Hello, everyone! Welcome back to the blog.

Lately, as I dive deep into urban density projects, specifically working through an urban density and infill analysis for the City of Burlington, I constantly question the physical consequences of how we construct our urban environments. Planners frequently discuss density in terms of housing economics and transit efficiency, but we must also address the severe environmental impacts of our design choices.

As urbanization accelerates, the Urban Heat Island (UHI) effect turns many of our cities into concrete ovens. Dense building materials and sprawling impermeable pavements absorb and trap heat, drastically altering our local climate. However, we possess the right tools to fight back. Geographic Information Systems (GIS) provide much more than just mapping capabilities; they serve as a powerful design for climate adaptation.

Recently, I explored two fascinating studies that tackle the UHI effect and urban climate resilience from completely different, yet perfectly complementary angles of spatial analysis. By bringing a global methodology into a direct dialogue with a local application right here in the Greater Toronto Area, we can see exactly how geospatial data science builds better cities.

Here is a look at how we can cool our urban spaces, from the layout of our streets down to the tops of our roofs.

Designing with the Wind (The Macro Approach)

Land Surface Temperature (LST) estimation of the study region of Zhumadian City, demonstrating the expansion of high-temperature zones between 2008 (a) and 2018 (b).

Before we can cool a city, we have to find the heat. A recent study by researchers Kang-Li Wu and Liang Shan focused on the central urban area of Zhumadian City in Henan Province, China. The researchers did not just map the problem; they engineered a solution by designing “urban wind corridors”.

First, the team utilized Remote Sensing, specifically comparing Landsat 5 satellite imagery from 2008 and Landsat 8 imagery from 2018, to track land surface temperatures. They discovered that high-temperature zones expanded significantly alongside rapid urban sprawl and high-density land development over that decade. To channel cleaner, cooler airflow from surrounding rural areas directly into these urban hotspots, the researchers integrated GIS to build a massive 3D digital city model. They then employed Computational Fluid Dynamics (CFD) to simulate how summer winds move through the city at the pedestrian level, roughly 1.5 to 2.0 meters above the ground.

The simulations revealed powerful street-level insights, particularly exposing the danger of the modern “superblock” concept. When developers place continuous, massive high-rise buildings on the outer perimeter of a community to surround inner low-rise buildings, these peripheral structures act as giant dams. They completely block prevailing summer winds from entering the neighbourhood. Similarly, in older districts, traditional enclosed-courtyard buildings lacked sufficient architectural openings for wind to pass through.

A large-scale simulation analysis identifying major urban wind corridor paths in the central urban area of Zhumadian City, based on prevailing southerly summer winds.

To fix this, the researchers concluded that planners must actively adjust urban morphology. By establishing first-level wind corridors measuring 200 to 300 meters wide, breaking up long “wall” buildings on the windward side and aligning new street orientations with prevailing winds, city engineers can literally “make way for the wind”.

Greening the Skyline (The Micro Approach)

A map of the study area in Toronto, Canada, displaying ward boundaries alongside general land cover patterns.

While establishing city-wide wind corridors provides an incredible strategy for new suburban developments, what do we do about existing, highly dense downtown cores? We cannot simply tear down skyscrapers in downtown Toronto to carve out a 300-meter-wide wind channel. This is where we pivot from structural layout to surface cover.

A fascinating 2025 study by Jung, Gomes, and Remme looked right here in our own backyard: Toronto, Ontario. The City of Toronto implemented a pioneering “Green Roof Bylaw” in 2009, recognizing green roofs as vital nature-based solutions. However, the researchers noted that the municipal government lacked a strategic spatial plan to maximize these benefits; they were not explicitly telling developers where a green roof would do the most good.

Using GIS spatial analysis and the InVEST software suite, the team modelled the biophysical priority areas for four key ecosystem services: flood regulation, temperature regulation, air quality regulation, and habitat provision. The spatial data revealed striking patterns. For instance, priority areas for temperature and air quality regulation clustered heavily around industrial zones and highways. By strategically retrofitting rooftops in these specific high-priority areas—such as the densely built-up Spadina-Fort York ward—the city can simultaneously retain stormwater runoff, cool the surrounding air, trap particulate matter, and provide stepping-stone habitats for urban biodiversity.

The Citizen-Weighted Map

What makes the Toronto study particularly brilliant is the human element. Urban planning should never exist in a vacuum, completely detached from the people who actually live and breathe in the city.

In addition to running their biophysical InVEST models, the researchers conducted a city-wide survey of 402 Torontonians to understand which ecosystem services the public genuinely values most. The survey revealed a fascinating disconnect between pure biophysical need and human preference. Biophysically, the models indicated that flood regulation scored the highest mean priority across the city. However, the citizens ranked air quality regulation as their absolute number-one priority (43% of top-ranked votes), followed by habitat provision, temperature regulation, and, finally, flood regulation.

Priority areas for green roof placement based on four combined ecosystem services, comparing purely biophysical weighting (A) with citizen-preference weighting (B). Map (C) shows the spatial overlap and differences between the two approaches in the Spadina-Fort York area
Computational Fluid Dynamics (CFD) analysis of a pedestrian wind field in a new residential district, illustrating how high-rise buildings on the perimeter can restrict natural ventilation for the inner buildings.

The researchers then applied these citizen preferences to their spatial models. Despite the differences in individual service rankings, the citizen-weighted priority maps and the purely biophysical priority maps aligned exceptionally well. Both models highlighted the same hotspots in central wards and industrial areas. This proves that leveraging spatial data alongside public participatory governance ensures we build green infrastructure exactly where the environment demands it and where the community supports it. Only a small fraction of Toronto’s roofs (0.2% biophysically, 1.2% citizen-weighted) fall into the absolute highest category of hotspot overlap for all four services, giving the city a highly targeted starting point for new green infrastructure.

The Future of Climate Resilience

These two studies perfectly illustrate the macro and micro scales of geospatial problem-solving. We tackle the UHI effect structurally in Zhumadian by shaping the layout of an entire neighbourhood to catch prevailing summer breezes, and at the surface level in Toronto by using spatial targeting to plant vegetation on specific downtown rooftops. Spatial intelligence unlocks both solutions. By integrating tools such as Remote Sensing, CFD, and GIS, we do not just build within our environment; we actively design with nature to construct resilient, breathable, and sustainable cities for the future!

Now I want to hear from you! Have you ever used 3D Analyst or Spatial Analyst to model climate variables in your own city? What other nature-based solutions do you think we should be mapping in the GTHA? Let me know your thoughts in the comments below!

Citations

The Zhumadian Wind Corridor Study

Wu, Kang-Li, and Liang Shan. “Make Way for the Wind—Promoting Urban Wind Corridor Planning by Integrating RS, GIS, and CFD in Urban Planning and Design to Mitigate the Heat Island Effect.” Atmosphere, vol. 15, no. 3, 2024, p. 257. MDPI, https://doi.org/10.3390/atmos15030257.

The Toronto Green Roof Study

Jung, Matthew, Sharlene L. Gomes, and Roy P. Remme. “Strategic Green Roof Placement in Toronto to Maximize Benefits While Incorporating Citizen Preferences.” Urban Forestry & Urban Greening, vol. 107, 2025, p. 128798. ScienceDirect, https://doi.org/10.1016/j.ufug.2025.128798.