Development of SLEUTH-Density for the simulation of built-up land density

Highlights

• Development of an algorithm and SLEUTH-Density version of SLEUTH for the estimation of spatial built-up land density.

• The model application has been demonstrated for simulating the built-up land density for Ajmer city in India.

• The model may be applied to other geographical locations and helpful in making land development decisions by the developmental agencies.

Abstract

Urban growth is a complex spatio-temporal phenomenon that includes built-up activities taking place both horizontally and vertically. The built-up land density in a city is a function of land desirability and suitability of a location in terms of the quality of the available public services, access to infrastructure, neighborhood type, vibrancy of socio-economic and cultural characteristics. The simulation of built-up land density may help planning and development authorities in making better land developmental decisions, making appropriate provisions for services, long term land use planning, and allocation of natural resources. In the recent past, various efforts have been made to assess built-up density in terms of the densification of built-up activities at the city scale in terms of floor space indices, building density, residential density (number of housing units per hectare) and urban volume. However, only limited research was reported on the simulation and prediction of spatially distributed built-up land density. In the present study, an algorithm is developed to simulate the spatial distribution of built-up land density as a function of a set of selected urbanization explanatory variables. Its application has been demonstrated using a newly developed version of the SLEUTH model (SLEUTH-Density) to simulating the built-up land density for Ajmer city in India. Development of SLEUTH-Density included designing a density algorithm, writing the programming code, integrating the code with the existing SLEUTH model, and testing the algorithm. The model results were validated indirectly using few built-up land density indices and directly through field verification, which were found to be in good agreement with the simulated built-up land density from SLEUTH-Density

Introduction

In many cities worldwide, few localities have been planned and developed efficiently to achieve specific targeted land use and built-up land density distributions. Over time, low-density built-up land areas are converted into higher density, then to very high-density areas because of the increasing demand for land on account of changes in the desirability of localities (Thapa & Murayama, 2011). Public and infrastructure facilities such as water supply, sewerage systems, space for parking and recreational activities are designed to support a particular initial population and built-up land density, but often later become inadequate leading to a variety of problems (Jantz, Goetz, Donato, & Claggett, 2010; Ward, Murray, & Phinn, 2000). Such problems include increases in pollution and traffic congestion, low levels of public services and unanticipated climate-related implications such as urban flooding, Urban Heat Island (UHI) effects and environmental degradation (Xian, Crane, & Su, 2007). Upgrading existing infrastructure facilities may be difficult and may further require funds which may lead to new landscape disturbances, conflicts, and socio-economic problems (Kanta Kumar, Sawant, & Kumar, 2011; Mills & Tan, 1980).

Increase in built-up land density in various parts of cities is a serious issue in developing countries like India, China, Nepal, and Sri Lanka, as they overpower the limited natural resources and public services. Such problems can be avoided by selecting appropriate land development policies after careful study of different scenarios of urban growth and built-up land densities simulated corresponding to various developmental options through simulation models like SLEUTH. Information on simulated built-up land density will give an idea of overall land development with respect to space and time corresponding to a particular land use policy at a particular location. Such information is very helpful in sustainable urban planning and development. Built-up land density is largely proportional to the per capita use of built-up surfaces for different activities. Information on probable built-up land density at a location can help planners and decision-makers for adequate provisioning of public services and utilities. Adequate provisions can be made in master plans for the parking spaces, open spaces, urban utilities like water supply & sewerage systems of adequate capacity, transportation network, stormwater drainage infrastructure with adequate capacity, green spaces, recreational places, etc. corresponding to the probable built-up land densities in future for their design life. Land-use adaptation measures can be planned to avoid or deal with the adverse impacts of climate change like an increase in earth surface temperature or the UHI effects, heatwaves, and urban flooding.

Various types of density measures have been used to represent different types of phenomena like population density, built-up density, and mass density, which are used in different urban contexts (Akın, Clarke, & Berberoglu, 2014; Li, Liu, & Yu, 2014; Lin, Huang, Chen, & Huang, 2014; Salvati, Zitti, & Sateriano, 2013; Ward et al., 2000). Population density may not be proportional to built-up land density, as few modeling efforts reported in the literature (Akın et al., 2014; Liu & Phinn, 2003; Salvati et al., 2013). In slums and low-cost development areas, per person utilization of the built-up area is very much less as compared to well-developed and higher-income localities.

Several approaches and methods have been developed and reported in the recent past which are capable of assessing the built-up density, directly or indirectly. Such methods and approaches can be categorized into indices developed for land-use and urban planning, remote sensing based landscape indices, and built-up land density simulation models. Built-up density is reflected in several quantitative measures reported in the literature, such as the fraction of developed space in a land parcel, number of buildings per unit area, number of floors per unit area, floor area ratio, or compactness (Burton, 2002). The Floor Area Ratio (FAR) and Building Coverage Ratio (BCR) can be extracted from high-resolution satellite images to map built-up intensity (Perini & Magliocco, 2014). Also, location-based feedbacks from the general public has been used as a new addition to the existing layers of information for analyzing urban densification using Public Participation Geographic Information System (PPGIS) based methods (Kyttä, Broberg, Tzoulas, & Snabb, 2013). Residential dwelling density was determined for any given suburb from population and dwelling counts derived from the census of population and housing (Lin, Meyers, & Barnett, 2015). Such density indices are useful from the urban planning perspective as an indication of residential area requirement or present built-up area per person. However, simulation and prediction of spatially explicit built-up land density, i.e., built-up area (horizontal or vertical growth in the built-up area), per unit land area including all type of built-up activities remains at a preliminary stage and there is a need for further research to develop new methods for its correct estimation and simulation (Dovey & Pafka, 2014; Godefroid & Koedam, 2007; Jiao, 2015; Wu, 1998).

Cadastral maps and urban inventories can also provide present-day density estimates, which can be easily extracted from medium and high-resolution remote sensing imagery. The availability of high spatial resolution images has given further impetus for the mapping of urbanization, primarily the two-dimensional extent of built-up activities (Makta, Erbek, & Jürgens, 2005). The built-up area (land coverage) is referred to differently for different applications including built-up density (built-up area fraction of total land area). It has been extracted as a land-use class from remotely sensed data using image pattern recognition techniques, image segmentation/classification, and support vector machine provided an insight into selecting the most effective ones out of many parameters for built-up density assessment (Sudhira, Ramachandra, Raj, & Jagadish, 2003; Xu, 2007, Xu, 2008; Zhang, Huang, Wen, & Li, 2017). Among many built-up indices like the Normalized Difference Built-up Index (NDBI), Enhanced Built-up and Bareness Index (EBBI), Urban Index (UI), Index based Built-up Index (IBI) that are used to assess built-up density, the EBBI was found to be more accurate (As-syakur, Adnyana, Arthana, & Nuarsa, 2012; Franceschetti & Iodice, 2016; Quartulli & Datcu, 2004; Vu, Yamazaki, & Matsuoka, 2009; Yang, Yin, Song, Liu, & Xu, 2014). Morphology-based methods (Chao, Yihua, Huajie, Bo, & Jinwen, 2016; Sohn & Dowman, 2007; Weidner & Förstner, 1995) like S-shaped function which included the parameters explicitly describing various characteristics of urban forms were fitted well. It also assessed built-up density for the tested samples and can be applied for non-monocentric cities and used in their planning (Jiao, 2015). Solar design approaches have also been used to analyze building density along with achieving the optimized shape of the built-up/building that helps in strategic urban planning (Lobaccaro & Frontini, 2014). However, the primary objective of these methods is to extract the built-up area or a signature of the existing built-up activities (horizontal extent) and not their simulation or prediction. Such methods have only limited capability to assess the intensity of built-up activities which include horizontal extent as well as vertical growth of built-up activities or its prediction in advance for planning decisions (Zhang et al., 2017).

Simulation and modeling approaches are extensively used for the simulation of LULC changes, urban growth, and urban densification in terms of population density, and considering a different set of urbanization explanatory variables (Agarwal, 2002; Al-shalabi, Billa, Pradhan, Mansor, & Al-Sharif, 2013; Herold, Menz, & Clarke, 2001). Simulation of built-up densification indirectly in terms of population density has also been reported in a few studies (Bonafoni & Keeratikasikorn, 2018; Kyttä et al., 2013; Loibl & Toetzer, 2003; Mustafa et al., 2018; Ruas et al., 2011). For forecasting rather than measuring built-up activities, GIS-based cellular automata models have been used to simulate urban growth by taking into account important urbanization drivers which includes accessibility, population density, building density, and height (Batty & Xie, 1997; Batty, Xie, & Sun, 1999; Clarke-Lauer & Clarke, 2011; Jantz et al., 2010; Lin et al., 2014; Liu & Feng, 2012; Silva & Clarke, 2002; Yeh & Li, 2002). Spatial agent-based simulation approaches have also been used to simulate the LULC change, urban growth, and built-up densification in terms of population density (Loibl & Toetzer, 2003; Ruas et al., 2011). Measuring built-up densification or density indirectly in terms of population density may not be the correct representation of built-up land density, for example, population density is very high in low-income areas and slums, however, built-up density (built-up area per unit land area) is very high in higher-income localities where population density is quite low. The GIS-based fuzzy Multicriteria Evaluation (MCE) method, in integration with procedural modeling, has also been an effective approach in identifying densification (Koziatek, Dragićević, & Li, 2016). However, it has limitations due to the requirement for high-resolution dataset and is incapable of future projections. Also, multinomial logistic regression analysis, descriptive statistics and Principal Components Analysis (PCA) based methods have been used to analyze the changes in vertical profiles of built-up areas in the recent past (Mustafa, Van Rompaey, Cools, Saadi, & Teller, 2018; Salvati et al., 2013). The iCity 3D modeling method has been developed to simulate the vertical growth of buildings using the geo-simulation method in GIS. However, it was specifically developed for the city of Surrey (Koziatek & Dragićević, 2017). The available set of urban growth and LULC change modeling tools have only limited capability for built-up land density simulation (Lin et al., 2014; Zhang et al., 2017). A lot of research has been done in the areas of LULC change, urban growth monitoring and modeling where forecasting the spatial distribution of land-use change was the goal. Also, many studies have focused on the assessment of built-up land density using traditional methods useful in the assessment of present-day built-up density. However, simulation of spatial built-up land density (usable built-up spaces horizontally and vertically encompassing all built-up activities) is still in its initial stages within LULC change and urban growth modeling domain (Akın et al., 2014; Li et al., 2014; Lin et al., 2014; Liu & Phinn, 2003; Salvati et al., 2013; Ward et al., 2000).

In the present study, an algorithm has been developed for built-up land density estimation as a function of few important urbanization explanatory variables using geospatial techniques and the cellular automata based SLEUTH model. A new version of SLEUTH i.e., SLEUTH-Density has been developed which is capable of simulating both urban growth and built-up land density. The model algorithm was developed, tested and its programming code was integrated with the code of the original SLEUTH model. Various measures (direct or indirect) of built-up land density like Land Surface Temperature (LST), and the Urban Index (UI) (Morabito et al., 2016; Xu, 2008; Zha, Gao, & Ni, 2003) were used to validate the results of the SLEUTH-Density model. Further, the model was applied to demonstrate the model application in simulating the built-up land density of a complex and rapidly changing urban area i.e., Ajmer city in India