Compact cities are recognized as sustainable urban forms rather than sprawl developments. Such cities are characterized by high density, land use diversity, accessibility, and efficient public transportation. However, few studies investigate how and how much these parameters affect and relate to compact cities. For instance, although mixed land use is the main key planning principle of compact development, no standard method exists for quantifying, measuring, and evaluating this parameter. This study performs a compact development analysis of Kajang City (Malaysia) with emphasis on evaluating and discussing the importance of mixed land use development. First, the land use diversity of Kajang City was measured. Second, the probability map of mixed land use developments was predicted using a weights-of-evidence (WoE) model. Finally, the importance of mixed land use for compact cities was evaluated using multicriteria decision analysis (MCDA). The created mixed land use probability map was validated using the receiver operating characteristic (ROC) technique. In addition, the 75% similarity between mixed land use and compact development suitability maps highlighted the importance of mixed land use development for compact cities. Results can be used as preliminary guidelines for local governments and planners regarding compact development and management to achieve sustainable urban forms.
Geographic information systems (GIS) have been integrated to many applications in facility location problems today. However, there are still some GIS capabilities yet to be explored thoroughly. This study utilizes the capability of GIS to generate service areas as the travel time zones in a facility location model called the maximal service area problem (MSAP). The model is addressed to emergency facilities for which accessibility is an important requirement. The objective of the MSAP is to maximize the total service area of a specified number of facilities. In the MSAP, continuous space is deemed as the demand area, thus the optimality was measured by how large the area could be served by a set of facilities. Fire stations in South Jakarta, Indonesia, were chosen as a case study. Three heuristics, genetic algorithm (GA), tabu search (TS) and simulated annealing (SA), were applied to solve the optimization problem of the MSAP. The final output of the study shows that the three heuristics managed to provide better coverage than the existing coverage with the same number of fire stations within the same travel time. GA reached 82.95% coverage in 50.60 min, TS did 83.20% in 3.73 min, and SA did 80.17% in 52.42 min, while the existing coverage only reaches 73.82%.
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