Smarter Planning Decisions with Geospatial Data

As more data on our environment becomes openly available, new ways to use it in design and decision-making emerge. A-Konsultit, a Finnish architectural design firm, and Sipoo, a municipality, set out to use geospatial data in a novel way.

“We got a commission from Sipoo to devise a method for determining development rights in rural areas. The work was part of their district planning process. We already had ideas about how to use geospatial data for locational evaluation, but, now, we had a chance to test it out in practice,” Matti Heikkinen, architect at A-Konsultit, tells.

Developing Rural Areas Favorably

Architect Matti Heikkinen

Planning what and how much to build in sparsely populated areas follows a convention that does not take into account socio-environmental factors. If no town plan exists, Finnish municipalities use a simple formula that outputs the allowed development rights as a percentage of the real estate area. Its purpose is to treat land owners fairly.

The problem with a mechanical calculation is that it fails to develop areas that are most favorable for a new building – for example, a large estate far away from main roads, schools, or public utilities can enjoy generous air rights, whereas a smaller, more favorably located area gets next to nothing.

A-Konsultit set out to create an assessment system that would take into account various environmental factors and determine development rights accordingly. The method they were developing works for any type of location-based evaluation both in urban environments and sparsely populated areas. They got funding from the Finnish government’s KIRA-digi project that aims at digitalizing built environment processes.

Open Geospatial Data as a Basis

There’s already a lot of open geospatial data available in a digital format. However, using it as a basis for environmental analyses is not always easy because of incompatible data models.

A-Konsultit was able to collect and combine GIS data on Sipoo properties, infrastructure, topography, soil, and so on. Fortunately, the data followed national standards, which made the process straightforward.

As an EU country, Finland is committed to the Union’s INSPIRE directive. It defines common, EU-wide standards for 34 spatial data themes. Once fully implemented, designers and software developers can make use of the huge repositories of geospatial data across Europe.

Creating an Assessment Framework

A-Konsultit had to come up with evaluation criteria to determine the value of various environmental elements in respect to a location – for example, distance from a bus route or a sewer. To do that, they created a grid that covered the whole district. Each rectangular cell of the grid got a certain number of points based on the criteria. The result looks like a heat map with colors referring to the point count.

“In practice, we created several maps, laid them on top of each other, and calculated the sum of the plusses and minuses of each rectangle. The higher the score, the better a location is as a building site,” Heikkinen explains. “We used QGIS for maps and Excel spreadsheets for the calculations.”

Example of the evaluation grid

Heikkinen admits that as mathematical, open, and objective as the process looks like, it has subjective aspects. A team that sets the criteria has a great influence on the outcome. For land owners, the air rights correlate with the value of the property. On the other hand, having high development rights in an unfavorable location does not add much value.

According to Heikkinen, the Sipoo project is unique because that the geospatial evaluation leads directly to legally binding decisions. So far, he has not discovered similar solutions elsewhere. The city of Stockholm has used an analogous method in district planning, but at a strategic level.

Automating the Design Process

The principle demonstrated in Sipoo applies to all kinds of environmental evaluation scenarios. Depending on the purpose of the analysis, the evaluation can be augmented with various calculation algorithms and scripts. Apart from established GIS sources, the data could be collected from mobile devices and other real-time sources.

Heikkinen envisions advanced applications of the technology. An algorithmic design app could use geospatial data to generate optimized 3D city models. The app could analyze the results from various points of view – for example, windiness or noise. If the user is not be satisfied, they can restart the process by adjusting certain parameters to get different results. It’s easy to imagine that machine learning would also come into play at some point in the process.

“In an environment that is becoming more complex every day, politicians and designers should take a huge number of variables into account,” says Heikkinen. “For humans, that’s becoming impossible. Machines can manage the flow of information and help in reaching a balanced design solution.”

If you want to learn more about the experiment, contact Matti Heikkinen at matti.heikkinen(Replace this parenthesis with the @ sign)a-konsultit.fi

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