Archive for the ‘Research’ Category

Welcome Home, GIS Professionals – Ryerson Geography at URISA’s 54th Annual Conference

November 12th, 2016

The Urban and Regional Information Systems Association (URISA) held its first conference on “First Annual Conference on Urban Planning Information Systems and Programs” in 1963 at the University of Southern California. Now dubbed “GIS-Pro”, the conference and URISA as an organization are the preeminent destinations for exchange of best-practices among Geographic Information Systems (GIS) professionals. This year, Canada, the birth place of GIS, welcomed URISA back for its 54th annual conference held at Toronto’s Westin Harbour Castle hotel from Oct 31-Nov 3, 2016.

The conference drew over 350 participants, with some 200 from Canada (including 150 from Ontario) and most of the remainder from the United States. Representatives from Australia, Barbados, Japan, Malaysia, Republic of Korea, Saudi Arabia, South Africa, and the United Kingdom rounded out the pre-conference attendee list. URISA is greatly engaged in the professional development of its members, and consequently, over 100 participants held the GISP designation. URISA is a founding member of the GIS Certification Institute, which awards the “GISP” status and was an exhibitor and workshop organizer at the conference. URISA’s Vanguard Cabinet of young geospatial professionals, URISA’s GISCorps of worldwide GIS volunteers, its GIS Management Institute, and its regional chapters were all involved in organizing the conference. In one of the conference highlights, Esri Canada founder and president Alex Miller was inducted to the URISA GIS Hall of Fame. More information about URISA can be found at

Title slide - 3D-printed geography

Ryerson’s Department of Geography and Environmental Studies attended the conference with three speakers and ten student volunteers. In the unusual format of a luncheon presentation and discussion table (, I presented work with Dr. Claire Oswald on “3D-Printed Geography for Education, Outreach, and More?” This was a summary of one-and-a-half years of 3D-printing of terrain models and cityscapes, focusing on the processing of geospatial data into 3D printer-compliant format, and on the reception of this project among potential users such as conservation authorities. Our slides are available at A previous review of the project is available at

My former graduate students Justin Pierre and Richard Wen had signed up for a session on open-source geospatial software ( Justin presented on his Master of Spatial Analysis (MSA) major research paper “Developing an Argumentation Platform in an Open Source Stack”. His map-based discussion forum on Toronto’s bike lane network runs on Ryerson’s cloud at, albeit not always as reliably as we wish. Richard outlined his MSA thesis research on “Using Open Source Python Packages for Machine Learning on Vector Geodata”. He applied the “random forest” algorithm to the task of detecting outliers in OpenStreetMap data, with the goal of developing tools for semi-automated data input and quality control in volunteered geographic data. Richard’s code and thesis are available at Both of these student were part of the Geothink SSHRC Parternship Grant,, which supported their conference participation.

RyersonGeo booth with AR-sandbox at GIS-Pro2016

@RyersonGeo also had a booth in the GIS-Pro 2016 exhibit hall. While conference participants were interested in the Department’s programs and research expertise, the main attraction of our booth was an augmented-reality (AR) sandbox. The sandbox was built, set up, and staffed by our collaborators in the GIS team at the Central Lake Ontario Conservation Authority (CLOCA – CLOCA staff had attended Dr. Oswald’s GeovisUW workshop ( in June 2016 and were inspired by the visit of Ryerson’s Digital Media Experience Lab, which demo’ed an AR sandbox. In subsequent discussions about public outreach around surface- and groundwater protection, we proceeded with 3D-prining of CLOCA’s watershed geography and terrain, while CLOCA staff endeavoured to build the sandbox. The two displays were used by CLOCA at the 2016 Durham Children’s Groundwater Festival in late September. At the GIS-Pro 2016 conference, some participants were wondering about combining the two technologies, while others were interested in using the sandbox to model real-world terrain and simulating flooding. While accurate modeling of terrain and water flow may prove difficult, we are indeed planning to test the sandbox with semi-realistic scenarios.

CLOCA's AR-sandbox at GIS-Pro2016

In conclusion, applied GIS researchers and practicing GIS professionals are a friendly, close-knit group. The conference volunteers from our BA in Geographic Analysis, BA in Environment and Urban Sustainability, and MSA in Spatial Analysis programs were given a lot of free time and thoroughly enjoyed the conference. They were truly impressed by the large number and variation in GIS applications presented, and left the conference with a greater sense for the professional community. For me, the conference confirmed that research and development of GIS should be led by geographers, within Geography departments, as we are best positioned to understand the professional end-user’s needs, yet also have the technical expertise, at least @RyersonGeo, to contribute to GIS R&D.

Leveraging Open Data: International Perspectives Presented at URISA’s GIS-Pro 2016 Conference

November 11th, 2016

Guest post by Sarah Greene (@SarahAGreene), Master of Spatial Analysis (MSA) candidate, Ryerson University

This past week, URISA held its 54th annual GIS-Pro conference in Toronto, bringing together GIS professionals and businesses from around the world. The conference provided many interesting sessions including one focused entirely on open data. This session, titled “Leveraging Open Data” (, included government as well as private sector perspectives.

The session began with a presentation from the Government of North Carolina, discussing the importance of metadata. They are currently collaborating with a number of agencies to create and share a metadata profile to help others open up their data and understand how to implement the standards suggested. They have produced a living document which can be accessed through their webpage

The next speaker at the session represented Pitkin County in Colorado. They represent an open data success story with a number of great resources available for download on their website including high quality aerial imagery. An important aspect to their open data project was their engagement with their local community to understand what data should be opened, and then marketing those datasets which were released.

The Government of Ontario was also present as this session, presenting on the current status of open data for the province. The Ontario Government promotes an Open by Default approach and currently has over 500 datasets from 49 agencies available to download through their portal at They are working towards continuing to increase their open datasets available.

A presentation by MapYourProperty ( provided an interesting perspective from the private sector using open data to successfully run their business. They heavily depend on visualizing open data to provide a web-based mapping application for the planning and real estate community to search properties, map zoning information and create a due diligence report based on the information found. This is one example of many that exist in the private sector of open data helping build new companies, or help existing companies thrive.

Lastly, a representative from Esri Canada’s ( BC office wrapped up the session reminding us all of the importance of opening data. This included highlighting the seemingly endless benefits to open data, including providing information to help make decisions, supporting innovation, creating smart cities and building connections. Of course, open data is big business for Esri too, with the addition of ArcGIS Open Data as a hosted open data catalog to the ArcGIS Online platform.

This session showcased some great initiatives taking place in Canada and the United States that are proving the importance of opening up data and how this can be done successfully. It is exciting to see what has been taking place locally and internationally and it will be even more exciting to see what happens in the future, as both geospatial and a-spatial data products continue to become more openly available.

Geographic Analysis Explained through Pokemon GO

August 8th, 2016

Hello, pokemon trainers of the World! Today, I would like to explain Geographic Analysis using the ideas of the Pokemon GO game that you know only too well. I hope that you will return to the game with a good understanding of the geographic concepts and the geospatial technology behind it.

Safe for some serious cheating, you have to move around this thing called THE REAL WORLD with your location-enabled device in order to “catch’em all”. Smartphone producers make it really difficult to manipulate GPS location, because it is such a critical function of your device. So, unless you are truly close to that poke stop, you won’t be able to access its resources: free poke balls, razz berries, etc. In Geography, we often study the location of points-of-interest or services. For example, if you live or work close to a specific shopping mall or hospital, you are likely to use their services at one point or another. Or, if you are far away from a college or university and still choose to pursue higher education, you may have to move in order to be within reach of that institution.

To use a poke stop or gym, or to catch a pokemon, you do not need to be at their exact coordinate locations, but you need them to appear within your proximity circle as you move around. In Geographic Analysis, we often examine this “reach”, or catchment area, that is defined by proximity to locations of interest. For example, when a coffee chain looks to open a new store, Geographers will examine their competitors’ locations and surrounding neighbourhood profiles to determine whether there is a gap in coverage or whether there are catchment areas that include enough people of the right demographic to support an additional cafe. In Retail Geography, we call these areas “trade areas”. That’s why you can find clusters of Tim Horton’s, Second Cup, and/or Starbucks at major intersections where the geodemographics are favourable – yes, this is likely a Geospatial Analyst’s work! And that’s also why you can find clusters of poke stops in some of your favourite busy locations.

To support business decision-making, AKA “location intelligence”, Geographers use data on population, household incomes and employment, the movement of people, and the built environment. If you have ever “watched” for different locations, you will have noticed great variation in the pokemon spawn density and frequency. For example, in our screenshots below you can see tons of pokemon in downtown Toronto, but not a single one in an area of rural Ontario. Similarly, there are dozens of poke stops and several gyms within walking distance in the City but a lone poke stop in rural Ontario. The Pokemon GO vendor, Niantic, seems to be using geodemographics in determining where pokemon will spawn. They make it more likely for pokemon to spawn where there are “clients”: that is, yourselves, the trainers/players.

(a)IMG_0035 (b)IMG_0042 (c)IMG_0099

Fig. 1: poke stops locations and pokemon appearances in downtown Toronto (a, b), compared to rural Ontario (c)

Geographic space is a unique dimension that critically influences our lives and societies. The spatial distribution of people and things is something that Geographers are studying. Just like the spawning of pokemon in general, the appearance of the different types of pokemon is not randomly distributed either. For example, it has been shown that water-type pokemon are more likely to appear near water bodies. See all those Magicarps near the Toronto lakefront in the screenshot below? A few types of pokemon even seem restricted to one continent such as Tauros in North-America and won’t appear on another (e.g., Europe). The instructions by “Professor Willow” upon installation of the app actually refer to this regional distribution of pokemon. I also believe that the points-of-interest, such as buildings, that serve as poke stops, determine the pokemon type spawning near them. For example, the Ontario Power Building at College St. and University Ave. in Toronto regularly spawns an Elektrabuzz, as shown in the last screenshot below.

(a)IMG_0026 (b)pokemon_cluster-of-magicarps-at-lakefront (c)algorithmic-regulation_aka-pokemon-go

Fig.2: (a), “Professor Willow” explaining his interest in studying the regional distribution of pokemon (what a great-looking Geographer he is!); screenshots of with (a) Magicarps at the Toronto lakefront and (b) an Elektrabuzz near the Ontario Power Building

In Environmental Geography, we often analyze (non-pokemon) species distribution, which is also not random. The availability of suitable habitat is critical, just like for pokemon. In addition, spatial interactions between species are important – remember the food chain you learned about in school. I am not sure that different pokemon types interact with one another; maybe that could be the topic of your first course project, as you enter a Geography program at university?

The techniques that we use within Geographic Information Systems (GIS) include suitability mapping, distance and buffer analysis, and distance decay. Distance decay means that it is becoming less and less likely to encounter a species as you move away from suitable habitat. Or in the business field, it is becoming less and less likely that people will shop at a specific mall the further away they live from it. A buffer is an area of a specified distance around a point, line, or polygon, just like the proximity circle around your pokemon avatar. GIS software can determine if other features are within the buffer around a location. Instead of enabling access to poke stops or gyms around your avatar, Geographers would use buffer analysis to determine which residents have access to public transit, e.g. if they are within walking distance of 500m or 1km of a transit stop.

A final thought about how Pokemon GO has brought Geography to the headlines concerns important professional and societal challenges that Geographers can tackle. These range from map design and online map functionality to crowdsourcing of geospatial data, as well as the handling of big data, privacy concerns, and ultimately the control of people’s locations and movement. The now-defunct Web map used Esri online mapping technology, one of the world-leading vendors of GIS software and promoters of professional Geography. Another approach, which is used by, has trainers (users) report/upload their pokemon sightings in real-time. This geospatial crowdsourcing comes with a host of issues around the accuracy of, and bias in, the crowdsourced data as well as the use of free labour. For example, poke stops were created by players of a previous location-based game called “Ingress” and are now used by Niantic in a for-profit venture – Pokemon GO! Finally, you have all read about the use and misuse of lure to attract people to poke stops at different times of day and night. The City of Toronto recently requested the removal of poke stops near the popular island ferry terminal for reasons of pedestrian control and safety. Imagine how businesses or government could in the future control our movement in real space with more advanced games.

I hope I was able to explain how Pokemon GO is representative of the much larger impact of Geography on our everyday lives and how Geographers prepare and make very important, long-term decisions in business and government on the basis of geospatial data analysis. Check out our BA in Geographic Analysis or MSA in Spatial Analysis programs to find out more and secure a meaningful and rewarding career in Geography. And good luck hunting and training more pokemon!

3D Printed Geographies – Techniques and Examples

April 25th, 2016

As a follow-up to my post on “Geospatial Data Preparation for 3D Printed Geographies” (19 Sept 2015), I am providing an update on the different approaches that I have explored with my colleague Dr. Claire Oswald for our one-year RECODE grant entitled “A 3D elevation model of Toronto watersheds to promote citizen science in urban hydrology and water resources”. The tools that we have used to turn geospatial data into 3D prints include the program heightmap2stl; direct loading of a grey scale image into the Cura 3D modeling software; the QGIS plugin DEMto3D; the script shp2stl.js; and a workflow using Esri’s ArcScene for 3D extraction, saving in VRML format, and translating this file into STL format using the MeshLab software.

The starting point: GIS and heightmap2stl

Being a GIS specialist with limited knowledge of 3D graphics or computer-aided design, all of the techniques used to make geospatial data printable rely heavily on the work of others, and my understanding of the final steps of data conversion and 3D print preparation is somewhat limited. With this in mind, the first approach to convert geospatial data, specifically a digital elevation model, used Markus Fussenegger’s Java program heightmap2stl, which can be downloaded from and used according to detailed instructions on “Converting DEMs to STL files for 3D printing” by James Dittrich of the University of Oregon. The process from QGIS or ArcGIS project to greyscale map image to printable STL file was outlined in my previous post at

Quicker and not dirtier: direct import into Cura

The use of the heightmap2stl program in a Windows environment requires a somewhat cumbersome process using the Windows command line and the resulting STL files seemed exceedingly large, although I did not systematically investigate this issue. I was therefore very pleased to discover accidentally that the Cura software, which I am using with my Lulzbot Taz 5 printer, is able to load greyscale images directly.

The following screenshot shows the available parameters after clicking “Load Model” and selecting an image file (e.g. PNG format, not an STL file). The parameters include the height of the model, height of a base to be created, model width and depth within the available printer hardware limits, the direction of interpreting greyscale values as height (lighter/darker is higher), and whether to smoothen the model surface.


The most ‘popular’ model created using this workflow is our regional watershed puzzle. The puzzle consists of a baseplate with a few small watersheds that drain directly into Lake Ontario along with a set of ten separately printed watersheds, which cover the jurisdiction of the Toronto and Region Conservation Authority (TRCA).

Controlling geographic scale: QGIS plugin DEMto3D

Both of the first two approaches have a significant limitation for 3D printing of geography in that they do not support controlling geographic scale. To keep track of scale and vertical exaggeration, one has to calculate these values on the basis of geographic extent, elevation differential, and model/printer parameters. This is where the neat QGIS plugin DEMto3D comes into play.

As can be seen in the following screenshot, DEMto3D allows us to determine a print extent from the current QGIS project or layer extents; set geographic scale in conjunction with the dimension of the 3D print; specify vertical exaggeration; and set the height at the base of the model to a geographic elevation. For example, the current setting of 0m would print elevations above sea level while a setting of 73m would print elevations of the Toronto region in relation to the surface level of Lake Ontario. One shortcoming of DEMto3D is that vertical exaggeration oddly is limited to a factor of 10, which we found not always sufficient to visualize regional topography.


Using DEMto3D, we recently printed our first multi-part geography, a two-piece model of the Oak Ridges Moraine that stretches over 200km in east-west direction to the north of the City of Toronto and contains the headwaters of streams running south towards Lake Ontario and north towards Lake Simcoe and the Georgian Bay. To increase the vertical exaggeration for this print from 10x to 25x, we simply rescaled the z dimension in the Cura 3D printing software after loading the STL file.

Another Shapefile converter: shp2stl

The DEMto3D plugin strictly requires true DEM data (as far as I have found so far), thus it would not convert a Shapefile with building heights for the Ryerson University campus and surrounding City of Toronto neighbourhoods, which I wanted to print. Additionally, the approach using a greyscale image of campus building heights and one of the first two approaches above also did not work, as the 3D buildings represented in the resulting STL files had triangulated walls.

In looking for a direct converter from Shapefile geometries to STL, I found Doug McCune’s shp2stl script at and his extensive examples and explanations in a blog post on “Using shp2stl to Convert Maps to 3D Models“. This script runs within the NodeJS platform, which needs to be installed and understood – the workflow turned out to be a tad too complicated for a time-strapped Windows user. Although I managed to convert the Ryerson campus using shp2stl, I never  printed the resulting model due to another, unrelated challenge of being unable to add a base plate to the model (for my buildings to stand on!).

Getting those walls straight: ArcScene, VMRL, and Meshlab

Another surprise find, made just a few days ago, enabled the printing of my first city model from the City of Toronto’s 3D massing (building height) dataset. This approach uses a combination of Esri’s ArcScene and the MeshLab software. Within ArcScene, I could load the 3D massing Shapefile (after clipping/editing it down to an area around campus using QGIS), define vertical extrusion on the basis of the building heights (EleZ variable), and save the 3D scene in the VRML format as a *.wrl (“world”) file. Using MeshLab, the VRML file could then be imported and immediately exported in STL format for printing.

While this is the only approach included in this post that uses a commercial tool, ArcScene, it is likely that the reader can find alternative workflow based on free/open-source software to extrude Shapefile polygons and turn them into STL, whether or not this requires the intermediate step through the VRML format.

Ryerson Geographers at AAG 2016

March 28th, 2016

Another year has passed, and another annual meeting of the Association of American Geographers (AAG) is about to start in San Francisco this week. The Department of Geography and Environmental Studies at Ryerson is sending its usual strong complement to AAG 2016, although the writer of these lines is sadly staying behind in a cold and rainy Toronto.

Contributions from @RyersonGeo have a traditional focus in Business Geography, with additional abstracts in the areas of urban forest, population health, migration & settlement, local food, renewable energy, and sustainability science. In approximate chronological order of presentation:

In addition to these contributions, Dr. Hernandez also serves as chair, introducer, organizer, and/or panelist of sessions on

  • BGSG Career Achievement Award: A Conversation with Ken Smith
  • Connecting Practitioners and Students – Advice on Career Development in the Field of Location Intelligence
  • Location Intelligence Trends in the Contemporary Omni-channel Retail Marketplace
  • Retail and Business Geography I & II

Dr. Millward also serves as chair of the session on “Arboriculture and Urban Forestry” and Dr. Steenberg is a panelist in the session entitled “Disrupt Geo 1: new ideas from the front lines of maps, mobile, and big data”.

We wish our colleagues and all participants a productive and enjoyable AAG 2016!

Victoria Fast and Daniel Liadsky receive Ryerson’s top award

November 11th, 2015

Blog post co-authored by Victoria Fast, Daniel Liadsky, and Claus Rinner

Ryerson’’s Department of Geography and Environmental Studies is celebrating two gold medal recipients this fall. The Ryerson Gold Medals are the University’s highest honours, presented annually to one graduate of each Faculty. Victoria Fast (PhD in Environmental Applied Science and Management, supervised by Dr. Claus Rinner) received the Gold Medal for the interdisciplinary programs housed at the Yeates School of Graduate Studies, while Daniel Liadsky (MSA in Spatial Analysis, supervised by Dr. Brian Ceh) received the Gold Medal for the Faculty of Arts.

Victoria’’s PhD research investigated the potential of novel geographic information techniques to reshape the interaction of government with community organizations and citizens through crowdsourcing and collaborative mapping. The study applied a VGI systems approach (Fast & Rinner 2014) to actively engage with urban food stakeholders, including regional and municipal government, NGOs, community groups, and individual citizens to reveal and map uniquely local and community-driven food system assets in Durham Region. The Durham Food Policy Council and Climate Change Adaptation Task Force are currently using the results to support informed food policy and program development. Victoria’s research contributes to, a SSHRC Partnership Grant on the impact of the participatory Geoweb on government-citizen interactions.

Daniel’’s research in the Master of Spatial Analysis (MSA) examined how dietary intake is mediated by individual, social, and environmental factors. The Toronto-based study was stratified by gender and utilized self-reported data from the Canadian Community Health Survey as well as measures of the food environment derived from commercial retail databases. The results uncovered some of the complex interactions between the food environment, gender, ethnocultural background, and socioeconomic restrictions such as low income and limited mobility. In addition and as part of an unrelated investigation, Daniel undertook a feasibility study into a mapping and data analytics service for the non-profit sector.



GIS Day 2015 at Ryerson – A Showcase of Geographic Information System Research and Applications

November 3rd, 2015

Ryerson students, faculty, staff, and the local community are invited to explore and celebrate Geographic Information Systems (GIS) research and applications. Keynote presentations will outline the pervasive use of geospatial data analysis and mapping in business, municipal government, and environmental applications. Research posters, software demos, and course projects will further illustrate the benefits of GIS across all sectors of society.

Date: Wednesday, November 18, 2015
Time: 1:00pm-5:00pm
Location: Library Building, 4th Floor, LIB-489 (enter at 350 Victoria Street, proceed to 2nd floor, and take elevators inside the library to 4th floor)

Tentative schedule:

  • 1:00 Soft kick-off, posters & demos
  • 1:25 Welcome
  • 1:30-2:00 Dr. Namrata Shrestha, Senior Landscape Ecologist, Toronto & Region Conservation Authority
  • 2:00-2:30 posters & demos
  • 2:30-3:00 Andrew Lyszkiewicz, Program Manager, Information & Technology Division, City of Toronto
  • 3:00-3:30 posters & demos
  • 3:30-4:00 Matthew Cole, Manager, Business Geomatics, and William Davis, Cartographer and Data Analyst, The Toronto Star
  • 4:00 GIS Day cake!
  • 5:00 End

GIS Day is a global event under the motto “Discovering the World through GIS”. It takes place during National Geographic’s Geography Awareness Week, which in 2015 is themed “Explore! The Power of Maps”, and aligns with the United Nations-supported International Map Year 2015-2016.

Event co-hosted by the Department of Geography & Environmental Studies and the Geospatial Map & Data Centre. Coffee/tea and snacks provided throughout the afternoon. Contact: Dr. Claus Rinner

Geospatial Data Preparation for 3D Printed Geographies

September 19th, 2015

I am collaborating with my colleague Dr. Claire Oswald on a RECODE-funded social innovation project aimed at using “A 3D elevation model of Toronto watersheds to promote citizen science in urban hydrology and water resources”. Our tweets of the first prototypes printed at the Toronto Public Library have garnered quite a bit of interest – here’s how we did it!


The process from geography to 3D print model includes four steps:

  1. collect geospatial data
  2. process and map the data within a geographic information system (GIS)
  3. convert the map to a 3D print format
  4. verify the resulting model in the 3D printer software

So far, we made two test prints of very different data. One is a digital elevation model (DEM) of the Don River watershed, the other represents population density by Toronto Census tracts. A DEM for Southern Ontario created by the Geological Survey of Canada was downloaded from Natural Resources Canada’s GeoGratis open data site at It came in a spatial resolution of 30m x 30m grid cells and a vertical accuracy of 3m.

The Don River watershed boundary from the Ontario Ministry of Natural Resources was obtained via the Ontario Council of University Libraries’ geospatial portal, as shown in the following screenshot.

Download of watershed boundary file

The population density data and Census tract boundaries from Statistics Canada were obtained via Ryerson University’s Geospatial Map and Data Centre at (limited to research and teaching purposes).

The Don River watershed DEM print was prepared in the ArcGIS software by clipping the DEM to the Don River watershed boundary selected from the quaternary watershed boundaries. The Don River DEM was visualized in several ways, including the “flat” greyscale map with shades stretched between actual minimum and maximum values, which is needed for conversion to 3D print format, as well as the more illustrative “hillshade” technique with semi-transparent land-use overlay (not further used in our 3D project).

DEM of Don River watershedHillshade of Don River valley at Thorncliffe Park

The population density print was prepared in the free, open-source QGIS software. A choropleth map with a greyscale symbology was created, so that the lighter shades represented the larger population density values (yes, this is against cartographic design principles but needed here). A quantile classification with seven manually rounded class breaks was used, and the first class reserved for zero population density values (Census tracts without residential population).


In QGIS’ print composer, the map was completed with a black background, a legend, and a data source statement. The additional elements were kept in dark grey so that they would be only slightly raised over the black/lowest areas in the 3D print.


The key step of converting the greyscale maps from the GIS projects to 3D print-compliant STL file format was performed using a script called “heightmap2stl.jar” created by Markus Fussenegger. The script was downloaded from, and used with the help of instructions written by James Dittrich of the University of Oregon, posted at Here is a sample run with zero base height and a value of 100 for the vertical extent.

Command for PNG to STL conversion

The final step of pre-print processing involves loading the STL file into the 3D printer’s proprietary software to prepare the print file and check parameters such as validity of the structure, print resolution, fill options for hollow parts, and overall print duration. At the Toronto Public Library, 3D print sessions are limited to two hours. The following screenshot shows the Don River DEM in the MakerBot Replicator 2 software, corresponding to the printer used in the Library. Note that the model shown was too large to be printed in two hours and had to be reduced below the maximum printer dimensions.

Don River watershed model in 3D printing software

The following photo by Claire Oswald shows how the MakerBot Replicator 2 in the Toronto Reference Library’s digital innovation hub prints layer upon layer of the PLA plastic filament for the DEM surface and the standard hexagonal fill of cavities.

DEM in printing process - photo by C. Oswald

The final products of our initial 3D print experiments have dimensions of approximately 10-20cm. They have made the rounds among curious-to-enthusiastic students and colleagues. We are in the process of improving model quality, developing additional models, and planning for their use in environmental education and public outreach.

The printed Don River watershed model

3D-printed Toronto population density map

Normalization vs. Standardization – Clarification (?) of Key Geospatial Data Processing Terminology using the Example of Toronto Neighbourhood Wellbeing Indicators

November 30th, 2013

In geospatial data processing, the terms “normalization” and “standardization” are used interchangeably by some researchers, practitioner, and software vendors, while others are adamant about the differences in the underlying concepts.

Krista Heinrich, newly minted Master of Spatial Analysis (MSA) and a GIS Analyst at Esri Canada, wrote her MSA major research paper on the impact of variable normalization and standardization on neighbourhood wellbeing scores in Toronto. More specifically, within a SSHRC-funded research project on multi-criteria decision analysis and place-based policy-making, we examined the use of raw-count vs. normalized variables in the City of Toronto’s “Wellbeing Toronto” online tool. And, we explored options to standardize wellbeing indicators across time. Here is what Krista wrote about these issues in a draft of her paper:

In most analysis situations involving multiple data types, raw data exist in a variety of formats and measures, be it monetary value, percentages, or ordered rankings. This in turn presents a problem of comparability and leads to the requirement of standardization. While Böhringer, & Jochem (2007), emphasize that there is no finite set of rules for the standardization of variables in a composite index, Andrienko & Andrienko (2006) state that the standardization of values is a requirement.

Several standardization techniques exist including linear scale transformations, goal standardization, non-linear scale transformations, interval standardization, distance to reference, above and below the mean, z scores, percentage of annual differences, and cyclical indicators (Dorini et al, 2011; Giovanni, 2008; Nardo et al., 2005; Malczewski, 1999).  It should be noted however, that there is inconsistency among scholars as to the use of terms such as normalization and standardization.

While Giovannini (2008) and Nardo et al. (2005) categorize standardization solely as the use of z-scores, they employ the term normalization to suggest the transformation of multiple variables to a single comparable scale. Additionally, Ebert & Welsch (2004) refer to Z score standardization as the definition of standardization and place this method, along with the conversion of data to a 0 to 1 scale, referred to as ‘ranging’, as the two most prominent processes of normalization. According to Ebert & Welsch (2004), “Normalization is in most cases a linear transformation of the crude data, involving the two elementary operations of translation and expansion.” In contrast, other scholars classify the transformation of raw values to a single standardized range, often 0.0-1.0, as standardization (Young et al., 2010A; Malczewski, 1999; Voogd, 1983) while Dailey (2006), in an article for ArcUser Online, refers to the normalization of data in ArcMap as the process of standardizing a numerator against a denominator field. […]

In this paper, we employed the term standardization to define the classification of raw values into a single standardized scale and in particular, through the examination of linear scale transformations and their comparison with Z score standardization.  The term normalization is used in this paper to describe the division of variables by either area or population, as is referred to by Dailey (2006), therefore regularizing the effect that the number of individuals or the size of an area may have on the raw count values in an area. “

In other words, the way we use the two terms, and the way we think they should be used in the context of spatial multi-criteria decision analysis and area-based composite indices, standardization refers to making the values of several variables (indicators, criteria) comparable by transforming them to the same range of, e.g.,  0-to-1. In contrast, normalization refers to the division of a raw-count variable by a reference variable, to account for different sizes of enumeration areas.

Unfortunately, I have to admit that in my cartography course, following the excellent textbook by Slocum et al. (2009), I am using the term “standardization” for the important concept of accounting for unit sizes. For example, choropleth maps should only be made for standardized (i.e., normalized!) variables, never for raw-count data (a great rationale for which is provided at  Furthermore, high-scoring blog posts at and define normalization as the rescaling to the 0-to-1 range (our definition of standardization) and standardization as the z-score transformation of a variable. Oops, did I promise clarification of these terms ?-)

In case you are wondering about Krista’s results regarding the Wellbeing Toronto tool: It depends! She discusses an example of a variable where normalization changes the spatial patterns dramatically, while in another example, spatial patterns remain very similar between raw-count and normalized variables. Standardization was used to make wellbeing indicators from 2008 comparable to those from 2011, as we will report at the Association of American Geographers (AAG) annual meeting in April 2014. Our abstract (URL to be added when available) was co-authored by Dr. Duncan MacLellan (Ryerson, Politics and Public Admin department), my co-investigator on the above-mentioned research grant, and Kathryn Barber, a student in Ryerson’s PhD in Policy Studies program.

Awards Season

February 22nd, 2013

Regular readers of this blog, if they existed, would have noticed a new static “page” listing various awards, scholarships, and bursaries for students in Cartography, Geography, and GIScience. January/February and the spring seem to have clusters of deadlines for these competitions, in which we will see more Ryerson Geography students participate this year!

Today, Ryerson University officially announced the recipients of the research awards handed to faculty members, and you will find yours truly as one of two awardees from the Faculty of Arts: Ryerson maintains a comprehensive approach to faculty contributions to knowledge, which is labeled as Scholarly, Research and Creative Activity (SRC). This year’s Faculty SRC Awards recognize outstanding achievements by faculty members in the 2011/12 academic year.

I would like to acknowledge my students, who continue to play a significant role in my research program, including those in our BA in Geographic Analysis and Master of Spatial Analysis (MSA) programs. For example, both peer-reviewed journal articles contributing to the above SRC Award were based on MSA students’ major research papers. As always, details on my team’s scholarship can be found on my homepage,, and many publications are posted with full text in Ryerson’s institutional repository,