Welcome to SLEUTsH: Railway-induced urban growth for SLEUTH’s documentation!

Note

This page is designed only for users who are already familiar with the SLEUTH model and have experienced implementing them smoothly in their system.

Introduction

The SLEUTH model is a cellular-automata model widely used to model and predict changes in land cover and urban cover. The model was developed (Clarke et al., 1997) by Dr. Keith C. Clarke, who continues advancing the model at the University of California, Santa Barbara, Department of Geography. Dr. Clarke’s group has developed its latest version of SLEUTH that can speed up the calibration phase through a widely known machine-learning algorithm called genetic algorithm (GA).

SLEUTsH (Varquez et al., 2020; Varquez et al., 2023) is a version of the original SLEUTH model, expanded to contain railway-induced urban growth. This version is represented by a patch file of the original source code to include a growth rule, which considers urban growth at surrounding railway stations that are represented as one of the standard inputs (.gif format) of the model.

This page is designed for users to follow the work and motivate advancement of railway transport representation in urban growth models, not limited to the SLEUTH model.

Disclaimer

The real world is a complex system. There is no guarantee that the predictions will reflect the actual future. Hence, users are advised to conduct sensitivity experiments and implement other urban growth models to increase plausibility of the forecasts. With this, the developers are not held responsible for any decisions based on the model

Installation

This installation works under the assumption that the user is familiar with the installation and implementation of the SLEUTH model. For basic tutorials and installation, refer to the Project Gigalopolis website. The procedure for installing the SLEUTsH is as follows.

Step 1: Downloading the original SLEUTH source code

The source codes for the SLEUTH models are available here. There are currently two versions listed that vary mainly on how model calibration is done.

The choice of the model will depend on the user. The SLEUTH GA will implement the calibration faster than the second one, which uses brute force calibrations using Monte Carlo simulation.

Step 2: Downloading the patch files to represent SLEUTsH

The source codes of the original SLEUTH have to be updated in order to reflect the growth rules of railway-induced urban growth. The patches to reflect this can be downloaded through the links below.

  • For SLEUTHGA, download the files through the repository here.

  • For the SLEUTH, download the files through the repository here.

Step 3: Replacing the original source codes with the patch

  1. Create a folder where the SLEUTsH is to be installed.

  2. Copy the contents of the original SLEUTH folder (either SLEUTHGA or SLEUTH) into the SLEUTsH folder.

  3. Move the patch files into the SLEUTH folder.

While the patch files are intended to introduce railway-induced urban growth, additional patches are added for efficiency, such as:

  • Increasing the allowed number of input files

  • Removing the years in the predicted images (useful when post-processing the outputs with GIS tools)

  • Increasing the character lengths of files

Step 4: Installing the SLEUTsH model

Inside the SLEUTsH folder, the common procedure used to install all SLEUTH models must be followed. For details, visit the Project Gigalopolis site.

Implementation

Note

Instructions are only for the additional steps and inputs needed to run the SLEUTsH model. The usual process of running the SLEUTH models can be conducted.

Implementation of SLEUTsH is the same as the default SLEUTH models except in the inclusion of an input file and a necessary modification in the scenario file.

Additional input (stations)

To run the SLEUTsH model, at leaast one gif file is needed to represent the locations of the station. The year it represent must at least match the latest year of the other inputs. This file is represented by an array of 1’s and 0’s, such that locations where stations are absent are assigned a value of 0. Furthermore, SLEUTsH represents stations with buffers (or a certain circular area centered at the station). A buffer radius of around 600-m should be set.

The gif must have the format <domain>.station.<year>.gif. Examples are as follows where QC refers to the domain to stand for Quezon City, Manila:

QC.station.2000.gif
QC.station.2005.gif
QC.station.2010.gif

At the current SLEUTsH version, the year must match at least the latest available year of the land cover or urban cover input.

A sample file can be downloaded here. A few visualization softwares may not be able to visualize the file properly because of the values set in the gif. Try visualizing with QGIS and assigning colors to the 1 and 0 values.

Modifications in the scenario file

In order for SLEUTsH to recognize the input station files, slight modifications in the scenario file are necessary. For example, to consider the gifs above as inputs, the following has to be included (e.g. below the URBAN_DATA files) in the scenario files.

...
URBAN_DATA= QC.urban.2010.gif

STATION_DATA= QC.station.2000.gif
STATION_DATA= QC.station.2005.gif
STATION_DATA= QC.station.2010.gif
...

Running the model

Once the above is set, you may run the model as in the default method. If an error is encountered, there is a possibility that it has something to do with the system settings. Hence, before running the SLEUTsH model, ensure that SLEUTH could run smoothly in your system.

References

SLEUTsH

Varquez, Alvin Christopher G., et al. “Evaluating future railway-induced urban growth of twelve cities using multiple SLEUTH models with open-source geospatial inputs.” Sustainable Cities and Society 91 (2023): 104442.

Varquez, A. C. G., Dong, S., Hanaoka, S., & Kanda, M. (2020). Improvement of an urban growth model for railway-induced urban expansion. Sustainability, 12(17), 6801.

SLEUTH

List of publications of the original SLEUTH can be found here.

Other datasets

Other datasets are also available in https://urbanclimate.tse.ens.titech.ac.jp/.