of people so are ranked as more serious hazards than those in the wilderness. Especially since the majority of wildfires are caused by people, it would be important to point out major camping groups and residential areas and their potential association with wildfires around them. Also, recalling the time since areas were last burned, as this can quantify the time regrowth has occurred for vegetation as well as the build-up of natural fuels which can then predict the size of future wildfires that can occur here if sparked. This is important for residential areas near these areas of high natural-fuel buildup and even insurance companies to locate large fire-prone areas. Overall, improving a visualization such as this requires the building of context surrounding it, such as filling in gaps of wildfire history through reviewing historical literature and surveying, as well as deriving data of wildfire risk using environmental and anthropogenic data.

Turbo Vs Snail

by Jazba Munir

The highways in Canada including the Trans Canada Highway (TCH) and the National Highways System (NHS), fall within provincial or territorial jurisdiction (Downs, 2004). The Greater Toronto Area (GTA) is surrounded by many of the 400 series highways. Some of the segments or between interchanges experience higher traffic volume than others (Downs, 2004). The traffic volume during certain hours such as morning rush hours (6:30 – 9:30) and evening rush hours (4:30- 6:30) results in traffic congestion. This traffic congestion is experienced on highway (Hwy) 401 that is the most “busiest” highway of North America. In 2016, City of Toronto Council approved the road tolls for Gardiner Express and Don Valley Parkway (DVP) to decrease the traffic volume and congestion on these two highways (Fraser, 2016). This proposal was not implemented; nonetheless, it can be visualized using Tableau that whether the speed improves by using the dataset to compare the toll route with non-toll route. The steps on the Tableau: The dataset used for visualization can be organized and clean using Microsoft Excel or Tableau. The speed data is retrieved in points form. For instance, each point has a x and y coordinates. The first step is to create field ID in order to connect each point (x, y) to next point (x, y); in order, to create the line of Hwy 401.The street , highways, routes layer provided by Tableau was used as a guideline to make sure that all the points are connected in a correct order (See Figure 1) .

Figure 1: The layer added into the map sheet

The x and y are converted from measures to dimensions since, the x and y default setting is measures. This change can be made by dragging and dropping x and y from measures to dimensions. Another way is putting longitude into columns and latitude in to rows (See Figure 2).

Figure 2: Columns and Rows for Longitude and Latitude.

The difference between the two is that dimensions are tools that are used to slice and describe data record whereas measures are values of those records that are aggregated. For further assistance please refer to: https://www.tableau.com/learn/training Once all the points appear on the map in tableau use the mark first selection to select line to connect the points (See Figure 3).

Figure 3: The option to connect the dots.

The speed data for any of the selected hwy can be placed in the colors and graduated color scheme from red to green is selected. In this color scheme red indicates minimum speed of 80km whereas green indicates maximum speed 120 km. These speeds were selected as standard to compare toll route with non-toll routes. These are some of the basics steps that are required for any spatial tableau project. The color, size, label and detail options can be selected to create the visualization much clearer (See Figure 4).

Figure 4: The option to add color, size and labels of the variables.

This shows the options for creating the comparison between the turbo vs snail. For further assistance please refer to: https://www.tableau.com/learn/training Once this is set up another sheet was added to include a graph component. The speeds can be organized by hour, minute, year, road (toll vs non-toll). The speed can be represented by using the color option. The speed on the map is represented with the red to green color gradient. The underneath map is layer map available through tableau (See Figure 5).

Figure 5: Showing the speed in color red to green.

This will indicate the difference between the speed at the different part of the hwy. All the other hwy’s appear in yellow to show insects of each hwy. The sheet 1 for map and sheet 2 with a graph are combined to create a dashboard. This dash board helps to visualize the graph and map at once. The filter for each sheet is combined to make space organized more space for the sheets (See Figure 6 and 7).

Figure 6: Showing the filters added into the dashboard.

For further assistance please refer to: https://www.tableau.com/learn/training The dashboard helps to know the speed and compare it based on the time and location. Based on the visualization, it can be concluded that toll routes have no congestion as the line is green. This indication is drawn based on the visualization. In contrast, the non-toll route appears red and light green for some sections. The color helps to know where the congestion occurs. Image 1:

Dashboard combining the two sheets In conclusion, the tableau visualization helps to compare between toll route vs non-toll route. Based on the dashboard, the toll route is turbo speed whereas the non-toll route are snails.

References https://www.brookings.edu/research/traffic-why-its-getting-worse-what-government-can-do/ https://www.cbc.ca/news/canada/toronto/city-council-meeting-road-tolls-1.3893884

Desperate Journeys

By Ibrahim T. Ghanem

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2019

Background:

Over the past 20 years, Asylum Seekers have invented many travel routes between Africa, Europe and Middle East in order be able to reach a country of Asylum. Many governmental and non-governmental provided information about those irregular travel routes used by Asylum Seekers. In this context, this geovisualization project aims at compiling and presenting two dimensions of this topic: (1) a comprehensive animated spider map presenting some of the travel routes between the above mentioned three geographic areas; (2) develop a dashboard that connects those routes to other statistics about refugees in a user-friendly interface. In that sense, the best software to fit the project is Tableau.

Data and Technology

Creation of Spider maps at Tableau is perfect for connecting hubs to surrounding point as it allows paths between many origins and destinations. Besides, it can comprehend multiple layers. Below is a description of the major steps for the creation of the animated map and dashboard.

Also, Dashboards are now very useful in combining different themes of data (i.e. pie-charts, graphs, and maps), and accordingly, they are used extensively in non-profit world to present data about a certain cause. The Geovisualiztion Project applied geocoding approach to come up with the animated map and the dashboard.

The Data used to create the project included the following:

-Origins and Destinations of Refugees

-Number of Refugees hosted by each country

-Count of Refugees arriving by Sea (2010-2015)

-Demographics of Refugees arriving by Sea – 2015

Below is a brief description of the steps followed to create the project

Step 1: Data Sources:

The data was collected from the below sources.

United Nations High Commissioner for Refugees, Human Rights Watch, Vox, InfoMigrants, The Geographical Association of UK, RefWorld, Broder Free Association for Human Rights, and Frontex Europa.

However, most of the data are not geocoded. Accordingly, Google Sheets was used in Geocoding 21 routes, and thereafter each Route was given a distinguishing ID and a short description of the route.

Step 2: Utilizing the Main Dataset:

Data is imported from an excel sheet. In order to compute a route, Tableau requires data about origins,and destination with latitude and longitude. In that aspect, the data contains different categories:

A-Route I.D. It is a unique path I.D. for each route of the 21 routes;

B-Order of Points: It is the order of stations travelled by refugees from their country of origin to country of Asylum;

C-Year: the year in which the route was invented;

D-Latitude/Longitude: it is the coordinates of the each station;

F-Country: It is the country hosting Refugees;

E- Population: Number of refugees hosted in each country.

Step 3: Building the Map View:

The map view was built by putting longitude in columns, latitude in rows, Route I.D. at details, and selecting the mark type as line. In order to enhance the layout, Oder of Points was added to Marks’ Path, and changing it to dimensions instead of SUM.  Finally, to bring stations of travel, another layer was added to by putting another longitude to columns, and changing it to Dual Axis. To create filtration by Route, and timeline by year, route was added Filter while year was added to page.

Step 4: Identifying Routes:

To differentiate routes from each other by distinct colours, the route column was added to colours, and the default setting was changed to Tableau 20. And Layer format wash changed to dark to have a contrast between the colours of the routes and the background.

Step 5: Editing the Map:

After finishing up with the map formation. A video was captured by QuickStart and edited by iMovie to be cropped and merged.

Step 6: Creating the Choropleth map and Symbology:

In another sheet, a set of excel data (obtained from UNHCR) was uploaded to create a Choropoleth map that would display number of refugees hosted by each country by year 2018. Count of refugees was added to columns while Country was added to rows. The Marks’ colour ramp of orange-gold, with 4 classes was added to indicate whether or not the country is hosting a significant number of refugees. Hovering over each country would display the name of the country and number of refugees it hosts.

Step 7: Statistical Graphs:

A pie-chart and a graph were added to display some other statistics related to count of Refugees arriving by Sea from Africa to Europe, and the demographics of those refugees arriving by sea. Demographics was added to label to display them on the charts.

Step 8: Creation of the Dashboard:

All four sheets were added in the dashboard section through dragging them into the layer view. To comprehend that amount of data explanation, size was selected as legal landscape. Title was given to the Dashboard as Desperate Journeys.

Limitations

A- Tableau does not allow the map creator to change the projection of the maps; thus, presentation of maps is limited. Below is a picture showing the final format of the dashboard:

B-Tableau has an online server that can host dashboard; nevertheless, it cannot publish animated maps. Thus, the animated maps is uploaded here a video. The below link can lead the viewer to the dashboard:

https://prod-useast-a.online.tableau.com/t/desperatejourneysgeovis/views/DesperateJourneys_IbrahimGhanem_Geoviz/DesperateJourneys/ibrahim.ghanem@ryerson.ca/23c4337a-dd99-4a1b-af2e-c9f683eab62a?:display_count=n&:showVizHome=n&:origin=viz_share_link

C-Due to unavailability of geocoded data, geocoding the routes of refugees’ migration consumed time to fine out the exact routes taken be refugees. These locations were based on the reports and maps released by the sources mentioned at the very beginning of the post.

The Toronto Financial Institution Market: Bridging the gap between Cartography and Analytics using Tableau

Nav Salooja

“Geovis Project Assignment @RyersonGeo, SA8905, Fall 2019”

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Introduction & Background

Banking in the 21st century has evolved significantly especially in the hyper competitive Canadian Market. Big banks nationally have a limited population and wealth share to capture given Canada’s small population and have been active in innovating their retail footprint. In this case study, TD Bank is the point of interest given its large branch network footprint in the Toronto CMA. Within the City of Toronto the bank has 144 branches and is used as the study area for the dashboard created.  The dashboard analyzes the market potential, branch network distribution, banking product recommendations and client insights to help derive analytics through a centralized and interactive data visualization tool.

Technology

The technology selected for the geovisualization component is Tableau given its friendly user interface, mapping capabilities, data manipulation and an overall excellent visualization experience. However, Alteryx was widely used for the build out of the datasets that run in Tableau. As the data was extracted from various different sources, spatial element and combining datasets was all done in Alteryx. The data extracted for Expenditure, Income and Dwelling Composition was merged and indexed in Alteryx. The TD Branches was web scrapped live from the Branch Locator and the trading areas (1.5KM Buffers) are also created in Alteryx. The software is also used for all the statistical functions such as the indexed data points in the workbook were all created in Alteryx. The geovisualization component is all created within the Tableau workbooks as multiple sheets are leverged to create an interactive dashboard for full end user development and results.

Figure 1 represents the Alteryx Workflow used to build the Market, Branch and Trade Area datasets
Figure 2 provides the build out of the final data sets to fully manipulate the data to be Tableau prepared

Data Overview

There are several data sets used to build the multiple sheets in the tableau workbook which range from Environics Expenditure Data, Census Data and webscrapped TD branch locations. In addition to these data sets, a client and trade area geography file was also created. The clients dataset was generated by leveraging a random name and Toronto address generator and those clients were then profiled to their corresponding market. The data collected ranges from a wide variety of sources and geographic extents to provide a fully functional view of the banking industry. This begins by extracting and analyzing the TD Branches and their respective trade areas. The trading areas are created based on a limited buffer representing the immediate market opportunity for the respective branches. Average Income and Dwelling composition variables are then used at the Dissemination Area (DA) geography from the 2016 Census. Although income is represented as an actual dollar value, all market demographics are analyzed and indexed against Toronto CMA averages. As such these datasets combined with Market, Client and TD level data provide the full conceptual framework for this dashboard.

Tables & Visualization Overview

Given the structure of the datasets, six total tables are utilized to combine and work with the data to provide the appropriate visualization. The first two tables are the branch level datasets which begin with the geographic location of the branches in the City of Toronto. This is a point file taken from the TD store locator with fundamental information about the branch name and location attributes. There is a second table created which analyzes the performance of these branches in respect to their client acquisition over a pre-determined timeframe.

Figure three is a visualization of the first table used and the distribution of the Branch Network within the market

The third table used consists of client level information selected from ‘frequent’ clients (clients transacting at branches 20+ times in a year. Their information builds on the respective geography and identifies who and where the client resides along with critical information that is usable for the bank to run some level of statistical analytics. The client table shows the exact location of those frequent clients, their names, unique identifiers, their preferred branch, current location, average incomes, property/dwelling value and mortgage payments the bank collects. This table is then combined to understand the client demographic and wealth opportunity from these frequent clients at the respective branches.

Figure four is the visualization of the client level data and its respective dashboard component

Table four and five are extremely comprehensive as they visualize the geography of the market (City of Toronto at a DA level). This provides a trade area market level full breakdown of the demographics and trading areas as DAs are attributed to their closest branch and allows users to trigger on for where the bank has market coverage and where the gaps reside. However, outside of the allocation of the branches, the geography has a robust set of demographics such as growth (population, income), Dwelling composition and structure, average expenditure and the product recommendations the bank can target driven through the average expenditure datasets. Although the file has a significant amount of data and can be seen as overwhelming, selected data is fully visualized. This also has the full breakdown of how many frequent clients reside in the respective markets and what kind of products are being recomened on the basis of the market demographics analyzed through dwelling composition, growth metrics and expenditure.

Figure five is the visualization of the market level data and its respective dashboard component

The final table provides visualization and breakdown of the five primary product lines of business the bank offers which are combined with the market level data and cross validated against the average expenditure dataset. This is done to identify what products can be recommended throughout the market based on current and anticipate expenditure and growth metrics. For example, markets with high population, income and dwelling growth with limited spend would be targeted with mortgage products given the anticipated growth and the limited spend indicating a demographic saving to buy their home in a growth market. These assumptions are made across the market based on the actual indexed values and as such every market (DA) is given a product recommendation.

Figure six is the visualization of the product recommendation and analysis data and its respective dashboard component

Dashboard

Based on the full breakdown of the data extracted, the build out and the tables leveraged as seen above, the dashboard is fully interactive and driven by one prime parameters which controls all elements of the dashboard. Additional visualizations such as the products visualization, the client distribution treemap and the branch trends bar graph are combined here. The products visualization provides a full breakdown of the products that can be recommended based on their value and categorization to the bank. The value is driven based on the revenue the product can bring as investment products drive higher returns than liabilities. This is then broken down into three graphs consisting of the amount of times the product is recommended, the market coverage the recommendation provides between Stocks, Mortgages, Broker Fees, Insurance and Personal Banking products. The client distribution tree map provides an overview by branch as to how many frequent clients reside in the branch’s respective trading area. This provides a holistic approach to anticipating branch traffic trends and capacity constraints as branches with a high degree of frequent clients would require larger square footage and staffing models to adequately service the dependent markets. The final component is the representation of the client trends in a five year run rate to identify the growth the bank experienced in the market and at a branch level through new client acquisition. This provides a full run down of the number of new clients acquired and how the performance varies year over year to identify areas of high and low growth.

This combined with the primary three mapping visualizations, creates a fully robust and interactive dashboard for the user. Parameters are heavily used and are built on a select by branch basis to dynamically change all 6 live elements to represent what the user input requires. This is one of the most significant capabilities of Tableau, the flexibility of using a parameter to analyze the entire market, one branch at a time or to analyze markets without a branch is extremely powerful in deriving insights and analytics. The overall dashboard then zooms in/out as required when a specific branch is selected highlighting its location, its respective frequent clients, the trade area breakdown, what kind of products to recommend, the branch client acquisition trends and the actual number of frequent clients in the market. This can also be expanded to analyze multiple branches or larger markets overall if the functionality is required. Overall, the capacity of the dashboard consists of the following elements:

1. Market DA Level Map
2. Branch Level Map
3. Client Level Map
4. Client Distribution (Tree-Map)
5. Branch Trending Graph
6. Product Recommendation Coverage, Value and Effectiveness

This combined with the capacity to manipulate/store a live feed of data and the current parameters used for this level of analysis bring a new capacity to visualizing large datasets and providing a robust interactive playground to derive insights and analytics.

The link for this full Tableau Workbook is hosted here (please note an online account is required):https://prod-useast-a.online.tableau.com/t/torontofimarketgeovisprojectsa8905fall2019/views/TheTorontoFIMarketDashboard/TorontoFIMarket?:showAppBanner=false&:display_count=n&:showVizHome=n&:origin=viz_share_link

A Shot in the Dark: Analyzing Mass Shootings in the United States, 2014-2019

By: Miranda Ramnarayan

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2019

The data gathered for this project was downloaded from the Gun Violence Archive (https://www.gunviolencearchive.org/), which is a non-for Profit Corporation. The other dataset is the political affiliation per state, gathered by scrapping this information from (https://www.usa.gov/election-results). Since both of these datasets contain a “State Name” column, an inner join will be conducted to allow the two datasets to “talk” to each other.

The first step is importing your excel files, and setting up that inner join.

There are four main components this dashboard is made of: States with Mass Shootings, States with Highest Death Count, Total Individuals Injured from Mass Shootings and a scattergram displaying the amount of individuals injured and killed. All of these components were created in Tableau Worksheets and then combined on a Dashboard upon completion. The following are steps on how to re-create each Worksheet. 

1. States with Mass Shootings

In order to create a map in Tableau, very basic geographic information is needed. In this case, drag and drop the “State” attribute under the “Dimensions” column into the empty frame. This will be the result:

In order to change the symbology from dots to polygons, select “Map” under the Marks section.

To assign the states with their correct political affiliation, simply drag and drop the associated year you want into the “Colour” box under Marks.

This map is displaying the states that have had mass shootings within them, from 2014 to 2019. In order to automatic this, simply drag and drop the “Incident Date” attribute under Pages. The custom date page has been selected as “Month / Year” since the data set is so large.

This map is now complete and when you press the play button displayed in the right side of this window, the map will change as it only displays states that have mass shootings within them for that month and year.

2. States with Highest Death Count

This is an automated chart that shows the Democratic and Republican state that has the highest amount of individuals killed from mass shootings, as the map with mass shootings above it runs through its time series. Dragging and dropping “State” into the Text box under Marks will display all the states within the data set. Dragging and dropping the desired year into Colour under Marks will assign each state with its political party.

 In order for this worksheet to display the state with the highest kill count, the following calculations have to be made once you drag and drop the “# Killed” from Measures into Marks.

To link this count to each state, filter “State” to only display the one that has the maximum count for those killed.

This will automatically place “State” under Filters.

Drag and drop “Incident Date” into Pages and set the filter to Month / Year, matching the format from section 1.

Format your title and font size. The result will look like:

3. Total Individuals Injured from Mass Shootings

In terms of behind the scenes editing, this graph is the easiest to replicate.

Making sure that “State Name” is above “2016” in this frame is very important, since this is telling Tableau to display each state individually in the bar graph, per year.

4. Scattergram

This graph displays the amount of individuals killed and injured per month / year. This graph is linked to section 1 and section 2, since the “Incident Date” under Pages is set to the same format. Dragging and dropping “SUM (#Killed)” into Rows and SUM (#Injured) into Columns will set the structure for the graph.

In order for the dot to display the sum of individuals killed and injured, drag and drop “# Killed” into Filter and the following prompt will appear. Select “Sum” and repeat this process for “# Injured”.

Drag and drop “Incident Date” and format the date to match Section 1 and 2. This will be your output.

Dashboard Assembly

This is where Tableau allows you to be as customizable as you want. Launching a new Dashboard frame will allow you to drag and drop your worksheets into the frame. Borders, images and text boxes can be added at this point. From here, you can re-arrange/resize and adjust your inserted workbooks to make sure formatting is to your desire.  

Right clicking on the map on the dashboard and selecting “Highlight” will enable an interactive feature on the dashboard. In this case, users will be able to select a state of interest, and it will highlight that state across all workbooks on your dashboard. This will also highlight the selected state on the map, “muting” other states and only displaying that state when it fits the requirements based on the calculations set up prior.

Since all the Pages were all set to “Month/Year”, once you press “play” on the States with Mass Shootings map, the rest of the dashboard will adjust to display the filtered information.

It should be noted that Tableau does not allow the user to change the projection of any maps produced, resulting in a lack of projection customization. The final dashboard looks like this:

Missing Migrants: The Mediterranean Sea

By: Austen Chiu

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2019

Background

The dangerous journey of migrants seeking a better life has existed as long as countries have experienced political unrest. Advancements in technology have brought greater visibility to migrant groups than ever before. However, those who failed to make the journey often go unseen. Due to the undocumented nature of migrant paths, accurate numbers of survivors and deaths is difficult to track.

The data used in this project were obtained from the Missing Migrants Project. A dashboard was created in Tableau desktop to visualize the locations of missing migrant reports across the Mediterranean Sea, and to improve awareness of the scale at which the migrant crisis is occurring.

Creating an Animated Time Series Map

Following the prompts in Tableau, import your data. The data imported from an excel file should appear like this.

Make sure the data contains a date column, and spatial coordinates. Tableau can read spatial coordinates such as latitude and longitude, or northing and easting, to create maps. You can designate a column to be read as a date, or assign its geographic role as a latitude or longitude, to draw a map.

The icon above the column reveals options for which you want to format the data.
Geographic roles can be assigned to your data, allowing Tableau to read them as a map.
Creating a new map can be done by clicking the new tab buttons at the bottom of your window.
This is a blank graph. You can create graphs by dragging data into the “columns” and “rows” fields.

Tableau will automatically generate a map if data assigned with geographic roles are used to populate the “Columns” and “Rows” fields. If the “Pages” field in the top right corner is populated with the date data, a time slider module will appear below the “Marks” module. The “Pages” field facilitates Tableau’s animation capabilities.

The “Filters” field has applied a filter to the data, so only cases that occur in the Mediterranean region are visualized in the map.

The “Pages” field in the top left has been populated by the date data and a time slider has appeared in the bottom left.
The time slider allows you to select a specific date to view. The right arrow below the slider starts the animation, and Tableau will run through each snapshot of time, much like a slideshow.

Tableau can produce many types of data visualizations to accompany the animated map. A histogram, live counter, and packed bubbles visuals accompany the map on my dashboard.

The final product of the dashboard I created has been shared to Tableau Online. However, Tableau online does not support the animation features. A gif of the animated dashboard in Tableau Desktop has been shared through google drive, and can be viewed here.

A Century of Airplane Crashes

Laine Gambeta
Geovisualization Project, @RyersonGeo, Fall 2019

Tableau is an exceptionally useful tool in visualizing data effectively.  It allows many variations of charts in which the software suggests the best type based on data content.  The following project uses a data-set obtained from the National Transportation and Safety Board identifying locations and details of plane crashes between 1908-2009. The following screenshot is a final product and a run through of how it was made.

Map Feature:

To create the map identifying accident location, a longitude and latitude is required.  Once inputted into the Columns and Rows, Tableau automatically recognizes the location data and creates a map. 

The Pages function is populated with the date of occurrence and filtered by month in order to create a time animation based on a monthly scale. When the Pages function is populated with a date the software automatically recognizes a time series animation and creates a time slide.

The size of the map icon indicates the total number of fatalities at a specific location and time.  To create this effect, the fatalities measure is inputted into the Size function.  This same measure is inserted into the label function to show the total number of occurrences with each icon appearance.

When you scroll over the icons on the map the details of each occurrence appear.  To create this tool, the measures you want to appear are inserted into the Details function.  In this function, Date, Sum Aboard, Sum Fatalities, Sum Survivors, and Summary of accident appears when you scroll over the icon on the map.

Vertical Bar Chart Feature:

To create the vertical bar chart you must insert the date on the Y axis (columns), and the X axis (rows) with people aboard and fatalities.

Next, we must create a calculation to pull the number of survivors by subtracting the two measures.  To do so, right click on a column title cell and click create calculated field.  Within this calculation you select the two columns

you want to subtract and it will populate the fields. We will use this to identify the number of survivors.

The next step is creating a dual- axis to show both values on the same chart.  Right click one of the measures in the rows field and click dual-axis.  This will combine the measures onto the same chart and overlap each other.

Following this we need to filter the data to move along the animation by month.  It tallies the monthly numbers and adds it to the chart. In order to combine the monthly tallies to show on an annual bar chart, the following filters are used.  First filter by year which tallies the monthly counts into a single column on the bar chart.  The Page’s filter identifies the time period increments used in the time slider animation, this value must be consistent across all charts in order to sync.  In this case, we are looking at statistics on a monthly basis.

To split the colours between green and red to identify survivors and fatalities, the Measure Names (which is created automatically by Tableau) is inserted into the colour function.  This will identify each variable as a different colour.

When you bring your mouse over top the bar chart it selects and identifies the statistics related to the specific year.  To create this feature, the measures must be added to the tooltip function and formatted as you please.

Horizontal Bar Chart Feature:

The second bar chart is similar to the previous one.  The sum of fatalities is put in Columns and the Date is put in Rows to switch the axis to have the date on the Y axis.  The Pages function uses the same time frame as other charts calculating monthly and adding the total to the bar chart as the time progresses.

Total Count Features:

To create the chart you must insert the date on the Y axis (columns), and the X axis (rows) with people aboard and fatalities.

Adding in running counts is a very simple calculation feature and is built into Tableau.  You build the table by putting the measure into the text function, this enable’s the value to show as text and not a chart.  You will notice below that the Pages function must be populated with a date measure on a monthly basis to be consistent with the other charts.   

In order to create the running total values, a calculation must be added to the measure.  Clicking the SUM measure opens the options and allows us to select Edit Table Calculation.  This opens a menu where you can select Running Total, Sum on a monthly basis.  We apply this to 3 separate counters to total occurrences, fatalities, and survivors.

Pie Chart Feature:

Creating a pie chart requires the following measures to be used.  Under the marks drop down you must select pie chart.  This automatically creates a function for angular measure values.  The fatality and survivor measures are used and filtered monthly.  The Measure Values which is automatically created by Tableau identifies the values of these measures and is inputted into the Angle function to calculate the pie chart.  Again, the Measures Names are inputted into the colour function to separate the values by fatalities and survivors. The Pages function is populated with date of occurrence by month to sync with the other charts.

Lastly, a dashboard is created which allows the placement of the features across a single page.  They can be arranged to be aesthetically pleasing and informative.  Formatting can be done in the dashboard page to manipulate the colors and fonts.

Limitations:

Tableau does not allow you to select your map projection. Tableau Online has a public server to publish dashboards to, however it does not support timeline animation. Therefore, the following link to my project is limited to selecting the date manually to observe the statistics.

https://prod-useast-a.online.tableau.com/t/lainegambeta/views/ACenturyofAirplaneCrashes/Dashboard2?:origin=card_share_link&:embed=n