Online Planning Support Systems Resource

Welcome to the Online Resource of Planning Support Systems (PSS) for Land Use Planning

PSS with additional information

CLUMondo

Developer
Peter Verburg, Sanneke van Asselen, David Eitelberg, VU University Amsterdam, the Netherlands

Contact person
Peter Verburg, peter.verburg@vu.nl

URL
not applicable

Description
A national to global scale simulation model of land system changes. Instead of representing land cover it is able to simulate conversions of land systems in response to changing demands for agricultural commodities, residential space, but also accounting for demands for ecosystem services. The model is able to simulate both regional and global scale scenarios.

Publication
Asselen S, Verburg PH. 2013. Land cover change or land use intensification: simulating land system change with a global-scale land change model. Global Change Biology 19(12): 3648-3667.

State of the PSS
The PSS is currently supported and intended for academic purposes.

Available assistance
Training material and user manuals are in preparation.

Target user group
Planners

Required skills
Geographic Information Systems: Advanced
Computer programming: No
System modelling: Advanced

Application
Global scale
Laos national scale

Planning task(s) that it targets
Strategic planning
Impact assessment
Global scale assessments/policy

Aspects included in the analysis
Land use
Environment
Ecosystem services

Geographical analysis scale
National
State/territorial

Required data input and format
Detailed data on land use, land management and its location factors.

Maximal size of data file
Not applicable

Output and format
Grid files

Underlying assumptions
See documentation in publication

Methods and techniques used
Uncertainty method: sensitivity analysis

Is the PSS customisable?
No

Requirements
No specific, stand alone software

Compatibility with other software
Not applicable

Accessibility
The supplier has to be contacted.

PSS evaluation undertaken
Validation

Cost
Free

Strengths of the PSS
A flexible land systems approach for scenario analysis.

Weaknesses of the PSS
Data demanding and requires experts to parameterize the model.

CommunityViz

Developer
Placeways, USA

Contact person
Doug Walker, doug@placeways.com

URL
http://placeways.com/communityviz

Description
Analyze. Visualize. Engage. These are fundamental tasks of urban planners as they seek to promote informed, collaborative decision-making about the future of cities and regions, large and small. CommunityViz® software is here to help. Working as a seamless ArcGIS® extension, CommunityViz provides an advanced-yet-accessible framework for planners and citizens to learn and make choices about the future of the places they love. Feature-rich, versatile, well established, widely used, award-winning, and famously people-friendly, CommunityViz is the software no planner should be without.

Publication
Walker, D., and T. L. Daniels. 2011. The planners guide to CommunityViz: The essential tool for a new generation of planning. Chicago: Planners Press, American Planning Association.

Scott N. Lieske and Jeffrey D. Hamerlinck. Integrating Planning Support Systems and Multicriteria Evaluation for Energy Facility Site Suitability Evaluation. URISA Journal Vol. 26 No. 1.

Peter Pelzer & Gustavo Arciniegas & Stan Geertman & Sander Lenferink. Planning Support Systems and Task-Technology Fit: a Comparative Case Study. Applied Spatial Analysis. Springer.

State of the PSS
The PSS is currently supported.

Available assistance
A full spectrum of support including built-in help, online help, online tutorials, videos, samples, etc. is provided. A wide variety of online and in-person training options are available. Commercial quality technical support via email, phone and web is available.

Target user group
Planners

Required skills
Geographic Information Systems: Intermediate
Computer programming: No
System modelling: Basic

Available case studies
See http://placeways.com/communityviz/casestudies.html

Application
It can be applied to any area.

Number of applications
5000

Planning task(s) that it targets
Urban management
Site selection
Strategic planning
Impact assessment
Build-out and capacity analysis
Growth allocation
Public engagement

Aspects included in the analysis
Land use
Transportation
Population
Employment
Environment
Economy

Geographical analysis scale
National
State/territorial
Regional and metropolitan
Local government
Neighbourhood/precinct
Parcel

Required data input and format
Varies by analysis but typically land use and demographic data in any standard GIS format.

Maximal size of data file
No limit

Output and format
Live on-screen visual displays
GIS feature classes
Tabular reports
Optional viewer formats for 3D and online apps

Underlying assumptions
Varies by analysis. Assumptions are clearly identified and the analyses are designed to be as transparent as possible.

Methods and techniques used
Simulation: time series analysis, what-if analysis

Uncertainty method: sensitivity analysis

Commensurate scale generation method: linear scale transformation

Criterion weighting methods: criterion weights aggregation methods, pairwise comparison, ranking, rating, trade-off analysis

Multi-attribute combination method: weighted linear combination

Multi-objective combination method: interactive programming

Is the PSS customisable?
Yes

Requirements
Windows, Esri ArcGIS Desktop, ArcGIS Online optional

Compatibility with other software
Exchanges data in standard formats with other models capable of generating tables, databases, geodatabases, etc. Specialized connections to Excel and Hazus.

Accessibility
It is available after purchasing.

PSS evaluation undertaken
Validation and usability

Cost
USD 501 – 3000

Strengths of the PSS
Widely used, well established, versatile and feature rich.

CorPlan

Developer
Renaissance Planning Group, USA

Contact person
Chris Sinclair, csinclair@citidesthatwork.com

URL
Not applicable

Description
CorPlan is allows users to develop and allocate place types to create future development scenarios from smaller areas to regions. Place types are the basic building block, and each quantifies the amount of building and parking areas by use and the number of people and jobs in those buildings. Place types can reflect any development type. Once composed, place types are allocated to polygons using the ArcGIS select tool. CorPlan maintains a running inventory of the allocated building areas and socioeconomic information.

Target user group
Planners

Required skills
Geographic Information Systems: Intermediate
Computer programming: No
System modelling: Basic

Planning task(s) that it targets
Site selection
Strategic planning

Aspects included in the analysis
Land use
Transportation
Population
Employment
Environment

Geographical analysis scale
Regional and metropolitan
Local government
Neighbourhood/precinct

Required data input and format
A virtual present is created, which includes parcel level land use data, census population, housing and jobs information and environmental layers, such as wetlands.

Output and format
Building and socioeconomic information by polygon and summarized at differing levels, such as traffic analysis zones for transportation modeling.

Is the PSS customisable (through scripting or open API access)?
Yes

Requirements
ESRI ArcGIS.

Accessibility
The supplier has to be contacted.

PSS evaluation undertaken
Validation

Cost
Free

Strengths of the PSS
Effective and efficient methods of developing land use scenarios

Weaknesses of the PSS
Requires ability to conceptualize future plans

Cube Land

Developer
Dr. Francisco Martinez and researchers at the University of Chile

Contact person
Heejoo Ham, generalsupport@citilabs.com

URL
http://citilabs.com/software/products/cube/cube-land

Description
Cube Land forecasts land use and land price by simulating the real estate market under different economic conditions. For a user- defined scenario, Cube Land forecasts the supply and the demand for different types of properties, and estimates the location of households and non-residential activities. Cube Land is an economic land-use forecasting software designed especially for interaction with transportation models and is based upon the MUSSA II framework.

Publication
Martínez, F., & Donoso, P. (2004). MUSSA: a behavioural land use equilibrium model with location externalities, planning regulations and pricing policies. Santiago: University of Chile.

Martínez, Francisco; Donoso, Pedro. (2004). MUSSA: a behavioural land use equilibrium model with location externalities, planning regulations and pricing policies. Santiago: University of Chile.

Sujeet Kumar Modi (2015) Development of a Land-Use and Transport Integration Demo-Model in Cube Land for the Munich Region. Master Thesis in Technische Universität München (Germany)

Guglielmo Barè (2013) UN MODELLO DI USO DEL SUOLO ORIENTATO AI TRASPORTI: UN’APPLICAZIONE AL COMUNE DI MILANO. Master Thesis in Politecnico di Milano (Italy)

State of the PSS
The PSS is currently supported.

Available assistance
User guides
Technical support
Webinars
Standard and bespoke training courses

Target user group
Planners

Required skills
Geographic Information Systems: No
Computer programming: No
System modelling: Intermediate

Available case studies
Phitsanulok, Thailand
Bakersfield, US
Twin Cities, US
Louisville, US
Boston, US
Paris, France (only non-residential model)
Berlin, Germany (Prototype)
City of Panama, Panama (under development)
Munich, Germany (Student dissertation)
Milan, Italy (Student dissertation)

Application
It can be applied to any area.

Number of applications
The PSS is being used around the world for both small and large scale applications

Planning task(s) that it targets
Urban management
Site selection
Strategic planning
Impact assessment

Aspects included in the analysis
Land use
Transportation
Population
Employment
Transportation through the direct and internal links with Cube Voyager

Geographical analysis scale
National
State/territorial
Regional and metropolitan
Local government
Neighbourhood/precinct
Parcel

Required data input and format
Cube Land processes supply, demand, and space in a disaggregated manner, based on the characteristics that describe:
– Activities to be localized
– Real estate supply
– Location of said activities at real estate properties
– Rent values of the resulting land uses

With input and configuration files, you specify the required information and the elements that define the city and market that Cube Land simulates. Specifically, you define and categorize the agents, properties, and zones involved.

Cube Land input files are grouped into three sets:
1- Input files that are used for predicting scenarios: files which generate changes in the city that will lead to changes in real estate market. Files containing total demand, subsidies, supply restrictions, location restrictions, and accessibility are the part of this set.
2- Input files that define market attributes as attributes of the zones, classification of the agents, and classification of the real estate. Cube Land’s demand model allows the inclusion of a large number of variables representing the most relevant attributes of consumers (socioeconomic characteristics), real estate (property types), and the locations (neighbourhoods).
3- Input files that define the Cube Land models: The files of demand model, supply model, rent model, cost adjustment, rent adjustment, and bid adjustment are part of this set. The files belonging to this subset contains all the calibration parameters that are obtained during calibration.

Inputs format are TEXT Files and DBF (Cube binary MAT format can also be used).

Maximal size of data file
No limit

Output and format
The outputs of the model are bids, real estate values, agent’s location, rent values and housing supply:
– zonal endogenous attributes at equilibrium (location externalities)
– how many agents are located at equilibrium by agent category, real estate type and zone
– rents for each type of property located in each zone
– bid for each consumer (households and firms) for each type of real estate and zone
– occupied supply of real estate, reached at equilibrium

The outputs are DBF files that can be easily post-processed within the Cube environment to obtain further information in DBF or text format.

Underlying assumptions
Main hypothesis are the ones behind the bid-rent theory and the market equilibrium process:
– The model used by Cube Land is based on microeconomic consumer theory, which assumes that the system’s agents, whether they are households, firms, or real estate, are rational beings that maximize their benefit.
– The auction theory is based on the assumption that each property is allocated to the highest bidding consumer. This buying and selling mechanism is justified in urban economics because location is quasi-unique; that is, suppliers cannot produce more of a location in identical quality to satisfy an increase in demand (as is the case with other goods). Auction theory guarantees that maximum utility is reached when the consumer is the highest bidder.
– The supply model is based on maximizing profits. The process consists of supply agents deciding on the amount of each type of real estate to offer in each area so as to optimize their own profits.
– The rent model links the supply models and demand models, and assumes that rents are endogenous variables in the Cube Land model, and they are derived from the real estate auction process, representing the highest bid for each property, so that consumer agents are located in the property where their willingness to pay exceeds that of all those bidding on said property.

Methods and techniques used
Simulation: agent-based modelling, time series analysis, what-if analysis
Uncertainty method: sensitivity analysis

Is the PSS customisable (through scripting or open API access)?
The process is not directly customisable but there is high flexibility in terms of configuration of the input data set, parameters, output post processing and interaction with transportation models.

Requirements
To run Cube Land, the Cube Base software is required to access the interface. Cube Base comes with a complete transportation GIS built on ESRI’s leading GIS technology.

Operating System: Windows 7 SP1 x64 Professional, Enterprise, or Ultimate; Windows 8 x64 Pro or Enterprise

Compatibility with other software
Cube Land is fully compatible with other Cube products, and particularly Cube Voyager for Land-Use-Transport-Interaction models. In addition, Cube allows the user to run external programs from within the Cube interface and can therefore link text files directly between external programs and Cube Land.

Generally, Inputs/Outputs from other software can be easily processed in Cube to obtain the Cube Land required formats, allowing high compatibility.

Accessibility
It is available online after purchasing it and contacting the supplier.
A small demo version is available to download with 30-day trial licence.

PSS evaluation undertaken
Validation and usability

Cost
> USD 3000

Strengths of the PSS
– Commercial support from Citilabs, a software firm with expertise in travel modelling, land use, and GIS
– Strong econometric bid-rent formulation simulates the auction of properties to the highest-valued use
– Flexible data requirements allow users to design and specify the model best suited to local conditions
– Scalable geography and market segmentation can be matched to the resolution of an existing travel model
– Transparent estimation process results in robust parameter sensitivities that remain intact after calibration
– Automated calibration process is virtually guaranteed to fit base year targets with relatively little effort
– Proven equilibrium solution readily lends itself to making valid comparisons between scenario alternatives
– Integrated ArcGIS and reporting results in attractive maps, charts, and other output data visualization graphics
– Open scripting platform makes it easy to integrate Cube Land with other software and third-party tools
– Easy to integrate with existing models/software

Weaknesses of the PSS
The complexity level of Cube Land is high because Cube Land is based on a solid microeconomic theory and includes complex components like supply and location externalities and handles multiple types of constraints: system constraints (for example spatial regulations, land capacities) and individual constraints (budget constraints on consumers).

DELTA

Developer
David Simmonds Consultancy (DSC), UK

Contact person
David Simmonds, david.simmonds@davidsimmonds.com

URL
http://www.davidsimmonds.com/index.php?section=33

Description
The DELTA package has been developed by DSC since 1995. It allows a range of models to be implemented for a city or region. The models can focus on change within one city, across a region or group of regions, or on a combination of both levels.

DELTA itself is a land-use/economic model, designed to interact with any appropriate transport model in order to create a full model of interactions between land-use, economy and transport (usually known as a land-use/transport interaction or LUTI model). Because land uses and economic activities take time to change, these interactions are modelled over time. DELTA provides land-use or economic inputs to the transport model, which generate demands for transport. The transport model (which may be very elaborate or very simple) provides inputs on travel and transport to DELTA, which influence subsequent changes in the location of households, production and jobs.

DELTA represents a number of distinct processes of urban and regional change, such as household change, migration, business location, etc. Each process is generally the subject of research in economics, urban geography, demography etc . Different processes are modelled at urban and regional levels, reflecting (for example) the differences between the variables affecting the choice of which city region to locate in, and the choice of where within that city region to locate.

Publication
See references and links at http://www.davidsimmonds.com/index.php?section=4

State of the PSS
The PSS is currently supported.

Available assistance
Projects generally require at least some consultancy input from DSC to advise on the model implementation and calibration.

Target user group
Planners

Required skills
Geographic Information Systems: Basic
Computer programming: No
System modelling: No

Available case studies
See references and links at http://www.davidsimmonds.com/index.php?section=4

Application
Any city or region. The modelled area needs to be defined with care, and should generally be larger than the area in which policies are to be tested.

Number of applications
24

Planning task(s) that it targets
Urban management
Strategic planning
Impact assessment
Investigating impacts of transport investment and other policies

Aspects included in the analysis
Land use
Transportation
Population
Employment
Environment
Economy

Geographical analysis scale
National
State/territorial
Regional and metropolitan
Local government
Neighbourhood/precinct

Required data input and format
An initial database of households and population, employment, the floorspace they occupy and the rents they pay needs to be compiled, together with matrices of travel costs and times (usually but not always obtained from a transport model). The data needs to be prepared as ASCII files in DELTA-specific formats.

Maximal size of data file
DELTA has been applied to models of up to 1300 zones

Output and format
The main outputs are tables of CSV files which are input to spreadsheets or mapping/GIS software for analysis and interpretation.

Underlying assumptions
The general assumptions are that urban systems need to be analysed as dynamic systems of distinct but interacting processes, the processes representing different kinds of choices made by residents, by firms and by developers – all of them influenced by government interventions. The details within this approach depend on the particular model implemented within DELTA.

Methods and techniques used
Simulation: what-if analysis

Multi-attribute combination method: value/utility function method

Multi-objective combination method: value/utility function method

Is the PSS customisable?
Yes, via definition files that are themselves input to the package

Requirements
DELTA itself runs in DOS under Microsoft Windows (XP or later)

Compatibility with other software
The CSV output files can be readily used in a wide range of other software.

Accessibility
It is available after purchasing

Cost
Contact developer

Strengths of the PSS
The DELTA package provides the platform for a range of sophisticated models. The strengths of any particular application of DELTA depend very largely on the effort and skill applied to implementing, calibrating and testing it.

DSCMOD

Developer
David Simmonds Consultancy (DSC), UK

Contact person
David Simmonds, david.simmonds@davidsimmonds.com

Description
DSCMOD was a relatively simple design of land-use model created in 1990-91 and used mainly to test the impact of major transport changes.

State of the PSS
The PSS is no longer supported, but the design can if necessary be implemented in the DELTA package, described elsewhere in this database.

Envision Tomorrow

Developer
Fregonese Associates Inc., USA

Contact person
Alex Steinberger, asteinberger@frego.com

URL
www.envisiontomorrow.org

Description
Envision Tomorrow is an open source, scenario planning platform that enables cities to better understand interactions between land use, transportation, housing, energy and water use and public health, to name just a few of the evaluation measures.

State of the PSS
The PSS is currently supported.

Available assistance
see www.envisiontomorrow.org

Target user group
Planners

Required skills
Geographic Information Systems: Intermediate
Computer programming: No
System modelling: No
Excel: Basic

Available case studies
Large-scale regional planning: Salt Lake City UT, Austin TX, Denver CO, Kansas City KS, and across Southern California.
Small-scale downtown and district-level plans: South Shore in Austin TX, the Brady District in Tulsa OK, Ogden UT, Bend OR, Portland OR.

Application
The PSS can be applied to any area.

Number of applications
over 20

Planning task(s) that it targets
Urban management
Site selection
Strategic planning
Impact assessment

Aspects included in the analysis
Land use
Transportation
Population
Employment
Environment
Economy

Geographical analysis scale
Regional and metropolitan
Local government
Neighbourhood/precinct
Parcel

Required data input and format
Envision Tomorrow relies heavily on GIS data, and specifically County Assessor data to understand what areas are built, vacant or constrained in some way. On the built parcels, we need to know what type of development is there, the amount of it and (ideally) the value of the development for redevelopment analysis.

Maximal size of data file
Limited by ESRI’s Geodatabase limits, not necessarily by Envision Tomorrow.

Output and format
ESRI Geodatabase and Excel

Underlying assumptions
Countless, modifiable assumptions exist in any PSS tool. Envision Tomorrow does not bury them in code, but rather allows user to adjust them to calibrate to their unique market or geography within GIS or the Excel sheets associated with Envision Tomorrow.

Methods and techniques used
Simulation: time series analysis, what-if analysis

Uncertainty method: sensitivity analysis

Criterion weighting method: ranking, trade-off analysis

Requirements
ESRI ArcGIS, Microsoft Excel

Compatibility with other software
No

Accessibility
It is available online

PSS evaluation undertaken
Validation and usability

Cost
Free

Strengths of the PSS
Free and open access. Assumptions are visible and changeable.

Weaknesses of the PSS
Not built on a relational database so models interact between ArcGIS Geodatabases and Excel, which can slow down when doing large-scale processes on very large datasets (over 1 million features).

Flowmap

Developer
Dept Human Geography and Planning, Faculty Geosciences, Untracht University, the Netherlands

Contact person
Dr. Tom de Jong, tdj@geo.uu.nl

URL
http://flowmap.geo.uu.nl

Description
See website

Publication
See website

State of the PSS
The PSS is currently supported.

Available assistance
Manual available on the website.

Target user group
Wider public

Required skills
Geographic Information Systems: No
Computer programming: No
System modelling: No
Common sense at academic level.

Available case studies
See website

Application
It can be applied to any area.

Planning task(s) that it targets
Site selection
Strategic planning
Impact assessment

Aspects included in the analysis
Land use
Transportation
Population
Employment
Economy

Geographical analysis scale
National
State/territorial
Regional and metropolitan

Required data input and format
Shapefiles of activity locations and transport networks.

Maximal size of data file
Up to 2Gb

Output and format
Attribute data in DBF format, vectordata in BNA or MapInfo Export Format (MifMid).

Underlying assumptions
Spatial rationality

Methods and techniques used
Simulation: what-if analysis

Uncertainty method: sensitivity analysis

Optimisation methods: heuristic algorithms, network optimisation

Is the PSS customisable?
Yes, professsional version only

Requirements
Windows XP, 7, 8

Compatibility with other software
Yes, MapInfo & ArcGIS

Accessibility
It is available online.

Cost
Free

Irregular City (iCity) and Agent iCity

Developer
Spatial Analysis and Modeling (SAM) Research Laboratory, Simon Fraser University

Contact person
Dr. Suzana Dragicevic, suzanad@sfu.ca

URL
http://www.sfu.ca/dragicevic/iCity/

Description
The novel irregular city iCity series of models are geosimulation approaches and tools developed to represent urban growth processes occurring at a fine cadastral scale using complex systems theory and geographic information systems (GIS). The Agent iCity model with interacting agent components that mimics some human drivers of urban development and having the capability to automatically subdivide land parcels to cadastral lots and roads. The iCity models were developed to potentially assist urban planners, land-use managers and policy-makers to generate urban growth outcomes and ‘what-if’ scenarios that can facilitate planning needs.

Publication
Stevens, D., Dragicevic, S. and Rothley, K. (2007). iCity: A GIS-CA modelling tool for urban planning and decision making. Environmental Modelling & Software, 22(6):761-773.

Stevens, D., Dragicevic, S. (2007). A GIS-based irregular cellular automata model of land-use change. Environment and Planning B, 34(4):708–724.

Jjumba, A. and Dragicevic, S. (2012). High resolution urban land-use change modeling: Agent iCity Approach. Applied Spatial Analysis and Policy, 5(4):291-315.

State of the PSS
The PSS is intended for academic purposes.

Available assistance
None

Target user group
Planners
Geosimulation modelers

Required skills
Geographic Information Systems: Advanced
Computer programming: Intermediate
System modelling: Advanced

Available case studies
Please see publications

Planning task(s) that it targets
Site selection
Strategic planning

Aspects included in the analysis
Land use
Transportation
Population
Environment

Geographical analysis scale
Neighbourhood/precinct
Parcel

Required data input and format
GIS data files

Output and format
Simulation output maps

Methods and techniques used
Simulation: agent-based modelling, cellular automata

Requirements
ESRI ArcGIS

PSS evaluation undertaken
Validation and usability

Cost
Not priced

Land Change Modeler

Developer
Clark Labs, Clark University, USA

Contact person
Stefano Crema, screma@clarku.edu

URL
http://www.clarklabs.org/

Description
Land Change Modeler is part of a constellation of tools that are part of TerrSet software. Fully integrated into the TerrSet system, Land Change Modeler is an innovative land planning and decision support software tool. With an automated, user-friendly workflow, Land Change Modeler simplifies the complexities of change analysis. Land Change Modeler allows you to rapidly analyze land cover change, empirically model relationships to explanatory variables, and simulate future land change scenarios. Land Change Modeler also includes special tools for the assessment of REDD (Reducing Emissions from Deforestation and forest Degradation) climate change mitigation strategies. Land Change Modeler provides a start-to-finish solution for your land change analysis needs.

Publication
Aguejdad, Rahim and Thomas Houet. “Modeling the Urban Sprawl Using Land Change Modeler on a French Metropolitan Area (Rennes): Forsee the Unpredictable”. Symposium “Spatial Landscape Modelling; From Dynamic Approaches to Functional Evaluations”. Toulouse, France. June 3-5 2008.

Sangermano, F., J.R. Eastman, and H. Zhu. “Similarity weighted instance based learning for the generation of transition potentials in land change modeling.” Transactions in GIS 14, 5 (2010): 569-580.

Wakode, Hemant Balwant, Klaus Baier, Ramakar Jha, and Raffig Azzam. “Analysis of Urban Growth Using Landsat TM/ETM Data and GIS—a Case Study of Hyderabad, India.” Arabian Journal of Geosciences (January 2013).

State of the PSS
The PSS is currently supported.

Target user group
Planners
Wider public

Required skills
Geographic Information Systems: Intermediate
Computer programming: No
System modelling: Intermediate

Available case studies
See list of publications.

Application
Broad international user community: USA, Central America, South America, Africa, Asia and Australia.

Planning task(s) that it targets
Urban management
Site selection
Strategic planning
Impact assessment
Reducing emissions from deforestation and forest degradation (REDD)

Aspects included in the analysis
Land use
Transportation
Population
Environment
Economy

Geographical analysis scale
National
State/territorial
Regional and metropolitan
Local government
Neighbourhood/precinct
Parcel

Required data input and format
The inputs are in IDRISI raster format (.rst) but the TerrSet software has a suite of import routines that covers most data formats.

Maximal size of data file
Unlimited depending on computer resources.

Output and format
The TerrSet software has a suite of export routines that covers most data formats.

Underlying assumptions
The Land Change modeler is an empirical model where it it takes the historical information and project it to the future.

Is the PSS customisable (through scripting or open API access)?
No

Requirements
Land Change Modeler in TerrSet:
TerrSet is an object-oriented system designed for professional-level use on platforms employing the Microsoft Windows operating environment.

Windows 7 and above, or Windows Server 2003 and above
Microsoft ACE 2010 or Microsoft Office 2010 or later
1.3 GB hard drive space for application
7.4 GB for Tutorial data
4 GB RAM, 8 GB or more recommended
HD display (1920×1080) or greater recommended

Land Change Modeler for ArcGIS:
The Land Change Modeler software is intended for professional-level planning on platforms employing the Microsoft® Windows operating system and the ESRI® ArcGIS® software. Any Windows system that supports ArcGIS 10.2 or later can run Land Change Modeler, although Windows 7 or above recommended. 500 MB of hard disk space is required.

Compatibility with other software
There is an extension for ArcGIS 10.2 or later.

Accessibility
It is available online after purchase and after contacting the supplier.
Trial versions are available.

PSS evaluation undertaken
Validation and usability.

Cost
USD 501 – 3000

Strengths of the PSS
Land Change Analysis:
Quickly generate graphs and maps of land change, including gains and losses, net change, and persistence of specific transitions.
Uncover underlying trends of complex land change with a change abstraction tool.

Land Transition Potential Modeling:
Model land cover transition potentials that express the likelihood that land will transition in the future using one of three methodologies-a multi-layer perceptron neural network with full reporting on the explanatory power of driver variables, logistic regression, and SimWeight, a modified machine-learning procedure.
Incorporate dynamic variables that drive or explain change.

Change Prediction:
Incorporate planning interventions, incentives and constraints, such as reserve areas and infrastructural changes that may alter the course of development when modeling future scenarios.
Conduct scenario mapping by creating either a hard prediction map based on a multi-objective land competition model with a single realization or a soft prediction map that is a continuous map of vulnerability to change.
Validate the quality of the predicted land cover map in relation to a map of reality through a 3-way crosstabulation. Hits, misses and false alarms are reported.

REDD Analysis:
Evaluate REDD related forest conservation strategies and carbon impact scenarios with full GHG emission impact accounting.
Assess additionality of REDD projects and business-as-usual projection scenarios.

Land Use-based Integrated Sustainability Assessment modelling platform (LUISA)

Developer
European Commission – DG Joint Research Centre (European Union)

Contact person
Carlo Lavalle, Carlo.lavalle@jrc.ec.europa.eu

URL
https://ec.europa.eu/jrc/en/luisa
http://sa.jrc.ec.europa.eu/?page_id=763

Description
The ‘Land-Use-based Integrated Sustainability Assessment’ modelling platform (LUISA) is primarily used for the ex-ante evaluation of EC policies that have a direct or indirect territorial impact. It is based on the concept of ‘land function’ for cross-sector integration and for the representation of complex system dynamics. Beyond a traditional land use model, LUISA adopts a new approach towards activity-based modelling based upon the endogenous dynamic allocation of population, services and activities.

Publication
https://ec.europa.eu/jrc/en/publications-list/?f[0]=im_field_identities%3A570

State of the PSS
The PSS is currently supported.

Target user group
Policy makers

Required skills
Geographic Information Systems: Advanced
Computer programming: Advanced
System modelling: Advanced

Available case studies
LUISA is applied to the 28 Member States of the European Union.

Application
It can be applied to any area, provided the necessary data is available.

Planning task(s) that it targets
Urban management
Site selection
Strategic planning
Impact assessment

Aspects included in the analysis
Land use
Transportation
Population
Employment
Environment
Economy
Ecosystem services
Energy

Geographical analysis scale
National
State/Territorial
Regional and metropolitan

Required data input and format
LUISA includes a set of procedures that capture top-down or macro drivers of land-use change (taken from a set of upstream models) and transform them into actual regional quantities of the modelled land-use types. Regional land demands for agricultural commodities are taken from the CAPRI (Common Agricultural Policy Regionalised Impact) model (Britz and Witzke, 2008), which simulates market dynamics using nonlinear regional programming techniques to forecast the consequences of the Common Agricultural Policy. Demographic projections from Eurostat and tourism projections from the United Nations World Tourism Organization (UNWTO) are used to derive future demand for urban areas in each region; land demand for industrial and commercial areas are driven primarily by the economic growth as projected by the Directorate-General for Economic and Financial Affairs of the European Commission (DG ECFIN); and the demand for forest is determined by extrapolating observed trends of afforestation and deforestation rates reported under the scheme of the United Nations Framework Convention on Climate Change (UNFCCC). The demand for the different land-use types is ultimately expressed in terms of acreage and defined yearly and regionally (NUTS2).
GIS format

Maximal size of data file
No limit

Output and format
The final output of LUISA is in the form of a set of spatially explicit indicators that can be grouped according to specific themes (bio-physical, ecological, economic, and social) which as referred to as ‘land function’. The indicators are projected in time until typically year 2030 or 2050, and can be represented at various levels (national, regional or other).
GIS format or tabular

Underlying assumptions
Depends on the policy case.
In the Reference scenario 2014, the economic and demographic assumptions are consistent with the 2012 Ageing Report (EC, 2012). The demographic projections, hereinafter referred as EUROPOP2010, were produced by Eurostat, whereas the long-term economic outlook was undertaken by DG ECFIN and the Economic Policy Committee. The actual economic figures used in LUISA were taken from the GEM-E3 model, which modelled the sector composition of future economy (GVA per sector) consistently with the DG ECFIN’s projections (EC, 2014). Both projections are mutually consistent in terms of scenario assumptions.

Methods and techniques used
Simulation: cellular automata, what-if analysis

Uncertainty methods: sensitivity analsysis

Criterion weighting method: criterion weights aggregation methods, ranking, rating, trade-off analysis

Multi-attribute combination method: value/utility function method, weighted linear combination

Optimisation method: value/utility function method

Is the PSS customisable (through scripting or open API access)?
Not applicable

Requirements
Windows, GeoDMS

Accessibility
Not applicable

PSS evaluation undertaken
Validation

Strengths of the PSS
EU policy coverage

Weaknesses of the PSS
Assumptions and exogenous dependencies

Luci2 Urban Simulation Model

Developer
John R. Ottensmann, United States

Contact person
John Ottensmann, john.ottensmann@gmail.com

Description
The luci2 model provides non-specialist users the ability to create and compare scenarios reflecting the effects of alternative assumptions and policy choices on urban development. The model simulates new urban development in grid cells as a function of accessibility to employment, availability of infrastructure, and other factors.

Publication
John R. Ottensmann. LUCI: Land Use in Central Indiana Model and the relationships of public infrastructure to urban development. Public Works, Management & Policy 8, 1 (July 2003): 62-76.

John R. Ottensmann. luci2 urban simulation model for generating alternative scenarios. Urban Design and Planning 161, 3 (September 2008): 131-140.

John R. Ottensmann. Accessibility in the luci2 Urban Simulation Model and the importance of accessibility for urban development. In Access to Destinations: Rethinking the Transportation Future, David M Levinson and Kevin J. Krizek, eds. Amsterdam: Elsevier, 2005, pp. 297-324.

John R. Ottensmann, Laurence Brown, Jon Fricker, and Li Jin. Incorporating a land consumption model with a statewide travel model. Proceedings of the 12th TRB National Transportation Planning Applications Conference. Transportation Research Board, Washington, DC, 2009, at http://www.trb-appcon.org/.

John R. Ottensmann and Don Reitz. luci2, scenarios, and the Hendricks County, Indiana, USA, comprehensive plan. In Future Cities and Regions: Simulation, Scenario and Visioning, Governance and Scales, Liliana Bazzanella, Luca Caneparo, Franco Corsico, and Giuseppe Roccasalva, eds. New York: Springer, 2012, pp. 125-146.

John R. Ottensmann and Jamie Palmer. New Model Predicts Growth Patterns in Central Indiana. Indianapolis: Center for Urban Policy and the Environment, 2003.

John R. Ottensmann and Jamie Palmer. LUCI Model Aids Planning for Transportation and Other Infrastructure. Indianapolis: Center for Urban Policy and the Environment, 2004.

Available assistance
Model includes comprehensive help system

Complete documentation

Contact John Ottensmann for further information

Target user group
Planners
Wider public

Required skills
Geographic Information Systems: No
Computer programming: No
System modelling: No

Available case studies
See list of publications

Application
The PSS can be applied to any area.

Number of applications
3 (Central indiana, state of Indiana, Indianapolis metropolitan area)

Planning task(s) that it targets
Strategic planning

Aspects included in the analysis
Land use
Population
Employment

Geographical analysis scale
State/territorial
Regional and metropolitan

Required data input and format
land use at two points in time for model estimation, aggregate amounts for grid cells
population
employment

Output and format
Results displayed on-screen, with two scenarios side-by-side
Results optionally exported to csv file which may be joined to shapefile for grid cells (provided)

Underlying assumptions
New development, coversion of land from nonurban to urban use depends on forecast population growth for entire area

Factors affecting location of new development will continue as in recent past (as estimated from data)

Methods and techniques used
Simulation: what-if analysis

Multi-objective combination method: value/utility function method

Is the PSS customisable (through scripting or open API access)?
No

Requirements
Windows (tested on versions through Windows 7)

Version being developed for Mac OS X

Compatibility with other software
No

Accessibility
The supplier has to be contacted

Cost
Free

Strengths of the PSS
Usable by anyone with no prior experience

Allows direct comparison of scenarios reflecting alternative assumptions and policy choices

Relationships estimated using historical data

Weaknesses of the PSS
Relatively simple model based on simple assumptions

Limited to the simulation of new urban development, conversion of land from nonurban to urban use

Relatively high degree of aggregation (at least compared with some models)

Online What if?

Developer
Dick Klosterman, USA

Contact person
Chris Pettit, c.pettit@unsw.edu.au

URL
http://aurin.org.au/projects/portal-and-infrastructure/what-if/

Description
The online What if? PSS tool has been designed to assist cities and regions across Australia in understanding land use supply, demand and likely future land use change scenarios.

Publication
Pettit, C. J., Klosterman, R. E., Nino-ruiz, M., Widjaja, I., Russo, P., Tomko, M., & Sinnott, R. (2013). The online what if? Planning support system. In S. Geertman, F. Toppen, & J. Stillwell (Eds.), Planning support systems for sustainable urban development, Vol. 195, pp. 349–362. Berlin: Springer.

State of the PSS
The PSS is currently supported.

Available assistance
User manual, training and assistance available on request.

Target user group
Planners
Wider public
Researchers in urban planning

Required skills
Geographic Information Systems: Advanced
Computer programming: No
System modelling: No

Available case studies
On request

Application
It can be applied to any area.

Number of applications
2 (Metropolitan areas of Melbourne and Perth, Australia)

Planning task(s) that it targets
Strategic planning
Population and employment projection
Future residential land demand
Future employment-related land demand

Aspects included in the analysis
Land use
Population
Employment
Planning policies and strategies

Geographical analysis scale
State/territorial
Regional and metropolitan
Local government
Neighbourhood/precinct
Parcel

Required data input and format
Vector base GIS data – Esri Shapefile format (zip compressed)

Maximal size of data file
Tested up to 750,000 polygons

Output and format
Future land use, same format as input data

Underlying assumptions
Assumptions related to factors and weights in land suitability analysis (MCE), population growth trends, land use densities (residential and employment related), employment sectors growth and spatial growth patterns (not compulsory).

Methods and techniques used
Simulation: what-if analysis

Alternatives screening methods: compensatory and non-compensatory screening

Criterion weighting methods: ranking and rating

Multi-attribute combination method: weighted linear combination

Is the PSS customisable?
Not at the moment, however, it has been developed using open source technologies.

Requirements
Internet browser

Compatibility with other software
Not applicable

Accessibility
It is available online and the supplier has to be contacted.

PSS evaluation undertaken
Validation

Cost
Contact developer

Strengths of the PSS
Project can be accessed by the members of a team, anywhere and anytime.

Weakness of the PSS
Relies on heavy data pre-processing.

SLEUTH

Developer
Keith Clarke and colleagues, University of California, Santa Barbara, USA

Contact person
Keith Clarke, kclarke@geog.ucsb.edu

URL
http://www.ncgia.ucsb.edu/projects/gig/

Description
SLEUTH is a cellular automaton-based urban growth and land use change model.

Publication
Chaudhuri, G. and Clarke, K. C. (2013) The SLEUTH Land Use Change Model: A Review. International Journal of Environmental Resources Research, 1, 1, 88-104.

State of the PSS
The PSS is currently supported and intended for academic purposes.

Available assistance
See the website and discussion forum.

Target user group
Planners
Wider public
Academics

Required skills
Geographic Information Systems: Basic
Computer programming: Basic
System modelling: Basic
Unix

Available case studies
See the cited paper, one of three comprehensive reviews.

Application
It can be applied to any area.

Number of applications
There are over 100 documented applications on all continents except Antarctica.

Planning task(s) that it targets
Urban management
Site selection
Strategic planning
Impact assessment

Aspects included in the analysis
Land use
Transportation
Environment
Topography

Geographical analysis scale
National
State/territorial
Regional and metropolitan
Local government
Neighbourhood/precinct
Parcel

Required data input and format
8-bit GIF files for slope, land use, exclusions, urban extent and topography.

Maximal size of data file
No limit. Supports parallel processing with MPI.

Output and format
Statistics, log files, graphic files and animations.

Underlying assumptions
Land use change is impacted by slope, urban extent and roads.

Methods and techniques used
Simulation: cellular automata, time series analysis

Uncertainty methods: monte carlo simulation, sensitivity analysis

Multi-objective combination methods: genetic algorithm, heuristic algorithms

Is the PSS customisable?
Yes

Requirements
Any unix, linux or emultor system

Compatibility with other software
Giles are simple GIF images, transferable to many GIS packages.

Accessibility
It is available online.

PSS evaluation undertaken
Validation and usability

Cost
Free

Strengths of the PSS
Works well, free, adaptable.

Weakness of the PSS
No social or economic inputs.

Spatial and Transport Emissions Assessment Module (STEAM)

Developer
CSIRO, Australia

Contact person
Leorey Marquez, leorey.marquez@csiro.au

Target user group
Planners

Required skills
Geographic Information Systems: No
Computer programming: No
System modelling: Basic

Application
Area-specific, it cannot be applied to any area.

Number of applications
1

Planning task(s) that it targets
Impact assessment

Aspects included in the analysis
Land use
Transportation
Population

Geographical analysis scale
Regional and metropolitan
Local government
Neighbourhood/precinct

Required data input and format
Land use, transport data
Excel files

Maximal size of data file
No limit

Output and format
Excel files

Methods and techniques used
Optimisation methods: heuristic algorithms, linear programming

Is the PSS customisable (through scripting or open API access)?
Yes

Requirements
Excel for Windows

Cost
Not priced

Sustainable Urban Structure Transport and Infrastructure Networks (SUSTAIN)

Developer
CSIRO, Australia

Contact person
Leorey Marquez, leorey.marquez@csiro.au

Target user group
Planners

Required skills
Geographic Information Systems: Basic
Computer programming: Basic
System modelling: Intermediate

Available case studies
None

Application
Area-specific, it cannot be applied to any area.

Number of applications
1

Planning task(s) that it targets
Strategic planning

Aspects included in the analysis
Land use
Transportation
Population

Geographical analysis scale
Regional and metropolitan
Local government
Neighbourhood/precinct

Required data input and format
Land use, transport data
Text files

Maximal size of data file
No limit

Output and format
Text files

Underlying assumption
Geometric simplification of urban form

Methods and techniques used
Multi-attribute combination method: weighted linear combination

Multi-objective combination method: heuristic algorithms

Optimisation methods: linear programming, network optimisation

Is the PSS customisable (through scripting or open API access)?
Yes

Requirements
Windows

Cost
Not priced

Strength of the PSS
Simplified geometry of urban form

Weakness of the PSS
Simplified geometry of urban form

Techniques for Optimal Placement of Activities in Zones (TOPAZ)

Developer
CSIRO, Australia

Contact person
Leorey Marquez, leorey.marquez@csiro.au

Target user group
Planners

Required skills
Geographic Information Systems: Basic
Computer programming: Basic
System modelling: Basic

Application
The PSS can be applied to any area.

Number of applications
5

Planning task(s) that it targets
Urban management
Site selection
Strategic planning
Impact assessment

Geographical analysis scale
Regional and metropolitan
Local government
Neighbourhood/precinct

Required data input and format
Land use, transport data
Text files

Maximal size of data file
No limit

Output and format
Text files

Methods and techniques used
Simulation: what-if analysis

Multi-objective combination method: heuristic algorithms

Optimisation methods: linear programming, network optimisation

Is the PSS customisable (through scripting or open API access)?
Yes

Requirements
Windows

PSS evaluation undertaken
Validation and usability

Cost
Not priced

UPlan

Developer
University of California, Davis, USA

Contact person
Nathaniel Roth, neroth@ucdavis.edu

URL
uplan.readthedocs.org

Description
UPlan is a rule based growth allocation mode. Based on locally defined opportunities and constraints including planning policy, accessibility, environmental conditions, and expert knowledge, new land uses are allocated to the landscape.

Publication
Beardsley, Karen, James H. Thorne, Nathaniel Roth, Shengyi Gao, and Michael C. McCoy. “Assessing the Influences of Rapid Urban Growth and Regional Policies on Biological Resources.” Landscape and Urban Planning 93, no. 3–4 (2009): 172–83. doi:10.1016/j.landurbplan.2009.07.003.

Byrd, Kristin B., Adena R. Rissman, and Adina M. Merenlender. “Impacts of Conservation Easements for Threat Abatement and Fire Management in a Rural Oak Woodland Landscape.” Landscape and Urban Planning 92, no. 2 (2009): 106–16.

Gerrard, R., P. Stine, R. Church, and M. Gilpin. “Habitat Evaluation Using GIS – A Case Study Applied to the San Joaquin Kit Fox.” Landsc. Urban Plan. 52, no. 4 (2001): 239–55.

Huber, Patrick R, James H. Thorne, Nathaniel E. Roth, and Michael M. McCoy. “Assessing Ecological Condition, Vulnerability, and Restorability of a Conservation Network Under Alternative Urban Growth Policies.” Natural Areas Journal 31 (July 2011): 234–45. doi:10.3375/043.031.0306.

Johnston, R. A., M. McCoy, M. Kirn, and M. Fell. “Streamlining the National Environmental Policy Act Process through Cooperative Local-State-Federal Transportation and Land Use Planning.” Transportation Research Record 1880 (March 3, 2004): 135–43.

Johnston, R. A., D. R. Shabazian, and S. Y. Gao. “UPlan – A Versatile Urban Growth Model for Transportation Planning.” Transportation Research Record: Journal of the Transportation Research Board, Transportation Research Record, 1831 (2003): 202–9.

Johnston, Robert A., Nathaniel Roth, and Jackie Bjorkman. “Adapting Travel Models and Urban Models to Forecast Greenhouse Gasses in California.” Transportation Research Record 2133 (2009): 23–32.

Merenlender, Adina M., Colin Brooks, David Shabazian, Shengyi Gao, and Robert Johnston. “Forecasting Exurban Development to Evaluate the Influence of Land-Use Policies on Wildland and Farmland Conservation.” Journal of Conservation Planning 1, no. 1 (2005): 40–57.

Roth, Nathaniel, James Thorne, Robert Johnston, James Quinn, and Michael McCoy. “Modeling Impacts to Agricultural Revenue and Government Service Costs from Urban Growth.” Journal of Agriculture, Food Systems, and Community Development 2, no. 4 (August 29, 2012): 43–62. doi:10.5304/jafscd.2012.024.008.

Thorne, James H., Maria J. Santos, and Jacquelyn H. Bjorkman. “Regional Assessment of Urban Impacts on Landcover and Open Space Finds a Smart Urban Growth Policy Performs Little Better than Business as Usual.” Edited by Matteo Convertino. PLoS ONE 8, no. 6 (June 5, 2013): e65258. doi:10.1371/journal.pone.0065258.

Thorne, J. H., S. Y. Gao, A. D. Hollander, J. A. Kennedy, M. McCoy, R. A. Johnston, and J. F. Quinn. “Modeling Potential Species Richness and Urban Buildout to Identify Mitigation Sites along a California Highway.” Transport. Res. Part D-Transport. Environ. 11, no. 4 (2006): 277–91.

Walker, T., S. Gao, and R. Johnston. “UPlan: Geographic Information System as a Framework for Integrated Land Use Planning Model.” Transportation Research Record: Journal of the Transportation Research Board 1994 (2007): 117–27

State of the PSS
The PSS is currently supported and is intended for academic purposes.

Available assistance
User manual
Email assistance
Training

Target user group
Planners

Required skills
Geographic Information Systems: Intermediate
Computer programming: Basic
System modelling: Basic
Land use planning

Available case studies
San Joaquin Valley Blueprint
Calaveras County
Amador County
Tuolumne County
Shasta County
Lake County
Mendocino County
Santa Barbara County

Application
It can be applied to any area.

Number of applications
approximately 30

Planning task(s) that it targets
Strategic planning

Aspects included in the analysis
Land use
Transportation
Population
Employment
Environment
Economy
Many are handled through exporting results to other tools such as a travel demand model.

Geographical analysis scale
Regional and metropolitan
Local government
Neighbourhood/precinct

Required data input and format
General/comprehensive plans
Road networks
Environmental constraints
Utility service areas
City boundaries
UPlan is open to the inclusion of any spatial data that the user believes influences the locations of growth. All data in the current version is stored in ESRI GRID format.

Maximal size of data file
Limited by ESRI file constraints

Output and format
A projection of new land use growth

Underlying assumptions
That growth is responsive to land use policy and the proximity to attractive or discouraging features of the landscape.

Methods and techniques used
Simulation: what-if analysis
Criterion weighting method: rating
Multi-attribute combination method: weighted linear combination

Is the PSS customisable (through scripting or open API access)?
Yes

Requirements
ESRI ArcGIS. The current version of UPlan is a raster-based model using VBA with the Spatial Analyst Extension. It is currently being rewritten into Python with support for polygon based (commonly parcel) tracking of space conversion.

Compatibility with other software
Export tools are available to standard interchange formats either built into UPlan, or through ArcGIS.

Accessibility
It is available online.

PSS evaluation undertaken
Validation and usability

Cost
Free

Strengths of the PSS
Flexible, can be used in data poor areas, and can grow with organization’s capacity.

Weaknesses of the PSS
Complex economic interactions, cross-boundary influences on growth. redevelopment and infill in compact areas.

Vacancy Chain Models for Housing Needs and Impact Assessments

Developer
Philip Emmi (USA) and Lena Magnusson (Sweden)

Contact person
Philip Emmi, philemmi@mac.com

URL
http://www.researchgate.net/profile/Philip_C_Emmi/publication/232923677_Residential_vacancy_chain_models_of_an_urban_housing_market._Exercises_in_impact_and_needs_assessment/links/548233080cf25dbd59ea948c.pdf

Description
Data on residential mobility is used to calibrate vacancy chain models for (1) urban housing needs assessments and (2) residential development impact assessments. Applications in the USA and Sweden show that the model parameters are stable through relevant forecast periods and produce highly accurate simulations of residential mobility in response to housing demographic and residential inventory changes.

Publication
Emmi, P. C. and L. Magnusson. 1995. Opportunity and mobility in urban housing markets. Progress in Planning, 43(1): 1-88.

Emmi, P. C. 1995. Further evidence on the accuracy of residential vacancy chain models, Urban Studies, 32(8): 1361-1367.

Emmi, P. C. and L. Magnusson. 1994. The accuracy of residential vacancy chain models, Urban Studies, 31(7): 1117-1131.

Emmi, P. C. and L. Magnusson. 1988. Vacancy chain models of an urban housing market: exercises in impact and needs assessment. Scandinavian Planning and Housing Research, 5(3): 129-145.

Emmi, P C. 1990. Testing the assumptions underlying residential vacancy chain models – a comment, Scandinavian Planning and Housing Research, 7: 55-56.

State of the PSS
The PSS is intended for academic purposes.

Available assistance
Email assistance: emmi@utah.edu

Target user group
Planners
Large residential developers

Required skills
Geographic Information Systems: No
Computer programming: No
System modelling: Intermediate

Available case studies
See list of publications, especially Emmi, P. C. and L. Magnusson. 1995. Opportunity and mobility in urban housing markets. Progress in Planning, 43(1): 1-88.

Application
It can be applied to any metropolitan housing market area.

Number of applications
4

Planning task(s) that it targets
Strategic planning
Impact assessment
Metropolitan housing needs assessment

Aspects included in the analysis
Land use
Population

Required data input and format
A matrix of residential mobility by characteristics of origin and destination plus a vector of newly built dwellings net demolitions and conversions or vectors of net household migration and new family formation.

Maximal size of data file
No limit

Output and format
(1) A vector of housing vacancies to be taken up by in-migration, newly formed families, demolitions and conversions. (2) Or a vector of new construction (by generalized housing type/location) required to accommodate expected housing needs.

Underlying assumptions
The model assumes that the metropolitan housing market can be intelligently divided in a small number of sub-markets, that the coefficients for inter-sub-market transfer of residential vacancies remains stable for the forecast period (5-15 years) as tests show they do and that either new construction plus outmigration or in-migration plus family formation can be accurately forecast.

Methods and techniques used
Simulation: what-if analysis

Is the PSS customisable?
Since the model relies on matrix inversion, yes, it can be semi-scripted using a spreadsheet.

Requirements
A spreadsheet with matrix algebraic capacity.

Compatibility with other software
No

Accessibility
The supplier has to be contacted.

PSS evaluation undertaken
Validation and usability.

Cost
Contact developer

Strengths of the PSS
Simplicity, accuracy, manageable data inputs and useful outputs.

Weaknesses of the PSS
Model accuracy declines with an excessive number of sub-market delineations.

Further PSS

ALCES

URL
www.alces.ca

Application Site
Australia, Western Australia, Kimberley Region
Canada, Alberta
India
South America

Amersfoort
Brisbane Urban Growth Model (BUG)
California Urban and Biodiversity Analysis (CURBA)

URL
http://www-dcrp.ced.berkeley.edu

Application Site
USA, California Region

California Urban Futures I/II Model (CUF-1/2)

URL
http://www-dcrp.ced.berkeley.edu

Application Site
USA, California Bay Region

Chicago Area Transportation Land use Analysis System (CATLAS)

Application Site
USA, Chicago

CityEngine

URL
http://www.esri.com/software/cityengine

Application Site
Switzerland, Zurich

Common-pool Resources and Multi-Agent Systems (CORMAS)
Computer-Aided Land-Use Transport Analysis System (CALUTAS)

Application Site
Japan, Tokyo

Constrained Cellular Automata model
Conversion Land Use and its Effects (CLUE/-s)

Application Site
China
Costa Rica
Ecuador
Indonesia, Java
Malaysia
Netherlands

Criem/GIS

Application Site
USA, Chicago

Disaggregated Residential Allocation Model of Household Location and the Employment Allocation Model (DRAM/EMPAL)

URL
http://dolphin.upenn.edu/~yongmin/intro.html

Application Site
USA

DSSM

Application Site
Thailand, Chiang Mai

Dynamic Urban Evolutionary Model (DUEM)

URL
http://www.bartlett.ucl.ac.uk/casa/latest/software/duem-ca

Application Site
USA, Detroit

Dyna-CLUE model
Environment Explorer (LOV)

URL
http://www.lumos.info/environmentexplorer.php

Application Site
Canada
Indonesia
Netherland

Envision

Application Site
Australia, Western Australia, City of Canning
Australia, Victoria, City of Manningham

Envision Scenario Planner (ESP)
EU-ClueScanner (EUCS)

Application Site
Europe

EZ-IMPACT
GeneticLand

Application Site
Portugal

GEOMOD2
GeoPlanner

URL
http://doc.arcgis.com/en/geoplanner/

Growth Simulation Model (GSM)

URL
http://www.mdp.state.md.us

Application Site
USA

Harvard Urban Development Simulation (HUDS)
Hedonic Pricing Model
Housing Development Tool

Application Site
Australia, North-west Melbourne Region

IFS model

Application Site
Global level simulation model

ILUMASS

URL
http://www.transport-research.info/web/projects/project_details.cfm?id=34012

Application Site
Germany, Dortmund

INDEX

URL
http://www.crit.com

Application Site
USA

Integrated Infrastructure Planning Tool (IIPT)
Integrated Land Use, Transportation and Environment (ILUTE) model

URL
http://www.civ.utoronto.ca/sect/traeng/ilute/ilute_the_model.htm

Application Site
Canada, Toronto

Integrated Model Prediction European Landuse (IMPEL)

Application Site
European level simulation model

Integrated Model of Residential and Employment Location (IMREL)
Integrated Transportation and Land Use Package (ITLUP)

URL
http://people.hofstra.edu/geotrans/eng/methods/flowitlup.html

Application Site
USA, Austin

Interactive Multivariable Analysis Tool (IMAT)

Application Site
Australia, Victoria, Brimbank

I-Place3S

URL
http://www.sacog.org/services/scenario-planning/

IRPUD (Dortmund)

URL
http://www.raumplanung.tu-dortmund.de/irpud/pro/mod/mod_e.htm

Application Site
Germany, Dortmund

KIM
LAND
Land Transformation Model (LTM)

URL
http://www.ltm.msu.edu

Application Site
USA

Land Use Change Analysis System (LUCAS)

URL
http://www.cs.utk.edu/~lucas

Application Site
USA

Land Use Change (LUC) model

Application Site
China
Northeast Asia

Landuse Evolution and Impact Assessment Model (LEAM)

URL
http://www.leam.illinois.edu/leam

Application Site
USA, City of St.Louis and Preoria Tri-County

Land Use Scanner

URL
http://www.lumos.info/landusescanner.php

Application Site
Netherlands

Land Use Scenario DevelopeR (LUSDR)

URL
http://www.oregon.gov/ODOT/TD/TP/pages/landuse.aspx

Application Site
Oregon, U.S.

Large Scale Urban Model

Application Site
Australia, Queensland Region

LILT

Application Site
Germany, Dortmund
Japan, Tokyo
UK

Lustre

Application Site
USA, Washington, DC.

Luti

Application Site
UK

MARXAN

URL
www.uq.edu.au/marxan

Application Site
Broad international user community

MENTOR
MEPLAN

URL
www.meap.co.uk

Application Site
Brasil, Sao Paolo
Chile
Colombia
Finland, Helsinki
Italy
Japan
Spain
Sweden
UK
USA, Sacramento and Salt Lake Ciy
Venezuela

Metronamica

URL
www.metronamica.nl

Metropolitan Integrated Land Use System (Metropilus)

Application Site
USA

MetroScope

URL
http://metroscope-psu.wikispaces.com/home

Metrosim

Application Site
USA

MODULUS

Application Site
Canada
Indonesia
Netherlands

MOLAND

Application Site
Urban areas across Europe

Multi-Agent-based Behavioral Economic Landscape (MABEL)
Mussa

Application Site
Chile, Santiago City

Mutopia

URL
www.mutopia.unimelb.edu.au/spatial-platform.html

NBER
NYMTC-LUM

Application Site
USA, New York

Online Envision
Osaka
PLUM
Predicting Urban Population (PUP) model

Application Site
Australia, Adelaide and in the eastern part of the South East Queensland Region

Production, Exchange, and Consumption Allocation System (PECAS)

Application Site
USA, Sacramento

Projective Optimisation Landuse Information System (POLIS)

Application Site
USA, Oakland

PUMA

Application Site
Netherlands, Randstad

Ramblas
Random-Utility Urban (RURBAN)
RapidFire

URL
http://www.calthorpe.com/scenario_modeling_tools

SALOC
SIMLUCIA

Application Site
Canada
Indonesia
Netherlands

Smart Growth INDEX

URL
www.crit.com

Application Site
USA

Smart Places

URL
http://www.epri.com or www.smartplaces.com

Application Site
USA

Spartacus

URL
http://www.fhwa.dot.gov/planning/toolbox/spartacus_overview.htm

Application Site
Finland, Helsinki
Italy, Naples
Spain, Bilbao

Spatial Vision's peri-urban model

URL
http://spatialvision.com.au/html/IA/

Sub-Area Allocation Model-Improved Method (SAM-IM)

URL
http://www.all4gis.com

Application Site
USA

Transportation Economic and Land Use System (TELUS)

URL
http://www.telus-national.org/products/telus.htm

Application Site
USA

Transportation and Land Use System (TRANUS)

URL
http://www.modelistica.com/tranus/

Application Site
Colombia, Bogota
Belgium, Brussel
Spain, Valencia
USA
Venezuela, Caracas and La Victoria and island of Curacao

UGrow

Application Site
USA

UrbanCanvas
UrbanFootprint

URL
http://www.calthorpe.com/scenario_modeling_tools

Application Site
USA, California

Urban Housing Growth Model

URL
www.lesterfranks.com.au/gis.html

Application Site
Australia, Tansania, Hobart

UrbanSim

URL
www.urbansim.org

Application Site
USA