In 2018, the world’s urban population was 55% of the world population, and it has been projected to reach 68% by 2050 [1
]. With this increased urban population, it becomes a global concern to devise effective urban land-use planning that can produce optimal benefits. This problem is more acute in high-density countries such as Bangladesh. For example, in 2021, the urban population of Bangladesh was 38.90% [2
], and it was projected that the urban population will rise to 57% by 2050 [3
]. This higher rate of the urban population causes unplanned urban development in Bangladesh. Current practice in urban land-use planning, especially in developing countries, exhibits inefficient patterns and allocation of land uses, which, in turn, produce a lot of problems in the face of sustainable urban development [4
]. For example, in Bangladesh, it has been acknowledged that problems such as sanitation and drainage, solid waste management, degradation of soil and land, uncontrolled emissions from domestic and industrial activities, traffic jams in the streets, and improper disposal of hazardous waste are consequences of rapid urbanization [7
]. To address the issue of inefficiency, a new concept of sustainable land-use planning has begun to take focus in planning literature and practice. Sustainable land-use planning ensures increased compatibility of adjacent land uses, promotes compactness, boosts up economic development, and results in desirable social and environmental outcomes [9
]. Like other countries, Bangladesh has also started to prepare proper urban development planning (e.g., structure plan, urban area plan, and detailed area plan), but the inclusion of sustainability in urban development planning is still far from the expectation [12
Land-use allocation concerning urban sustainability involves social, economic, and environmental aspects. However, in reality, there exist many conflicting and competing objectives among social, economic, and environmental outcomes. One objective may be optimized at the expense of another objective. For example, if residential development is to take place in a low-lying area, it may fulfill the demand for urban housing, but it will create problems with urban drainage. Construction of building structures may increase economic benefit, but it will deteriorate the environment and urban health. Therefore, careful land allocation is of paramount importance in land-use planning. Sustainable land-use optimization involves optimizing the composition and configuration of land-use types within a geographical area in order to meet the requirements of sustainable development. Typically, this method balances trade-offs between several land-use objectives (e.g., social, economic, and environmental benefits), intending to maximize net benefits across all outcomes. Often, one objective may only be optimized at the expense of other objectives [15
]. However, the main problem is that, in early studies, the inclusion of sustainability criteria is very limited due to the complexity of calculating social, economic, and environmental benefits in land-use allocation. For example, Wang, Zhang, and Wang [16
] optimized land-use allocation in Dawa District, a district of Northeast China, but they did not include social benefits due to the difficulty in the quantification of social benefits.
Rahman and Szabó [17
] conducted an extensive systematic literature review on multiobjective urban land-use optimization using preferred reporting items for systematic reviews and meta-analyses (PRISMA). In their systematic review, they investigated 55 journal papers (screened out from 2291 journal articles) to identify and analyze different aspects of urban land-use optimization problems, where they identified that sustainability criteria were merely touched upon in urban land-use optimization problems. While some studies partially considered sustainability dimensions, the quantifications of sustainability indicators lacked appropriate methods. For example, Song and Chen [18
] optimized land-use allocation using the NSGA-II method considering four objectives, including maximization of agricultural suitability, construction suitability, conservation suitability, and spatial compactness; Zheng et al. [19
] optimized land-use allocation in Wuhan, China, to balance ecosystem services and economic benefits; Cao, Zhang, and Wang [20
] optimized land-use allocation considering land-use compatibility and environmental benefits. A nondominated sorting genetic algorithm (NSGA) was employed by Cao et al. [21
] to solve a multiobjective land-use optimization problem (NSGA-II-MOLU) that aimed to reduce conversion costs and increase accessibility while also ensuring that land-use types were compatible with one another. Using a genetic algorithm, Haque and Asami [22
] optimized urban land-use allocation by considering the maximization of land price and minimization of incompatibility between nearby land-use categories in an area. Neema and Ohgai [23
] used genetic algorithms to find the best locations for urban parks and open spaces based on four objective functions, using the Euclidian distances between the facility and the demand points, in a multiobjective optimization model. If we critically look at the optimization objectives of the above studies, we could see that those studies did not directly include three dimensions of sustainability.
The studies mentioned in the above text also noted that public participation was very negligible to optimize land-use allocation, but only quantitative optimization was insufficient to meet the public agreement. To integrate public opinion decision making, the multicriteria decision-making (MCDM) approach was introduced in many disciplines. MCDM is a collection of techniques that aim to determine a preference order among alternative decision options based on their performance in terms of multiple criteria [24
]. Land-use decision is a spatial allocation problem in which the geographic information system (GIS) plays an important role in the spatial planning and management of land-use planning. MCDM integrated with GIS can help in many complex land-use decision supports, and interest in this integration is increasing day by day to solve many complex spatial problems. Combining MCDM procedures with GIS capabilities is becoming increasingly popular because of the GIS’s capacity to handle enormous amounts of complicated geo-referred data from many sources at a variety of spatial, temporal, and spatial scales, resulting in a time-efficient analysis [25
]. GIS-based multicriteria decision making (GIS-MCDM) can be defined as a process of integrating and transforming geographic data (input map criteria) and value judgments (decision makers’ preferences and uncertainties) into an overall assessment of the decision alternatives [26
]. Due to its applicability to solving the complex spatial problem, GIS-MCDM was applied in the decision making of the many land-use allocation problems. However, although the GIS-MCDM approach incorporated public participation, the inclusion of sustainability criteria was overlooked. It was also noted that only the physical criteria dominated in the GIS-MCDM approach in land-use decision making with little attention to the inclusion of sustainability criteria. For example, Nguyen et al. [27
] designed a GIS-based multicriteria land suitability analysis for sustainable land-use planning at the regional level in Central Vietnam. They used seven criteria for their suitability analysis. Although they used environmental criteria, other two criteria (social and economic) were absent.
Given the above, this study aims to fill the gaps by presenting a GIS-based multicriteria decision-making (GIS-MCDM) approach to optimize residential land-use allocation while considering three dimensions of sustainability (maximization of social, economic, and environmental benefits) and incorporating stakeholder opinion. This study specifically seeks two research questions: (a) how can we integrate sustainability criteria into land-use optimization problems, and (b) does inclusion of sustainability criteria increase the overall sustainability benefits? The contribution of this paper is twofold: (a) integration of sustainability factors to optimize residential land-use allocation and (b) use of our developed method (calculation of social and environmental benefit) in the optimization process. Previously, some proxy variables were used to measure social and environmental benefits. In our previous studies, we developed a method for calculating social and environmental benefits in urban land-use allocation [28
]. In this study, we applied our methods to quantify the social and environmental benefits of land-use allocation.
The rest of the paper is structured as follows. Section 2
describes the literature review. Section 3
describes the data used in this study and the methods followed for optimizing residential location based on selected factors and constraints. This section describes the methods to calculate, standardize, and aggregate the factors to derive combined land-use suitability for residential development. Section 4
presents and discusses the findings of the study. Finally, this paper ends with Section 5
, which contains concluding remarks on this study.
2. Literature Review
Urban land-use optimization planning is very important to achieve long-term urban sustainability. However, it becomes a global challenge, especially in developing countries, to optimize urban land-use allocation in the face of rapid urbanization, migration, and climate change [4
]. Sustainable urban land-use optimization/allocation is considered an effective tool to achieve urban sustainability. Sustainability is one of the important goals of urban land-use planning, which requires taking into account the social, economic, and environmental benefits of people [33
]. Sustainable land-use planning encompasses social, economic, and environmental components, each of which has a distinct objective. Additionally, optimality is a fundamental principle of sustainable land-use planning. Owing to this, many researchers conducted land-use optimization. For example, Handayanto et al. [35
] combined particle swarm optimization, genetic algorithms, and a local search method into a single hybrid optimization method for land-use planning in Bekasi City, Indonesia. They used four criteria to optimize land-use allocation. These are maximizing compactness, compatibility, dependency, and suitability. Li et al. [36
] applied a particle swarm optimization algorithm to optimize urban land-use allocation while maximizing spatial compactness, land ecosystem service value, land-use suitability, and land transformation benefit. Mohammadi et al. [31
] optimized land-use allocation using nondominated sorting genetic algorithm-II (NSGA-II) while maximizing spatial compactness, floor area ratio, land-use compatibility, and economic benefit and maximizing mixed use. However, these studies tried to optimize land-use allocation mainly based on the spatial configuration of urban land-use types in which urban sustainability was ignored. While there are some studies on sustainable land-use optimization [37
], they did not consider all the three dimensions (social, economic, and environmental) of sustainability.
Although some of the early studies included sustainability criteria partially, they did not use any established method to quantify sustainability indicators. They used a single variable-based measure to quantify sustainability dimensions. For example, Zhang et al. [39
] quantified social benefit as a function of the value of social security services, Yuan et al. [38
] quantified social benefit using the spatial compactness of an area, and Cao et al. [37
] used spatial accessibility as a proxy for social sustainability. However, focusing exclusively on a single metric may not be the optimal way to assess social benefit. Jenks and Jones [40
], for example, noted that while spatial compactness in cities offers numerous social benefits, it may result in less living space, reduced access to open spaces, less affordable housing, and bad health. Even a compact city may suffer negative consequences if land uses are incompatible. If the land uses are compatible but there is a lack of accessibility, the social benefit is also reduced. Thus, compatibility and accessibility are also factors in determining social benefit. Similarly, there may be additional indicators contributing to the social benefit metric. As a result, we argue that many indicators can be ascribed to the social benefit metric. As a result, some form of the composite index is necessary to quantify societal gain. To resolve this problem, Rahman and Szabó [29
] developed a composite index to quantify social benefit in land-use allocation. In the same way, there was no established method to quantify environmental benefits. Several researchers used several methods to measure environmental benefits in urban land-use allocation. Yuan et al. [38
], for example, employed carbon storage as a proxy for quantifying environmental benefits, assuming that carbon storage can help sustain air pollution levels. Numerous studies have also used spatial compactness as a proxy for environmental benefits, assuming that a compact city is more sustainable and livable [41
], provides better access to city facilities and promotes public health and well-being [42
], and can maximize the overall environmental benefits of people [43
]. However, environmental benefits can also be measured according to the approach proposed by Rahman and Szabó [29
In the introduction section, it was mentioned that public participation was very negligible in the early land-use optimization problem. To overcome this, the multicriteria decision-making (MCDM) approach was introduced [44
]. With the help of an MCDM process, decision making is aided by helping to structure the problem and providing all parties involved with a common language for discussing and learning about the problem [46
]. Since its inception, MCDM has been applied in many fields including land-use planning. For example, Zhang et al. [47
] proposed a GIS-based multicriteria decision analysis technique to resolve conflicts in urban land-use allocation games. They developed a spatial conflict resolution strategy to help stakeholders and planners to formulate specific land-use proposals through an iterative modification process. However, they did not mention sustainability criteria in conflict resolution. Mendas and Delali [48
] integrated multicriteria decision analysis in GIS to develop land suitability for wheat cultivation in the region of Mleta in Algeria. They also did not include sustainability criteria in their suitability analysis. Luan et al. [49
] conducted a land-use suitability assessment for urban development using a GIS-based MCDM approach in Ili Valley, China. They used 13 criteria for land suitability assessment. Although they grouped some subcriteria under socioeconomic factors, still, sustainability criteria were not fully addressed. Romano et al. [25
] integrated geographical information systems (GIS) and multicriteria decision analysis (MCDA) to evaluate the potential of a rural coastal area, located in northern Puglia (Southern Italy), to improve its sustainable development through the restoration of farmland. They used only physical criteria for sustainable allocation of farmland. In fact, there was no specific inclusion of sustainability criteria. Therefore, it is evident that although stakeholder participation was addressed in the MCDM approach in land-use optimization/allocation, still, the inclusion of sustainability criteria (social, environmental, and economic) is weak. This inevitably requires incorporating both sustainability criteria and a participatory approach in sustainable urban land-use optimization.
In this study, we used the MCDM approach to select the optimal location for a new residential area. We used five physical criteria and three sustainability criteria (social, economic, and environmental benefits) to determine the optimal location while maximizing the sustainability benefits. First, we created suitability maps for each criterion using the fuzzy membership function. Then, we created a combined suitability map using ordered weighted average (OWA) [50
] techniques for different decision risk scenarios. Finally, we identified the optimal location considering overall sustainability benefits.