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Configuring custom metrics in ArcGIS Urban to inform development design

ArcGIS Urban is a web-based Esri solution that supports urban planning and decision-making through simplification of complex zoning requirements and impactful 3D visualizations of potential building forms. While it can be used to create an impressive model containing city-wide plans and projects, one of its most effective planning features is the ability to configure custom metrics, important values that can be used to assess scenario performance in various aspects. In this blog post, we’ll follow an example that makes use of greenspace metrics to compare and assess alternative development scenarios and explore the underlying potential of metrics configuration.

View of the metrics example study area and the surrounding city model in ArcGIS Urban.

ArcGIS Urban is a web-based solution that streamlines the planning process and can be used to generate visually impactful 3D massings, benefitting municipal governments, developers, and planning students alike. However, what really makes ArcGIS Urban an effective tool for planning, beyond pure visualization, is the capability to compute relevant metrics of interest automatically. These metrics are a central component of planning and typically one of the primary factors driving planning decisions. Metrics such as numbers of residential units, jobs created, or the estimated revenue generated by proposed development are important factors to consider when comparing various design scenarios. With ArcGIS Urban, you have the ability to configure and display custom metrics for your Urban model or plan to highlight output values that are key to assessing proposed development scenarios in your project.

New metrics can be added to the metrics dependency graph and are driven by relevant parameters associated with space use types. Once a new metric has been added and connected to another metric in the graph, the connection itself can be edited. A specified space use type parameter name will appear in the metrics tab of each space use type, where unique values can be entered. Alternatively, a constant value can be entered to weight a metric that serves as an input to another, or to perform unit conversion. Initial metrics are area-based and must be connected to ‘Net space area’, but subsequent metrics can build off each other, so your dependency graph can quickly become heavily layered!

Screenshots of an example metrics dependency graph and corresponding space use type metric parameters in ArcGIS Urban.

Example of a metrics dependency graph and corresponding space use type metric parameters in ArcGIS Urban. 

The metrics you configure ultimately serve to aid in assessment and comparison of potential development scenarios. For instance, you can look at the amount of greenspace in a proposed development to determine if you have sufficient green space per capita. The incorporation of greenspace into residential development is a requirement in most municipalities, due to its important contributions to health and well-being, recreational amenities, visual aesthetics, and microclimate regulation. As an example, compare two alternative development scenarios with differing lot coverage values to assess their performance in meeting minimum greenspace requirements. Scenario 1 shows a design for mixed use development with relatively low lot coverage, while Scenario 2 represents an alternative design that approaches the maximum lot coverage permitted within zoning regulations. To compensate for reduced ground space, Scenario 2 introduces green roofs as an alternative greenspace source, as some cities such as Toronto allow for green roofs to contribute to a portion of the required outdoor amenity space.

An overhead view of Scenarios 1 and 2 as created in ArcGIS Urban, with individual space use types differentiated by colour.

To assess scenario performance, you need to configure metrics that will highlight the area of greenspace created compared to the area of greenspace required. If you have a known target greenspace per resident value (m2/person), you must first determine the number of residents supported by the new development, as well as the area of greenspace created. Number of residents can be calculated using a residential space use parameter for the approximate amount of living space needed per resident (m2/person). To differentiate and track the two types of greenspace created in this scenario, green roofs and ground-level greenspaces are treated as two separate space use types and assigned a constant value of 1 to combine them in a summary metric for total greenspace area.

Finally, you can multiply the residents metric by the target greenspace per resident value to obtain the required greenspace area, which can be combined by assigning a constant value of -1 with total greenspace area to subtract the value and calculate the outstanding greenspace requirement that still needs to be fulfilled. Combining required and outstanding greenspace with constant values will produce a greenspace summary metric.

First portion of the metrics dependency graph created in ArcGIS Urban, depicting the parameters for and relationships between the number of residents, green roof area, ground-level greenspace area, total greenspace, required greenspace, outstanding greenspace, and the greenspace summary.

Now, configure the dashboard with the relevant statistics for the development scenarios, including the estimated number of residents, the area of greenspace generated, the types of greenspace, and the outstanding greenspace required to reach the minimum greenspace per resident target. These data can help inform planning decisions, including whether the proposed development needs to be revised to include more greenspace. However, this can be taken a step further to build on the existing metrics and derive other metrics of interest.

Dashboard configured in ArcGIS Urban to display metric values for number of residents, total greenspace broken down into ground-level greenspace and green roofs, and the greenspace summary, broken down into required and outstanding greenspace.

One factor that may influence decision-making is the volume of water consumption associated with greenspace maintenance, especially while new vegetation is establishing, which can be costly. While you could simply apply a single water usage rate directly to the total greenspace value, you can get more detailed results if you take the extra steps to layer the metrics with additional relevant inputs. For starters, not all designated greenspace will in fact be green – paved walkways, playground foundations, and other amenities constitute non-permeable surfaces which won’t require watering. Thus, you can use a space use type parameter for percent average permeable surfaces (%) to determine the area of permeable ground-level greenspace. Similarly, a metric for non-permeable greenspace area can be configured and combined with permeable area to see an overall breakdown of ground-level greenspace, which is useful in drainage and overland flow considerations. A metric for vegetated green roof area can likewise be created, separately, as green roof surfaces aren’t technically considered to be permeable. You can assign constant values of 1 to combine vegetated green roof with permeable ground-level greenspace surface and obtain the overall area (m2) that needs to be irrigated.

Finally, water consumption rates can be entered as space use type parameters (liters/day/m2) to compute total daily water usage based on the irrigated area. These rates can vary greatly depending on local soil, climate, vegetation type, and watering method, among other factors. In this case, rates were chosen based on the assumptions of installation of drip irrigation systems, and grass and ground cover for ground-level greenspaces and green roofs, respectively.

Second portion of the metrics dependency graph created in ArcGIS Urban, depicting the parameters for and relationships between the green roof, ground-level greenspace, total greenspace, permeable greenspace, non-permeable greenspace, ground-level greenspace summary, irrigated area, and water consumption metrics.

You can now configure a complete dashboard with the metrics of interest to help assess the performance of the proposed development scenarios. Comparing the two, you can see clear trade-offs from altering the lot coverage value and consequently the areas and types of greenspace. A reduced built area leaves more room for greenspace in Scenario 1, resulting in not just satisfaction but exceedance of the target greenspace per resident value. Conversely, the limited lot space available for accessible greenspace in Scenario 2 is supplemented by the addition of green roofs, but the overall greenspace still falls short of target goals. However, Scenario 2 can accommodate 745 more residents than Scenario 1, and the addition of green roofs may be a desirable feature.

Comparison of the output metrics relating to number of residents, greenspace, and water consumption for Scenarios 1 and 2.

The insights gleaned from such metrics are important to scenario assessment and decision-making. If you create very straightforward, clear-cut goals, such as maximizing greenspace or resident capacity, you can confidently select one scenario over the other. More likely, all metrics will need to be considered and weighed against each other before making a final decision. Metrics also provide an opportunity for highlighting deficiencies that could encourage revisions. While Scenario 1 clearly meets the minimum greenspace requirements, you could revise the proposed development to replace some of the excess greenspace with more building coverage to increase the number of residential units and reduce irrigation costs. 

While the current metrics dashboard certainly is informative, you have simply scratched the surface of all the potential metrics you could create for the scenarios:  metrics on water use costs, the number and types of residential units, the number of jobs created from non-residential space use types, and many others could be added as well. When it comes to metrics, you are only limited by the data you have available to you. Whether a student working on a research project, or a land use planner assessing proposed development, ArcGIS Urban’s metrics can help you evaluate scenarios to see the bigger picture, beyond the visual aesthetics.

This post was translated to French and can be viewed here.

About the Author

Kendra Munn is an Urban Solutions Specialist in the Education and Research group at Esri Canada. She contributes to academic research projects in collaboration with universities, and creates educational resources and workshops on ArcGIS software. Working with the Professional Services Urban team, she assists municipal organizations in developing Urban models and data preparation workflows through the deployment of packaged services. Kendra holds a bachelor’s degree in environmental science, applied biology and a master’s degree in geography, geographic information systems from Simon Fraser University.

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