Visualizing Station Delays on the TTC

By: Alexander Shatrov

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2018.

Intro:

The topic of this geovisualization project is the TTC. More specifically, the Toronto subway system and its many, many, MANY delays. As someone who frequently has to suffer through them, I decided to turn this misfortune into something productive and informative, as well as something that would give a person not from Toronto an accurate image of what using the TTC on a daily basis is like. A time-series map showing every single delay the TTC went through over a specified time period.  The software chosen for this task was Carto, due to its reputation as being good at creating time-series maps.

Obtaining the data:

First, an excel file of TTC subway delays was obtained from Toronto Open Data, where it is organised by month, with this project specifically using August 2018 data. Unfortunately, this data did not include XY coordinates or specific addresses, which made geocoding it difficult. Next, a shapefile of subway lines and stations was obtained from a website called the “Unofficial TTC Geospatial Data”. Unfortunately, this data was incomplete as it had last been updated in 2012 and therefore did not include the recent 2017 expansion to the Yonge-University-Spadina line. A partial shapefile of it was obtained from DMTI, but it was not complete. To get around this, the csv file of the stations shapefile was opened up, the new stations added, the latitude-longitude coordinates for all of the stations manually entered in, and the csv file then geocoded in ArcGIS using its “Display XY Data” function to make sure the points were correctly geocoded. Once the XY data was confirmed to be working, the delay excel file was saved as a csv file, and had the station data joined with it. Now, it had a list of both the delays and XY coordinates to go with those delays. Unfortunately, not all of the delays were usable, as about a quarter of them had not been logged with a specific station name but rather the overall line on which the delay happened. These delays were discarded as there was no way to know where exactly on the line they happened. Once this was done, a time-stamp column was created using the day and timeinday columns in the csv file.

Finally, the CSV file was uploaded to Carto, where its locations were geocoded using Carto’s geocode tool, seen below.

It should be noted that the csv file was uploaded instead of the already geocoded shapefile because exporting the shapefile would cause an issue with the timestamp, specifically it would delete the hours and minutes from the time stamp, leaving only the month and day. No solution to this was found so the csv file was used instead. The subway lines were then added as well, although the part of the recent extension that was still missing had to be manually drawn. Technically speaking the delays were already arranged in chronological order, but creating a time series map just based on the order made it difficult to determine what day of the month or time of day the delay occurred at. This is where the timestamp column came in. While Carto at first did not recognize the created timestamp, due to it being saved as a string, another column was created and the string timestamp data used to create the actual timestamp.

Creating the map:

Now, the data was fully ready to be turned into a time-series map. Carto has greatly simplified the process of map creation since their early days. Simply clicking on the layer that needs to be mapped provides a collection of tabs such as data and analysis. In order to create the map, the style tab was clicked on, and the animation aggregation method was selected.

The color of the points was chosen based on value, with the value being set to the code column, which indicates what the reason for each delay was. The actual column used was the timestamp column, and options like duration (how long the animation runs for, in this case the maximum time limit of 60 seconds) and trails (how long each event remains on the map, in this case set to just 2 to keep the animation fast-paced). In order to properly separate the animation into specific days, the time-series widget was added in the widget tab, located next to to the layer tab.

In the widget, the timestamp column was selected as the data source, the correct time zone was set, and the day bucket was chosen. Everything else was left as default.

The buckets option is there to select what time unit will be used for your time series. In theory, it is supposed to range from minutes to decades, but at the time of this project being completed, for some reason the smallest time unit available is day. This was part of the reason why the timestamp column is useful, as without it the limitations of the bucket in the time-series widget would have resulted in the map being nothing more then a giant pulse of every delay that happened that day once a day. With the time-stamp column, the animation feature in the style tab was able to create a chronological animation of all of the delays which, when paired with the widget was able to say what day a delay occurred, although the lack of an hour bucket meant that figuring out which part of the day a delay occurred requires a degree of guesswork based on where the indicator is, as seen below

Finally, a legend needed to be created so that a viewer can see what each color is supposed to mean. Since the different colors of the points are based on the incident code, this was put into a custom legend, which was created in the legend tab found in the same toolbar as style. Unfortunately this proved impossible as the TTC has close to 200 different codes for various situations, so the legend only included the top 10 most common types and an “other” category encompassing all others.

And that is all it took to create an interesting and informative time-series map. As you can see, there was no coding involved. A few years ago, doing this map would have likely required a degree of coding, but Carto has been making an effort to make its software easy to learn and easy to use. The result of the actions described here can be seen below.

https://alexandershatrov.carto.com/builder/8574ffc2-9751-49ad-bd98-e2ab5c8396bb/embed

Visual Story of GHG Emissions in Canada

By Sharon Seilman, Ryerson University
Geovis Project Assignment @RyersonGeo, SA8905, Fall 2018

Background

Topic: 

An evaluation of annual Greenhouse Gas (GHG) Emissions changes in Canada and an in-depth analysis of which provinces/ territories contribute to most of the GHG emissions within National and Regional geographies, as well as by economic sectors.

  • The timeline for this analysis was from 1990-2015
  • Main data sources: Government of Canada Greenhouse Gas Emissions Inventory and Statistics Canada
Why? 

Greenhouse gas emissions are compounds in the atmosphere that absorbs infrared radiation, thus trapping and holding heat in the atmosphere. By increasing the heat in the atmosphere, greenhouse gases are responsible for the greenhouse effect, which ultimately leads to global climate change. GHG emissions are monitored in three elements -its abundance in the atmosphere, how long it stays in the atmosphere and its global warming potential.

Audience: 

Government organizations, Environmental NGOs, Members of the public

Technology

An informative website with the use of Webflow was created, to visually show the story of the annual emissions changes in Canada, understand the spread of it and the expected trajectory. Webflow is a software as a service (SaaS) application that allows designers/users to build receptive websites without significant coding requirements. While the designer is creating the page in the front end, Webflow automatically generates HTML, CSS and JavaScript on the back end. Figure 1 below shows the user interaction interface of Webflow in the editing process. All of the content that is to be used in the website would be created externally, prior to integrating it into the website.

Figure 1: Webflow Editing Interface
The website: 

The website it self was designed in a user friendly manner that enables users to follow the story quite easily. As seen in figure 2, the information it self starts at a high level and gradually narrows down (national level, national trajectory, regional level and economic sector breakdown), thus guiding the audience towards the final findings and discussions. The maps and graphs used in the website were created from raw data with the use of various software that would be further elaborated in the next section.

Figure 2: Website created with the use of Webflow
Check out Canada’s GHG emissions story HERE!

Method

Below are the steps that were undertaken for the creation of this website. Figure 3 shows a break down of these steps, which is further elaborated below.

Figure 3:  Project Process
  1. Understanding the Topic:
    • Prior to beginning the process of creating a website, it is essential to evaluate and understand the topic overall to undertake the best approach to visualizing the data and content.
    • Evaluate the audience that the website would be geared towards and visualize the most suitable process to represent the chosen topic.
    • For this particular topic of understanding GHG emissions in Canada, Webflow was chosen because it allows the audience to interact with the website in a manner that is similar to a story; providing them with the content in a visually appealing and user friendly manner.
  2. Data Collection:
    • For the undertaking of this analysis, the main data source used was the Greenhouse Gas Inventory from the Government of Canada (Environment and Climate Change). The inventory provided raw values that could be mapped and analyzed in various geographies and sectors. Figure 4 shows an example of what the data looks like at a national scale, prior to being extracted. Similarly, data is also provided at a regional scale and by economic sector.

      Figure 4: Raw GHG Values Table from the Inventory
    • The second source for this visualization was the geographic boundaries. The geographic boundaries shapefiles for Canada at both a national scale and regional scale was obtained from Statistics Canada. Additionally, the rivers (lines) shapefile from Statistics Canada too was used to include water bodies in the maps that were created.
      • When downloading the files from Statistics Canada, the ArcGIS (.shp) format was chosen.
  3. Analysis:
    • Prior to undertaking any of the analysis, the data from the inventory report needed to be extracted to excel. For the purpose of this analysis, national, regional and economic sector data were extracted from the report to excel sheets
      • National -from 1990 to 2015, annually,
      • Regional -by province/territory from 1990 to 2015, annually
      • Economic Sector -by sector from 1990 to 2015, annually
    • Graphs:
      • Trend -after extracting the national level data from the inventory, a line graph was created in excel with an added trendline. This graph shows the total emissions in Canada from 1990 to 2015 and the expected trajectory of emissions for the upcoming five years. In this particular graph, it is evident that the emissions show an increasing trajectory. Check out the trend graph here!
      • Economic Sector -similar to the trend graph, the economic sector annual data was extracted from the inventory to excel. With the use of the available data, a stacked bar graph was created from 1990 to 2015. This graph shows the breakdown of emissions by sector in Canada as well as the variation/fluctuations of emissions in the sectors. It helps understand which sectors contribute the most and which years these sectors may have seen a significant increase or decrease. With the use of this graph, further analysis could be undertaken to understand what changes may have occurred in certain years to create such a variation. Check out the economic sector graph here!
    •  Maps:
      • National map -the national map animation was created with the use of ArcMap and an online GIF maker. After the data was extracted to excel, it was saved as a .csv files and uploaded to ArcMap. With the use of ArcMap, sixteen individual maps were made to visualize the varied emissions from 1990 to 2015. The provincial and territorial shapefile was dissolved using the ArcMap dissolve feature (from the Arc Tool box) to obtain a boundary file at a national scale (that was aligned with the regional boundary for the next map). Then, the uploaded table was joined to the boundary file (with the use of the Table join feature). Both the dissolved national boundary shapefile and the river shapefile were used for this process, with the data that was initially exported from the inventory for national emissions. Each map was then exported a .jpeg image and uploaded to the GIF maker, to create the animation that is shown in the website. With the use of this visualization, the viewer can see the variation of emissions throughout the years in Canada. Check out the national animation map here!
      •  Regional map -similar to the national one, the regional map animation was created in same process. However, for the regional emissions, data was only available for three years (1990, 2005 and 2015). The extracted data .csv file was uploaded and table joined to the provinces and territories shapefile (undissolved), to create three choropleth maps. The three maps were them exported as .jpeg images and uploaded to the GIF maker to create the regional animation. By understanding this animation, the viewer can distinctly see which regions in Canada have increase, decreased or remained the same with its emissions. Check out the regional animation map here!
  4. Final output/maps:
    • The graphs and maps that were discussed above were exported as images and GIFs to integrate in the website. By evaluating the varied visualizations, various conclusions and outputs were drawn in order to understand the current status of Canada as a nation, with regards to its GHG emissions. Additional research was done in order to assess the targets and policies that are currently in place about GHG emissions reductions.
  5. Design and Context:
    • Once the final output and maps were created, and the content was drafted, Webflow enables the user to easily upload external content via the upload media tool. The content was then organized with the graphs and maps that show a sequential evaluation of the content.
    • For the purpose of this website, an introductory statement introduces the content discussed and Canada’s place in the realm of Global emissions. Then the emissions are first evaluated at a national scale with the visual animation, then the national trend, regional animation and finally, the economic sector breakdown. Each of the sections have its associated content and description that provides an explanation of what is shown by the visual.
    • The Learn More and Data Source buttons in the website include direct links to Government of Canada website about Canada’s emissions and the GHG inventory itself.
    • The concluding statement provides the viewer with an overall understanding of Canada’s status in GHG emissions from 1990 to 2015.
    • All of the font formatting and organizing of the content was done within the Webflow interface with the end user in mind.
  6. Webflow:
    • The particular format that was chosen in for this website because of story telling element of it. Giving the viewer the option to scrolls through the page and read the contents of it, works similarly as story because this website was created for informative purposes.

Lessons Learned: 

  • While the this website provides informative information, it could be further advanced through the integration of an interactive map, with the use of additional coding. This however would require creating the website outside of the Webflow interface.
  • Also, the analysis could be further advanced with the additional of municipal emissions values and policies (which was not available in the inventory it self)

Overall, the use of Webflow for the creation of this website, provides users with the flexibility to integrate various components and visualizations. The user friendly interface enables uses with minimal coding knowledge to create a website that could be used for various purposes.

Thank you for reading. Hope you enjoyed this post!

Visualizing Urban Land Use Growth in Greater Sào Paulo

By: Kevin Miudo

Geovis Project Assignment @RyersonGeo, SA8905, Fall 2018

https://www.youtube.com/watch?v=Il6nINBqNYw&feature=youtu.be

Introduction

In this online development blog for my created map animation, I intend to discuss the steps involved in producing my final geovisualization product, which can be viewed above in the embedded youtube link. It is my hope that you, the reader, learn something new about GIS technologies and can apply any of the knowledge contained within this blog towards your own projects. Prior to discussing the technical aspects of the map animations development, I would like to provide some context behind the creation of my map animation.

Cities within developing nations are experiencing urban growth at a rapid rate. Both population and sprawl are increasing at unpredictable rates, with consequences for environmental health and sustainability. In order to explore this topic, I have chosen to create a time series map animation visualizing the growth of urban land use in a developing city within the Global South. The City which I have chosen is Sào Paulo, Brazil. Sào Paulo has been undergoing rapid urban growth over the last 20 years. This increase in population and urban sprawl has significant consequences to climate change, and such it is important to understand the spatial trend of growth in developing cities that do not yet have the same level of control and policies in regards to environmental sustainability and urban planning. A map animation visualizing not only the extent of urban growth, but when and where sprawl occurs, can help the general public get an idea of how developing cities grow.

Data Collection

In-depth searches of online open data catalogues for vector based land use data cultivated little results. In the absence of detailed, well collected and precise land use data for Sào Paulo, I chose to analyze urban growth through the use of remote sensing. Imagery from Landsat satellites were collected, and further processed in PCI Geomatica and ArcGIS Pro for land use classification.

Data collection involved the use of open data repositories. In particular, free remotely sensed imagery from Landsat 4, 5, 7 and 8 can be publicly accessed through the United States Geological Survey Earth Explorer web page. This open data portal allows the public to collect imagery from a variety of satellite platforms, at varying data levels. As this project aims to view land use change over time, imagery was selected at data type level-1 for Landsat 4-5 Thematic Mapper and Landsat 8 OLI/TIRS. Imagery selected had to have at least less than 10% cloud cover, and had to be images taken during the daytime so that spectral values would remain consistent across each unsupervised image classification.

Landsat 4-5 imagery at 30m spectral resolution was used for the years between 2004 and 2010. Landsat-7 Imagery at 15m panchromatic resolution was excluded from search criteria, as in 2003 the scan-line corrector of Landsat-7 failed, making many of its images obsolete for precise land use analysis. Landsat 8 imagery was collected for the year 2014 and 2017. All images downloaded were done so at the Level-1 GeoTIFF Data Product level. In total, six images were collected for years 2004, 2006, 2007, 2008, 2010, 2014, 2017.

Data Processing

Imagery at the Level-1 GeoTIFF Data Product Level contains a .tif file for each image band produced by Landsat 4-5 and Landsat-8. In order to analyze land use, the image data must be processed as a single .tiff. PCI Geomatica remote sensing software was employed for this process. By using the File->Utility->Translate command within the software, the user can create a new image based on one of the image bands from the Landsat imagery.

For this project, I selected the first spectral band from Landsat 4-5 Thematic Mapper images, and then sequentially added bands 2,3,4,5, and band 7 to complete the final .tiff image for that year. Band 6 is skipped as it is the thermal band at 120m spatial resolution, and is not necessary for land use classification. This process was repeated for each landsat4-5 image.Similarly for the 2014 and 2017 Landsat-8 images, bands 2-7 were included in the same manner, and a combined image was produced for years 2014 and 2017.

Each combined raster image contained a lot of data, more than required to analyze the urban extent of Sào Paulo and as a result the full extent of each image was clipped. When doing your own map animation project, you may also wish to clip data to your study area as it is very common for raw imagery to contain sections of no data or clouds that you do not wish to analyze. Using the clipping/subsetting option found under tools in the main panel of PCI Geomatica Focus, you can clip any image to a subset of your choosing. For this project, I selected the coordinate type ‘lat/long’ extents and input data for my selected 3000×3000 pixel subset. The input coordinates for my project were: Upper left: 46d59’38.30″ W, Upper right: 23d02’44.98″ S, Lower right: 46d07’21.44″ W, Lower Left: 23d52’02.18″ S.

Land Use Classification

The 7 processed images were then imported into a new project in ArcPro. During importation, raster pyramids were created for each image in order to increase processing speeds.  Within ArcPro, the Spatial Analyst extension was activated. The spatial analyst extension allows the user to perform analytical techniques such as unsupervised land use classification using iso-clusters. The unsupervised iso-clusters tool was used on each image layer as a raster input.

The tool generates a new raster that assigns all pixels with the same or similar spectral reluctance value a class. The number of classes is selected by the user. 20 classes were selected as the unsupervised output classes for each raster. It is important to note that the more classes selected, the more precise your classification results will be. After this output was generated for each image, the 20 spectral classes were narrowed down further into three simple land use classes. These classes were: vegetated land, urban land cover, and water. As the project primarily seeks to visualize urban growth, and not all types of varying land use, only three classes were necessary. Furthermore, it is often difficult to discern between agricultural land use and regular vegetated land cover, or industrial land use from residential land use, and so forth. Such precision is out of scope for this exercise.

The 20 classes were manually assigned, using the true colour .tiff image created from the image processing step as a reference. In cases where the spectral resolution was too low to precisely determine what land use class a spectral class belong to, google maps was earth imagery referenced. This process was repeated for each of the 7 images.

After the 20 classes were assigned, the reclassify tool under raster processing in ArcPro was used to aggregate all of the similar classes together. This outputs a final, reclassified raster with a gridcode attribute that assigns respective pixel values to a land use class. This step was repeated for each of the 7 images. With the reclassify tool, you can assign each of the output spectral classes to new classes that you define. For this project, the three classes were urban land use, vegetated land, and water.

Cartographic Element Choices:

 It was at this point within ArcPro that I had decided to implement my cartographic design choices prior to creating my final map animation.

For each layer, urban land use given a different shade of red. The later the year, the darker and more opaque the colour of red. Saturation and light used in this manner helps assist the viewer to indicate where urban growth is occurring. The darker the shade of red, the more recent the growth of urban land use in the greater Sào Paulo region. In the final map animation, this will be visualized through the progression of colour as time moves on in the video.

ArcPro Map Animation:

Creating an animation in ArcPro is very simple. First, locate the animation tab through the ‘View’ panel in ArcPro, then select ‘Add animation’. Doing so will open a new window below your work space that will allow the user to insert keyframes. The animation tab contains plenty of options for creating your animation, such as the time frame between key frames, and effects such as transitions, text, and image overlays.

For the creation of my map animation, I started with zoomed-out view of South America in order to provide the viewer with some context for the study area, as the audience may not be very familiar with the geography of Sào Paulo. Then, using the pan tool, I zoomed into select areas of choice within my study area, ensuring to create new keyframes every so often such that the animation tool creates a fly-by effect. The end result explores the very same mapping extents as I viewed while navigating through my data.

While making your own map animation, ensure to play through your animation frequently in order to determine that the fly-by camera is navigating in the direction you want it to. The time between each keyframe can be adjusted in the animation panel, and effects such as text overlays can be added. Each time I activated another layer for display to show the growth of urban land use from year to year, I created a new keyframe and added a text overlay indicating to the user the date of the processed image.

Once you are satisfied with your results, you can export your final animation in a variety of formats, such as .avi, .mov, .gif and more. You can even select the type of resolution, or use a preset that automatically configures your video format for particular purposes. I chose the youtube export format for a final .mpeg4 file at 720p resolution.

I hope this blog was useful in creating your very own map animation on remotely sensed and classified raster data. Good luck!

Invasive Species in Ontario: An Animated-Interactive Map Using CARTO

By Samantha Perry
Geovis Project Assignment @RyersonGeo, SA8905, Fall 2018

My goal was to create an animated time-series map using CARTO to visualize the spread of invasive species across Ontario. In Ontario there are dozens of invasive species posing a threat to the health of our lakes, rivers, and forests. These intruding species can spread quickly due to the absence of natural predators, often damaging native species and ecosystems, and resulting in negative effects on the economy and human health. Mapping the spread of these invasive species is beneficial for showing the extent of the affected areas which can potentially be used for research and remediation purposes, as well as awareness for the ongoing issue. For this project, five of the most problematic or wide-spread invasive species were included in an animated-interactive map to show their spatial and temporal distribution.

The final animated-interactive map can be found at: https://perrys14.carto.com/builder/7785166c-d0cf-41ac-8441-602f224b1ae8/embed

Data

  1. The first dataset used was collected from the Ontario Ministry of Natural Resources and Forestry and contained information on invasive species observed in the province from 1982 to 2012. The data was provided as a shapefile, with polygons representing the affected areas.
  2. The second dataset was downloaded from the Early Detection & Distribution Mapping System (EDDMapS) Ontario website. The dataset included information about invasive species identified between 2010 and 2018. I obtained this dataset to supplement the Ontario Ministry dataset in order to provide a more up-to-date distribution of the species.

Software
CARTO is a location-intelligence based website that offers easy to use mapping and analysis software, allowing you to create visually appealing maps and discover key insights from location data. Using CARTO, I was able to create an animated-interactive map displaying the invasive species data. CARTO’s Time-Series Widget can be used to display large numbers of points over time. This feature requires a map layer containing point geometries with a timestamp (date), which is included in the data collected for the invasive species.

CARTO also offers an interactive feature to their maps, allowing users control some aspects of how they want to view the data. The Time-Series Widget includes animation controls such as play, stop, and pause to view a selected range of time. In addition, a Layer Selector can be added to the map so the user is able to select which layer(s) they wish to view.

Limitations
In order to create the map, I created a free student account with CARTO. Limitations associated with a free student account include a limit on the amount of data that can be stored, as well as a maximum of 8 layers per map. This limits the amount of invasive species that can be mapped.

Additionally, only one Time-Series Widget can be included per map, meaning that I could not include a time-series animation for each species individually, as I originally intended to. Instead, I had to create one time-series animation layer that included all five of the species. Because this layer included thousands of points, the map looks dark and cluttered when zoomed out to the full extent of the province (Figure 1). However, when zoomed in to specific areas of the province, the points do not overlap as much and the overall animation looks cleaner.

Another limitation to consider is that not all the species’ ranges start at the same time. As can be seen in Figure 1 below, the time slider on the map shows that there is a large increase in species observations around 2004. While it is possible that this could simply be due to an increase in observations around that time, it is likely because some of the species’ ranges begin at that time.

Figure 1. Layer showing all five invasive species’ ranges.

Tutorial

Step 1: Downloading and reviewing the data
The Ontario Ministry of Natural Resources and Forestry data was downloaded as a polygon shapefile using Scholars GeoPortal, while the EDDMapS Ontario dataset was downloaded as a CSV file from their website.

Step 2: Selection of species to map
Since the datasets included dozens of different invasive species in the datasets, it was necessary to select a smaller number of species to map. Determining which species to include involved some brief research on the topic, identifying which species are most prevalent and problematic in the province. The five species selected were the Eurasian Water-Milfoil, Purple Loosestrife, Round Goby, Spiny Water Flea, and Zebra Mussel.

Step 3: Preparing the data for upload to CARTO
Since the time-series animation in CARTO is only available for point data, I had to convert the Ontario Ministry polygon data to points. To do this I used ArcMap’s “Feature to Point” tool which created a new point layer from the polygon centroids. I then used the “Add XY Coordinates” tool to get the latitude and longitude of each point. Finally, I used the “Table to Excel” conversion tool to export the layer’s attribute table as an excel file. This provided me with a table with all invasive species point data collected by the Ontario Ministry that could be uploaded to CARTO.

Next, I created a table that included the information for the five selected species from both sources. I selected only the necessary columns to include in the new table, including; Species Name, Observation Date, Year, Latitude, Longitude, and Observation Source. This combined table was then saved as an excel file to be uploaded to CARTO.

Finally, I created 5 additional tables for each of the species separately. These were later used to create map layers that show each species’ individual distribution.

Step 4: Uploading the datasets to CARTO
After creating a free student account with CARTO, I uploaded the six datasets as excel files. Once uploaded, I had to change the “Observation Date” column from a “string” to “date” data type for each dataset. A “date” data type is required for the time-series animation to run.

Step 5: Geocoding datasets
Each dataset added to the map as a layer had to be geocoded. Using the latitude and longitude columns previously added to the Excel file, I geocoded each of the five species’ layers.

Step 6: Create time-series widget to display temporal distribution of all species
After creating a blank map, I added the Excel file that included all the invasive species data as a layer. I then added a Time-Series Widget to allow for the temporal animation. I then selected Observation Date as the column to be displayed, meaning that the point data will be organized by observation date. I chose to organize the buckets, or groupings, for the corresponding time-slider by year.

Since “cumulative” was not an option for the Time-Series layer, I had to use CARTCSS to edit the code for the aggregation style. Changing the style from “linear” to “cumulative” allowed the points to remain on the screen for the duration of the animation, letting the user see the entire species’ range in the province. The updated CSS code can be seen in the screenshots below.

Step 7: Creating five additional layers for each species’ range
Since I could only add one Time-Series Widget per map, and the layer with the animation looks cluttered at some extents, I decided to create five additional layers that show each of the species’ individual observation data and range.

Step 8: Customizing layer styles
After adding all of the layers, a colour scheme was selected where each of the species’ was represented by a different colour to clearly differentiate between them. Colours that are generally associated with the species were selected. For example, the colour purple was selected to represent Purple Loosestrife, which is a purple flowering plant. The “multiply” style option was selected, meaning that areas with more or overlapping occurrences of invasive species are a darker shade of the selected colour.

A layer selector was included in the legend so that users can turn layers on or off. This allows them to clearly see one species’ distribution at a time.

Step 9: Publish map
Once all of the layers were configured correctly, the map was published so it could be seen by the public.

Urban Development of San Francisco

By Hannah Burdett

SA8905 Geovisualization Project, Ryerson University

The Development of San Francisco

San Francisco is located in the center of Northern California. It started as a base for the gold rush of 1849, the city quickly became one of the most populated cities in the United States. Shortly thereafter, San Francisco was devastated by the 1906 earthquake. Development peaked in the 1900’s as San Francisco rebuilt areas demolished by the earthquake and fires to compensate the growing population. During the 1930’s the San Francisco-Oakland Bay Bridge and the Golden Gate Bridge were opened. Additionally, during World War II, San Francisco was a major mainland supply point and port of embarkation for the war in the Pacific. Both factors led to another peak in construction. After World War II, many American military personnel who had fallen in love with the city while leaving for or returning from the Pacific settled in the city. This led to promoting the development of the Sunset District, Visitacion Valley, and the total build-out of San Francisco. Starting in the latter half of the 1960’s, San Francisco became most recognized for the hippie movement. Currently, San Francisco has become known for finance and technology industries. There is a high demand for housing, driven by its close proximity to Silicon Valley, and a low supply of available housing has led to the city being one of America’s most expensive places to live.

Data

The data used for the time series animation was imported from data.gov. Data.gov is a repository for the US Governments open source data. The imported data included a Land use Shapefile for San Francisco. The shapefile included information such as land use, shape area, street address, street number, etc. The land use shapefile also included the year the building was built. The building years range from 1848 to 2016 displaying 153 years of urbanization. The buildings were represented as polygons throughout San Francisco. Additionally, a grey scale base map from ArcGIS Pro was displayed to create a more cohesive map design.

 

 

Time Series Animation

To develop the reconstruction of San Francisco throughout the years, both QGIS and ArcGIS Pro were utilized. Both platforms were used so to provide a comparison between time series animation tools from an open source application and a non-open source application.

QGIS is an open source geographic information systems application that provides data visualization, editing, and analysis through functions and plugins. To create the time series animation the Time Manager plugin was utilized. The Time Manager plugin animates vector features based on a time attribute. For this study the time attribute was the years built.

ArcGIS Pro is the latest professional desktop GIS from Esri. ArcGIS Pro enables users to view, explore, analyze, edit and share maps and data. Unlike QGIS, no additional plugins are required to create the animated time series.

QGIS Methodology

To generate the time series in QGIS, the land use shapefile was downloaded and opened in QGIS. The attribute table from the land use shapefile was then exported and opened in Excel so that the yrbuilt column could be reformatted to meet QGIS Time Manager requirements. The yrbuilt column had the data presented as YYYY format for building dates. QGIS Time Manager requires timestamps to be in YYYY-MM-DD. To correct the format, -01-01 was added to the end of each building year. The modified values were then saved into a new column called yrbuilt1. The Excel sheet was then imported into QGIS and joined to the land use shapefile.

In QGIS, each of the buildings was presented as polygons. The shapefile symbology was changed from single symbology to quantified symbology. In other words, the symbology for each of the polygons was broken down to seven classes defined by years. Each class was then distinguished by color, so that one may differentiate the oldest building from the newest buildings. Furthermore, a grey scale basemap was added to create a more cohesive map.

Furthermore, in the Time Manager settings, “Add Layer” was selected. The land use shapefile was chosen as the Layer of interest. The start time was set to the yrbuilt1 attribute, whereas the end time was set to “No end time – accumulate features”. This allows newer buildings to be added without older buildings being removed from the map. For the animation, each time frame will be shown for 100 milliseconds. The Time Manager plugin was then turned on so that the time series may run.

 

In order to export the time series animation, Time Manager offers an “Export Video” option. However, this exports the animation as an image series, not as an actual video. To correct this, the image series was uploaded to Mapbox where additional Mapbox styles were used to render the map. It was then exported as a Gif from Mapbox.

ArcGIS Pro Methodology

In ArcGIS Pro, the land use shapefile was imported. The symbology for each of the polygons was then broken down to seven classes defined by years. The same colours utilized in QGIS were applied to the classes in ArcGIS Pro to differentiate between the building years. Within the layer’s properties, the Layers Time was selected as “each feature has a single time field”. Furthermore, the start and end times were set to the newest and oldest building years. The number of steps were assigned a value of sixteen. In View, the animation was added, and the Time Slider Steps were imported. The time frames were set to match the QGIS animation so that both time series animations would run at the same speed. The time series animation was then exported as a Gif.

Final Animated Map

Finally, to create a cohesive animated map the exported Gif’s were complied together in PowerPoint. Additional map features, such as a legend, were designed within PowerPoint. A bar graph was added along the bottom of the map to show years of peak building construction. The final time series map was then exported as a .mp4 and upload to YouTube.

ArcPro Animation of 1923 Canoe Trip in Algonquin Park

By Sarah Medland

Geovis Course Project @RyersonGeo, SA8905, Fall 2018

Context

While searching the web for historic maps to inspire this project I came across the personal website of Bob and Diane McElroy. Their website includes an extensive personal collection of present and historic records of the natural environment within Ottawa Valley and Algonquin Park. The collection of thoughts and logs on their site consist of those of their ancestors – dating back many decades from now. The following map is a section of the one which was chosen for the purpose of this assignment. It dates back to 1921:

In July of 1923, a group of 4 men led by a guide embarked on a 12-day canoe-trip, creating a log of their route as they traveled. The map log included handwritten details by W. H. McConnell about wildlife, weather, and their experience in the Park.

Purpose:

 to animate an artistic rendering of a historic canoeing route which…

 – brings to life a historic map by integrating it with modern GIS technology

– reveals information from approx. a hundred years prior about an ever-popular canoeing area

Methods

To begin, the map was download as a JPEG and brought into ArcMap. A DMTI Spatial minor water bodies Shapefile was added. Using this present-day layer, labelled by lake name, it was fairly easy to align this with the lakes from the historic map. Some challenges arose as the map is from 1921 therefore its accuracy is questionable, however, I was able to geo-reference the map fairly well.

Historic Map in ArcMap where it was georeferenced to a present-day water bodies layer

Next, DEM tiles were downloaded from Scholar’s Geoportal. These were converted into a TIN using the raster to TIN tool in ArcMap, and then into TIN nodes using the TIN node tool. This allowed the tiles to be combined into one continuous TIN using the Create TIN tool which could be clipped to the extent of the map surface. Once the elevation surface was made, the map could be given height.

The map surface after it was draped over an elevated TIN surface and atmospheric effects were applied

To visualize the canoe route, a line Shapefile was created over the route drawn on the map. Campsites were also added as a point Shapefile which included a ‘Date’ field in the attribute table. In the ArcGIS Pro Global setting the map was draped over the TIN surface and campsites symbolized in 3D with the dates labelled.

An example of some of the original annotations on the map

Lastly, a animation following the canoe route was created in ArcGIS Pro. The animation was created to guide the viewer along the route of the 1923 trip and included annotations such as those above and historic pictures from the time period.

Results: The following video is the final product:

Visualizing Alaska’s Forest Damage in Twenty Years

Author: Anitha Muraleedharan
Geovis Project Assignment@RyersonGeo,
SA 8905, Fall 2018 (Rinner)

Forest Damage in Alaska

Alaska is a dynamic region and has a long history of changeable climate. Alaska has lost a lot of its forests due to insect infestation, fire, flood, landslides, and windthrow. Aerial surveys are conducted to monitor forest health for the State of Alaska and to identify insect and some disease pest trends. This time series map animation will visualize the forest damage in Kenai Peninsula, Tanana Region and Fort Yukon Region of Alaska during the years 1989 to 2010. This blog will cover the entire processes involved in creating this visualization.

Data

The spatial data of the forest damage survey conducted during the period from 1989 to 2010 by the Alaska Department of Natural Resources are readily available for download from AK State Geo-Spatial Data Clearinghouse (http://www.asgdc.state.ak.us/?#2952). The shapefiles are available individually for each year from 1989 to 2010 except for years from 2000 to 2007. These data were used for preparing this Time Series map animation.

Preparing Data for Animation in QGIS

QGIS 3.2.3 64bit was used to prepare the data for animated map visualization of Alaska’s forest damage. QGIS is a free and open-source cross-platform desktop geographic information system (GIS) application that supports viewing, editing, visualization and analysis of geospatial data. Since the data were available only as individual files, the first step in preparing the data was to merge this data together into one shapefile. For this task, I used the Merge Vector Layers Tool in QGIS which merged all the individual shapefiles into a single shapefile.

Steps to Merge multiple vector layers into one

  • Step1: Add all the vector layers, intended to be merged, into QGIS
  • Step2: Go to Vector →Data Management Tools → Merge Vector Layers in the menu
  • Step3: Click input layers button and select all the layers needed to be merged
  • Step4: Click Merged Layer button to give a name for the merged output layer
  • Step4: Click Run in Background button to create the merged layer and add it to QGIS

Fig. 1 Merge Vector Layer Tool in QGIS

The next task was to format the timestamp column to fit the QGIS Time Manager plugin tool that will be used to create the animated map visualization. The timestamp column for this data was “SURVEY_YR” which was in four-digit format. The QGIS Time Manager Plugin requires that the timestamp data be in YYYY-MM-DD format. For this, a new field was created with name “Damage_Yr” and type string and used the Field Calculator tool in Processing Toolbox of QGIS.

Fig. 2 Field Calculator Tool in QGIS

In the Field Calculator tool, the expression “tostring(SURVEY_YR) + ‘-01-01’ ” was used to concatenate data in the field “SURVEY_YR” and the “-01-01”  together to make the timestamp in YYYY-MM-DD format and copy the data to the new field “Damage_Yr”.

Fig. 3 Attribute table showing the Damge_Yr in YYYY-MM-DD format after update.

Visualizing the Time Series

The Time Manager plugin was downloaded and installed in QGIS. The forest damage data was then added as a layer in QGIS. The Google Terrain map was added as the base map for this time series animation. The following steps were performed to add the Google Terrain map to QGIS.

  • Step1: Add a new connection to XYZ Tiles in QGIS and give it a name, say “Google Terrain”
  • Step2: Use https://mt1.google.com/vt/lyrs=t&x={x}&y={y}&z={z} as the URL.
  • Step3. Click Ok and then double-click the created layer to add the “Google Terrain” as the layer.

After the data was added, it was time to apply symbology to the polygon data showing the forest damage in QGIS. The layer was styled using the attribute “Damage_Yr” and categorized with sequential symbology. Once the data was styled, the Time Manager plugin needed to be configured to visualize the time series animation.

In the Time Manager Settings window, the Forest damage layer which needs to be animated was added using the “Add layer” button. The Damage_Yr column was chosen for the Start and End time and “Accumulate features” option was selected to show the features accumulated on the map as the year changes during the animation. 500 milliseconds duration was set in the animation options to show each year for that many seconds in the animation before showing the next year. To display each year as a label in the map during the animation, time format was set as “%Y” and the font, font size, and text color were also set.

Fig. 4 Time manager settings window

Fig. 5 Time display options.

The time frame in the Time manager dock was set as years since the forest damage in each year will be animated and displayed. The time frame size for the animation was set as 1 since we have data for each year from 1989 to 2010. The animation can be played by clicking the play button and QGIS will show the forest damage of Alaska in each year from 1989 to 2010 on the map window for 500 milliseconds each.

Fig. 6 Time Manager dock showing settings for the animation in QGIS

Converting the Time Series into Video

The Time Manager allows exporting the animation to a video. However, there is no option to add a legend onto the rendered maps in the animation in QGIS. Therefore, the maps were exported as .PNG image files. The map frames were exported first with the full extent of the map and subsequently, two more times with map zoomed to areas Tanana and Fort Yukon respectively for showing different areas in one animation. The legend along with title and data source labels were then added for each exported map frame using photoshop.

Finally, VirtualDub software was used to convert the .PNG files to video for each series of maps. VirtualDub is a free and open-source video capture and video processing utility for Microsoft Windows written by Avery Lee.  The generated .PNG files were then renamed in ascending order sequence in the format “frameXXX.png” where XXX is the frame number. For example, frame000, frame001 and so on. This is required for VirtualDub to detect the files as a sequence of images and then combine it to a video. The steps followed to create the animated video is as given below.

  • Step1: Open VirtualDub software
  • Step2: Go to File → Open video file option in the menu and navigate to the images folder
  • Step3: Click the first image in the map image series and VirtualDub will automatically add all the other images that are in sequence
  • Step5: Go to Video → Frame rate and set fps as 0.5 to show each frame for 500 milliseconds in the video
  • Step6: Preview the video and save it using File → Save as AVI option in the menu

Fig. 7 Combining the png files in VirtualDub software

Results


Watch the visualization on YouTube

North American Impact Events throughout History – A Map Animation

By: Nicole Slattery. Geovis Project Assignment @RyersonGeo, SA8905, Fall 2018

For my Geovisualization assignment, I wanted to create an animated map of impact crater events in North America throughout history. I decided to use ArcGIS Pro in order to do this because of the nature of the data. The Earth Impact Database maintained by the Planetary and Space Science Centre (PSSC) in New Brunswick, has achieved the 190 confirmed impact craters from around the world. The impacts have occurred anywhere from 1850 million years ago to 600 000 years ago. Usually, when creating an animated map throughout time, the map software requires a date. The impacts did not occur within the span of the Gregorian calendar used today; therefore, this software cannot map this data. However, ArcGIS Pro includes a tool “Animate through a range” which allows for this data to be animated sequentially without a date.

2D Map of Impact Craters in North America

In order to utilize ArcGIS Pro’s animation through a range tool, the data points of impact craters were geocoded and added to a new map. The points were displayed by proportional symbols of their diameter on the earth in km. Therefore, the map displays the distribution of impact craters across North America by their diameter. The locations were symbolized as well, in a gradient colours brown to black, in order for the points to appear to have depth. The 2D map can be viewed above. The basemap of the map was added from the basemap gallery under the Map tab in ArcGIS Pro. The World Imagery basemap was selected; this layer presents high resolution satellite and aerial imagery of the world. Another interesting feature of ArcGIS Pro is that any 2D map can be converted to a 3D scene for data visualization. Under the View tab, the Convert button was selected. Within this drop-down menu, the option To a Global Scene was selected. This converted the map into a 3D globe.

 

Converting the 2D map into a Global Scene     
Global Scene 3D Map

Under Properties of the scene layer impact points, the range setting was enabled for the “Age” attribute of the layer. The age attribute describes when the impact occurred in MYA (millions of years ago). The range was set between 1850 and 0 MYA, as this is the full range of the data in the layer. A range slider was added to the side of the scene. By dragging the slider, the points animatedly appear and disappear depending on their ages.

 

Enabling Range on the “Age” Attribute of the Impacts
Range Slider (display at 1315 MYA)

In order to start an animation, the Add button was selected in the View tab. This created and opened an Animation tab within ArcGIS Pro. In order to start the video, the Range of visible data was selected as 1850-1850. This way only the oldest impact crater is displayed. The scene was zoomed out for the first shot of the animation. By setting the Append Time to 5 seconds and selecting Append, the first clip of animation was created. This clip was 5 seconds. In order to display the progression of impacts occurring, the slider was dragged closer throughout time. By increasing Append time to 15 seconds and selecting Append, the animation clip was created. The animation clip is range aware therefore it will progress through the range slider up to where the slider was dragged throughout this append time. This process was repeated until the whole range was animated.

Range set at 1850-1850 in order to start animation
Append Animation to Video
Animation Timeline for video editing

After the range of ages of the impacts was animated, a camera path was animated in order to create an interesting visual. By zooming and changing the view of the map and using the append animation clip, a visualization of the satellite imagery of the impact craters was created. For example, the Sudbury crater was zoomed in upon and animated. Then, a paragraph of facts about the Sudbury crater was overlaid using the Overlay option in the Animation Tab. As well, a scale was overlaid using the same tool. This was done for three other craters and was added to the animation video.

Add overlay graphics to the video
Overlay with details about the impact

Finally, the animation was exported as a MP4 file in order to easily share the file.

The Final Video seen above was uploaded to YouTube.

Map Animation of Toronto’s Watermain Breaks (2015)

Audrey Weidenfelder
Geovis Project Assignment @RyersonGeo, SA8905, Fall 2016
mymap

For my geo-viz project, I wanted to create a map animation.  I decided to use CARTO, a web mapping application.

CARTO

CARTO is an open source web application software built on PostGIS and PostgreSQL open source spatial databases.  Users can manage data, run spatial analysis and design custom maps.  Within CARTO, there is an interface where SQL can be used to manipulate data, and a CartoCSS editor (a cartography language) to symbolize data.

CARTO has a tool called Torque that allows you to ‘bring your data to life’.  It’s good for mapping large data sets that have a time and/or date reference.  CARTO is well documented, and they offer guides and tutorials to assist users in their web mapping projects.  You can sign up for a free account here.  The free account is limited to 250Mb of storage after which charges apply.

The Process:  Connect to data, create new data set, add new column, symbolize

To create a map animation, simply connect to your data set either by dragging and dropping or browsing to your file.  If you don’t have data, you can search CARTO’s data library.  I had a file that I downloaded from the Toronto Open Data Catalogue.  I wanted to test CARTO’s claim that it can ‘bring large data sets to life’.  The file contained over 35,000 records of the city’s watermain breaks from 1990 to 2015.  I brought it into CARTO through the file browser, and in about 40 seconds all 35,000 point locations appeared in the map viewer.  From here, I explored the data, experimented with all the different visualization tools, and practised with CartoCSS to symbolize the data.

I decided to animate the 1,353 watermain breaks for 2015.  I had to filter the data set using a SQL statement to create a new data set containing only the 2015 breaks.  It’s easy to do using SQL.  You select from your table and column:

Select * from Breaks where Break_Year = 2015

CARTO asks if you wish to create a new data set from your selection – select ‘Yes’.  A new data set is created.  It will transfer your selected data into a new table along with the attributes associated with the selection.  You can keep the default table name or change the name of your table.  I re-named the table to ‘Watermain Breaks 2015’

From here, I wanted to organize the data by the seasons:  Spring, Summer, Winter and Fall.  This required creating a new column, selecting data according to the months and days of the season, entering the selected data into the column, and reassigning it a new name.

In data view, select ‘Add Column’ from the table designer, give it a name and a data type.  In this case I called it ‘Season’ and selected ‘String’ as the data type for text.  The next step was to update the column ‘Season’ based on values from the ‘Break_Date’ column that contained the dates of all breaks.  This was accomplished through the SQL Query editor, as so:

Update Watermain_Breaks _2015 set Season = ‘Spring’
where Break_Date >= ‘2015-03-21’ and Break_Date <= ‘2015-06-20’

The value of ‘Spring’ replaced the selected date range in the new column.  This was repeated for summer, fall and winter, substituting the appropriate date range for each season.

I then switched to the Category Wizard to symbolize this map layer.  Here you select the column you wish to symbolize.  I wasn’t pleased with the CARTO default symbolization, and there are were few options to choose from, so I used the CartoCSS editor to modify:

/** category visualization */
#breaks {
Marker-fill-opacity: 0.9;
Marker-placement: point;
Marker-type: ellipse;
Marker-width: 8;
Marker-allow-overlap: true;
}

#breaks[season=”Fall”] {
Marker-fill: #FF9900;
Marker-line-color: #FF9900
}

#breaks[season=”Spring”] {
Marker-fill: #229A00;
Marker-line-color: #229A00;
}

And so on …

To make the map layer interactive, I used the Infowindow designer in map view.  Here you can create pop-up windows based on a column in the table.  Options are available for a hover window or a clickable window.

Adding Layers

To add more interest to the map, I added the City of Toronto Neighbourhood boundaries so that the number of breaks per neighbourhood could be viewed.  I downloaded the shapefile from Toronto Open Data, connected the data set to my map and added it as a second layer.  I added info pop-ups, and changed the default symbolization with CartoCSS editor:

/** simple visualization */  #neighbourhoods_wgs84{
Polygon-fill: #FF6600;
Polygon-opacity: 0;
Line-color: #000000;
Line-width: 0.5;
Line-opacity: 1;
}

Animation

CARTO only allows animation on one map layer, and it does not permit info windows.  You also cannot copy a layer.  As such, I added a new layer by connecting to the watermain breaks data table, and then used the Torque Cat Wizard to animate the layer.

Animation is based on the column that contains either a date or time.  I selected the Break_Date column, and used CartoCSS editor to set the number of frames, duration of the animation, data aggregation to cumulative so that the points remained on the map, and then symbolized the data points to match the original watermain breaks layer.  A legend was then added to this layer.

CARTO has the option to add elements such as title, text boxes and images.  I added a title and a text box containing some facts about the city’s watermain breaks and pipe distribution.

The map animation can be viewed here .  Zoom in, pan around, find your neighbourhood, move the date slider, and select from the visible layers.

Note:  CARTO does not function well in Microsoft Edge

 

 

Displaying Brooklyn’s Urban Layers by Mapping Over 200 Years of Buildings

Renad Kerdasi
Geovis Course Assignment
SA8905, Fall 2015 (Rinner)

Growth in Brooklyn
Located at the far western end of Long Island, Brooklyn is the most populous of New York City’s five boroughs. The borough began to expand between the 1830s and 1860s in downtown Brooklyn. The borough continued to expand outwards as a result of a massive European immigration, the completion of the Brooklyn Bridge connecting to Manhattan, and the expansion of industry. By mid 1900s, most of Brooklyn was already built up as population increased rapidly.

Data
The data in the time series map are from PLUTO, which is a NYC open data site created by NYC Department of City Planning and released in 2015. The data contain information about each building located in the boroughs, including the year the construction of the building was completed (in numeric 4 digits format) and the building footprints. The building years range from 1800 to 2015, there are some missing dates in the dataset as well as some inaccuracy in the recorded dates. The data are available in Shapefile and Windows Comma Separated format, found on NYC Planning website: http://www.nyc.gov/html/dcp/html/bytes/dwn_pluto_mappluto.shtml

The Making of the Time Series
To present the structural episodes of Brooklyn’s built environment, QGIS 2.10 was utilized with the Time Manager plugin. QGIS is an open source GIS application that provides data visualization, editing, and analysis through functions and plugins (https://www.qgis.org/en/site/about/). The Time Manager plugin animates vector features based on a time attribute (https://plugins.qgis.org/plugins/timemanager/). This tool was effective in presenting a time series of Brooklyn’s building construction dates.

To create the time series, the PLUTO SHP was downloaded and prepared by removing any unnecessary fields. The columns of interest are: FID, Shape, and YearBuilt. Because we are interested in the time column, the formatting must fit with QGIS Time Manager. QGIS Time Manager requires timestamps to be in YYYY-MM-DD format whereas the building dates in the PLUTO SHP are in a four-digit format. Therefore, the date in the dataset must be modified to fit the Time Manager format before it can be brought into QGIS.

Table 1_BrooklynData

In QGIS, Time Manager plugin must be installed first. The SHP can then be added into QGIS as well as other Shapefiles needed: roads, highways, state boundaries, etc. Note: to use Time Manager, the data must be in SHP format.

Layer_BrooklynData

Once the data are added, the polygons (i.e. buildings) are styled based on age. This will be effective in distinguishing the oldest buildings from the newest. In QGIS, there are a large number of options available to apply different types of symbology to the data. The layer is styled based on the attribute Year Built, since the time series will show urban layers using building dates. Also, Graduated is chosen because features in the layer should not be styled the same way. The other data file, such as roads, highways, and state boundaries, are styled as well.

Once all the data are added and styled, it can be oriented and applied to the Time Manager plugin. To truly see the urban layers, the map is zoomed on the upper portion of Brooklyn. In Time Manager settings, the layer with building dates is added and the Start Time is the Year Built field, which includes the timestamp data. To get features to be configured permanently on the map, in the End Time option “No End Time” is selected. For animation options, each time frame will be shown for 100 milliseconds, and timestamp (i.e. built year) will be displayed on the map.

Layer_BrooklynData

In the Time Manager dock, the time frame is changed to years since the animation will be showing the year the construction of the building was completed. The size of the time frame will be 5 years. With these settings, each frame will display 5 years of data every 100 millisecond. Playing the video will display the animation inside QGIS, and one can see the time scrolling from 1800-2015 in the dock.

Dock_BrooklynData

Time Manager also enables you to export the animation to an image series using the “Export Video” button. Actual video export is not implemented in Time Manager. To play the animation outside of QGIS, various software applications can be used on the resulting image series to create a video file.

In addition, QGIS only allows users to insert a legend and title in the Composer Manager window. Currently, it is not possible to get the legend rendered in the main map window. One approach to generate a video with a legend is to create a dummy legend and add the image containing the legend into the PNGs that Time Manager produces. A dummy legend and a title for Brooklyn’s urban layers was created outside of QGIS, and added to each PNG.

Finally, to create a time-lapse and compile the images together, Microsoft Movie Maker was utilized. Other software applications can be used, including mancoder and avidemux.

Results

Link: https://youtu.be/52TnYAVxN3s