A SPATIAL DECISION SUPPORT FRAMEWORK FOR PLANNING
Creating Tool-Chains for Organisational Teams
PRADEEP ALVA1, PATRICK JANSSEN2and RUDI STOUFFS3
1,2,3School of Design & Environment, National University of Singapore
Abstract. In practice, most planners do not make significant use
of planning support systems. Although significant research has been
conducted, the focus tends to be on supporting individual tasks, and
the outcomes are often the development of new stand-alone tools that
are difficult to integrate into existing workflows. The knowledge
contribution in this paper focuses on developing a novel spatial decision
support framework focusing on the workflows and tool-chains that span
across different teams within an organisation, with varying skill sets
and objectives. In the proposed framework, the core decision-making
process uses set decision parameters that are combined using a weighted
decision tree. The framework is evaluated by developing and testing
tool-chains for a real-world land suitability case study. The tool-chain
was implemented on top of a GIS platform.
Keywords. GIS SDSS PSS; Planning Automation; Geoprocessing;
Data Analytics; Geoinformatics.
Although many Spatial Decision Support Systems (SDSS) and Planning Support
Systems (PSS) exist, most planners do not make significant use of such systems in
practice (Geertman & Stillwell 2003, 2009; Vonk 2006, Schilder 2016). The aim
of the research is to explore an alternative approach to the effective use of PSS and
SDSS, evaluated through a land suitability case study. The focus is on developing
systems for large organisations with teams of diverse professionals with different
In order to improve the support function of PSS, a better conceptual and
empirical understanding is required of the relation between planning tasks and
PSS. Pelzer et al. (2015) discuss the concept of task-technology fit and PSS. They
believe this concept can lead to a better understanding of the usefulness that is
reachable by different planning support capabilities of a PSS for different tasks.
However, PSS research typically focuses on supporting individual tasks, and the
outcomes are often the development of new stand-alone tools that are difficult to
integrate into existing workflows.
Intelligent & Informed, Proceedings of the 24th International Conference of the Association for
Computer-Aided Architectural Design Research in Asia (CAADRIA) 2019, Volume 2, 11-20. © 2019
and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA),
12 P. ALVA, P. JANSSEN AND R. STOUFFS
In our research, we focus on workflows rather than individual tasks. Rather
than developing stand-alone tools, we aim to support specific workflows by
developing tool-chains on top of existing GIS frameworks. These workflows
and tool-chains will typically span across different teams within the organisation,
with varying skill sets and objectives. For example, a key challenge is a typical
workflow in which planning decisions need to be made based on geoprocessed data
(Zhu and Ferreira 2015). In such a workflow, the task for the GIS team involves
analyzing large amounts of geospatial data, which is typically slow and requires
a high level of technical knowledge. The task for planners focuses on decision
making using that processed data, which must be interactive and requires domain
knowledge. Finally, the results of the decisions need to be communicated by the
This paper discusses how such planning tasks within an organisation can be
linked into seamless workflows, supported by geospatial tool-chains. In order to
demonstrate the proposed framework, a case-study on land suitability assessment
and resource management is presented. The case study is based on workflows
being developed by NUS in partnership with JTC Corporation, the lead agency in
Singapore spearheading the planning, promotion and development of the industrial
The next section proposes geospatial tool-chains, and section 3 presents a case
study focusing on land suitability assessment and resource management. Finally,
section 4 draws conclusions and discusses directions for future research.
2. Geospatial Tool-Chains
In the proposed framework, the core decision-making process uses a set of decision
parameters that are combined into a single metric. The parameters are first
calculated based on the latest geospatial data. Once each parameter is calculated,
overall fitness is then derived by using a weighted decision tree (Klosterman et
al. 2018). These weights may vary depending on any kind of development for an
assessment of land suitability. As a result, the weights must be user-defined.
For example, for supporting decisions related to land suitability, the parameters
may include Land Area, Plot Ratio, Shape and Location. When making queries,
a user can set the desired weightages for each of these parameters. This decision
tree is then used to calculate assessments for all of the available plots (see Figure
1). Each plot is then given a percentage score, 100% being the maximum score for
the specific query.
Such a decision-making process will be embedded within a broader workflow.
The research proposed a workflow consisting of three main stages, with
customized tools targeting different types of users with varying expertise. A
definite workflow is proposed, consisting of three main stages: 1) Geoprocessing
performed by the GIS team, 2) Interactive decision making performed by the
planners, and 3) Communications performed by the managerial team (see Figure
A SPATIAL DECISION SUPPORT FRAMEWORK FOR PLANNING 13
Figure 1. Decision tree.
Figure 2. Tool-chain workflow.
2.1. THREE STAGES
Such planning workflows will typically involve different teams from varying
departments in an organisation. In the proposed framework, a series of tools are
developed for performing specific tasks in the workflow. In addition, an important
issue is also the flow of data between specific tasks performed by different teams.
•Stage 1 - The geoprocessing task is performed by the GIS team, as this stage
deals with compiling the latest data for the decision tree. The decision tree will
need to be defined before this stage can be carried out.
•Stage 2 - The decision making task is performed by planners with specific
domain skills in an organisation. This process may include extensive
customisation and exploration of the decision-tree weights.
•Stage 3 - The communications task is performed by the managerial team,
creating reports to be presented and deployed to a land developer. If the
requirements cannot be met, the process will usually go back to stage 2.
14 P. ALVA, P. JANSSEN AND R. STOUFFS
The geoprocessing task handled by the GIS Team is the slowest task to execute,
but is executed infrequently and does not need to be interactive. This task results in
data sets that are then consumed by the decision making task. This task must be fast
and interactive so that the planners are able to iteratively explore many alternative
parcels. Finally, the communications task will consume the data sets created by
the decision making task, and produce a set of final reports for presentation to the
In this division of labour, the geoprocessing task is only executed when new
input data is added to the GIS database, such as changes to the masterplans
and new land use information. The decision making-task, on the other hand,
can be executed multiple times without having to re-execute the time-consuming
geoprocessing task. If the requirements cannot be met, then the communications
task can loop back to the decision-making task to consider alternative plots. The
formalisation of these three stages makes the overall decision-making logic more
transparent and evidence-based.
3. Land Suitability Case Study
The case study focuses on a land suitability assessment process for different land
use developments using open-data available (see Figure 3). This process involves
land developers looking for real estate developments.
Figure 3. Plots with different land uses and public transit access in Singapore (open data
source: data.gov.sg & mytransport.sg).
These land developers will approach organisations and will submit a request
A SPATIAL DECISION SUPPORT FRAMEWORK FOR PLANNING 15
for available land parcels that meet specified requirements. Through the land
suitability assessment process, the organisation will select appropriate land parcels
to present to the land developers. The land parcels are selected based on
the requirements, taking into account planning regulations and other long term
strategic planning goals. The process iterates back and forth filtering land parcels
for different requirements and constraints in parallel.
A tool-chain has been developed and tested to semi-automate the land
suitability assessment process (see Figure 4). The tool-chain combines various
requirements and constraints into a single integrated query and helps planners to
select land parcels based on geospatial data analysis
Figure 4. The land suitability assessment tool-chain with three stages. .
The Land Suitability tool-chain allows the planners to interactively query
and interrogate an integrated database of land parcel information. The
decision-making tool also provides the ability to customise the land query based
on specific land developer requests.
The tools chains operate on GIS datasets called feature classes. Feature classes
are homogeneous collections of common features, each having the same spatial
representation, such as points, lines, or polygons, and a common set of attribute
columns. Feature classes are stored in geodatabase files (.gdb). Geodatabase files
were used due to their compact and efficient nature.
3.1. STAGE 1: GEOPROCESSING TOOL
The Geoprocessing Tool calculates spatial data required for the analysis performed
with the Decision-Making Tool.
The main input to the Geoprocessing Tool consists of a feature class containing
a set of land parcel polygons, referred to as plots. The results of the Geoprocessing
Tool are added to this feature class as additional attributes. Other feature
classes required by the tool include the masterplan with Gross Plot Ratio (GPR)
information and public transit information (for buses and Mass Rapid Transit
The types of analysis include shape analysis, regional or zone analysis, next
to a specific customer/supplier, and additionally customised location preferences.
16 P. ALVA, P. JANSSEN AND R. STOUFFS
Additional feature classes may also be required depending on the types of analysis
3.2. STAGE 2: DECISION-MAKING TOOL
The Decision-Making Tool allows planners to interactively and iteratively assess
available plots by setting the requirements and defining parameter weightages (see
The planners use the tool to enter the requirements from the land request and
the weightages for the decision tree parameters. The tool then assesses the plot,
and assigns an overall fitness value to each plot, normalised to the range of 0 to
100%. The fitness value is added as an additional attribute to the feature class.
Plots are then ranked, with the plots with higher fitness being more suitable. In
order to visualise this data, a heatmap of the plot fitness values is automatically
generated. The planner can then review the plots with high fitness values and
either select a preferred plot make, or adjust the parameter weightages and run a
Figure 5. User interface of the Decision-Making Tool in ArcGIS.
3.3. STAGE 3: COMMUNICATIONS TOOL
The Communications Tool helps the managerial team to check various additional
requirements and constraints based on the local authorities code of practices for a
The tool checks two key constraints. The first constraint to be checked is the
effective buildable area of the selected plot. The buildable area is calculated taking
into account various no-build zones. These include no-build zones imposed by
adjacent roads, with different road categories having different offsets. The second
A SPATIAL DECISION SUPPORT FRAMEWORK FOR PLANNING 17
constraint to be checked is the permissible building height of the selected plot. The
building height is constrained by limits imposed by various government agencies.
The maximum floor area for the development is first calculated based on the plot
area and Gross Plot Ratio (GPR), which is derived from the Masterplan (2014).
The building height is then calculated by applying a set of predefined rules for the
selected building typology for the development of the buildable area of the plot.
This building height is then checked against the height limits for the plot.
The tool also automatically generates a formatted report (in .docx format) that
summarises all relevant information about the selected plot. The information in
the report includes text and tables describing the key data for the selected plot, as
well as all the regulations and codes of practice that are applicable. The report
also includes a formatted plan diagram of the selected plot, showing graphical
representations of some of the key constraints (see Figure 6). This report can be
used for effective communication to the land developers or to officially register
the chosen plot for tenure.
Figure 6. Result of Communications tool: plot plan showing calculated buildable area.
The tool-chain has been implemented within the ArcGIS platform. This
platform was chosen due to the fact that it is already commonly used within
planning organisations, making tool adaptation and utilization easier. Our initial
attempts to create tool-chains were developed using the ModelBuilder visual
programming environment within ArcGIS. However, the programming paradigm
lacked scalability and as a result, models quickly became very complex and
difficult to understand. In the final version, all tools were implemented as Python
The three tools all include graphical user interfaces (GUIs) to allow users to
enter the parameters, with built-in validation and error checking. The tools can
be easily shared and deployed, becoming an integral part of the everyday working
18 P. ALVA, P. JANSSEN AND R. STOUFFS
environment. Documentation for each tool has also been developed, which can
be accessed in the same way as the documentation for system tools (Zandbergen
2013). All three tools automatically filter out invalid data types and field names
and will flag errors for the user, giving feedback and warnings.
In addition, all the output data of the three tools are saved inside geodatabase
files (.gdb) located in a shared workspace in a network drive. This allows teams
in the organisation to easily work together, linking and sharing data in a seamless
The tool-chain has been tested with a wide variety of queries using open data
collected for plots in Singapore. As an example, a query for an industrial plot
is presented. The query specifies the required plot area, GPR, plot shape, location,
together with a set of decision tree weights. The results of the query are described
below (see Figure 7 & 8).
Figure 7. Plot query table.
The stage-1 Geoprocessing Tool was tested with a feature class containing
111,525 plot polygons. The time taken to execute the Tool was around 30
minutes. Stage-1 found 7484 industrial plots and created 11,555 second-order
plot combinations. This resulted in a total of 19,039 plots available for query.
The stage-2 Decision-Making Tool then filters these plots based on a set of
specified weights and other requirements, resulting in a total of 413 available
plots. The decision maker can then select the most suitable plot. Finally, the
stage-3 Communications Tool can then be used to check additional requirements
and constraints, and to generate a report.
At the end of stage-3, the registration of the selected plot is fed back into the
database. This becomes a part of an organisational process, as the managerial team
informs the GIS Team of these updates, thereby closing the geospatial data loop.
Within the framework, the system, organisation, and its members go hand-in-hand
in maintaining and manipulating geospatial data.
The tool-chain is currently limited to a specific number of input parameters.
If additional parameters need to be added, then these can be added. This would
require both the geospatial data schema and the Python script to be updated.
However, such modifications may be difficult for users who are not familiar with
Python programming and who do not have knowledge of the various tool templates
defined within ArcGIS. Approaches are being investigated that are more flexible
and modular, allowing additional parameters to be defined without requiring
A SPATIAL DECISION SUPPORT FRAMEWORK FOR PLANNING 19
Figure 8. The result of land suitability assessment query using tool-chains.
The paper has presented a spatial decision support framework focusing on the
workflows that span across different teams within the organisation, with varying
skill sets and objectives. A tool-chain was implemented on top of ArcGIS,
supporting decision making for planners. In the proposed framework, the core
decision-making process requires set decision parameters that need to be combined
using a weighted decision tree (Klosterman et al. 2018).
A case-study demonstration is presented on land suitability assessment and
resource management. In the decision-making stage, the planners assign priorities
to different types of spatial analysis and filter land parcels. This tool-chain helps
planners to select plots based on geospatial data analysis. The demonstration
shows how planning tasks within an organisation can be linked to seamless
The broader aim of this research is to develop a practice-orientated approach
to creating Planning Support Systems. A key problem with existing SPSS and
PSS systems is that they are developed in isolation of the workflows within
organisations. This results in a lack of synergy between the tools and the
workflows in practice. This research started by first mapping out end-to-end
workflows that existed within the organisation. Based on these workflows, a set of
modular and flexible tool-chains were then developed on top of the existing GIS
infrastructure already being used within the organisation. This approach ensures
that these tools and system are well aligned with the workflows, result in effective
20 P. ALVA, P. JANSSEN AND R. STOUFFS
usage. Future research will apply the same overall approach to other planning
workflows, including long-term planning projections taking into account various
dynamic urban and city-scale constraints.
This research received funding support from NUS-JTC i3Centre. We would like
to thank the JTC Corporation, under the Deputy Directorship of Aloysius Iwan
Handono, for their support in providing information and technical data to facilitate
the development of the workflows and tool-chains.
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