“…Although the scope of government power at varying organizational levels of unitary, federal countries may differ in detail, they generally have similar planning logic and execution frameworks [1][2][3][4][5]. There are many types of spatial planning, however, and this can lead to contradictions, and even conflicts, in the implementation process [6][7][8], as some departments may not consider the interests of others in their plans. The innovation of China's territorial spatial planning lies in the construction of a multiple-planning integration system, which would integrate economic and social development goals and balance the interests of different departments.…”
Section: Discussionmentioning
confidence: 99%
“…For example, Lopes et al thought that city planning encompasses disciplines related to socio-economic, land-use, transport, environment, and others, but these disciplines face communication difficulties and objectives divergence due to contradictory interests and isolated evolution [6]. Sangawongse et al took Wat Ket, Chiang Mai, Thailand as an example to study its transition between planning modes, from centralized planning to collaborative urban land use planning [7]. Kaczmarek et al developed a machine learning approach for the integration of spatial development plans based on natural language processing [8].…”
Optimization of the territorial spatial patterns can promote the functional balance and utilization efficiency of space, which is influenced by economic, social, ecological, and environmental factors. Consequently, the final implementation of spatial planning should address the issue of sustainable optimization of territorial spatial patterns, driven by multiple objectives. It has two components—the territorial spatial scale prediction and its layout simulation. Because a one-sided study of scale or layout is divisive, it is necessary to combine the two to form complete territorial spatial patterns. This paper took Hefei city as an example and optimized its territorial spatial scale using the multiple objective programming (MOP) model, with four objective functions. A computer simulation of the territorial spatial layout was created, using the patch-generating land use simulation (PLUS) model, with spatial driving factors, conversion rules, and the scale optimization result. To do this, statistical, empirical, land utilization, and spatially driven data were used. The function results showed that carbon accumulation and economic and ecological benefits would be ever-increasing, and carbon emissions would reach their peak in 2030. The year 2030 was a vital node for the two most important land use types in the spatial scale—construction land and farmland. It was projected that construction land would commence its transition from reduced to negative growth after that time, and farmland would start to rebound. The simulation results indicated that construction land in the main urban area would expand primarily to the west, with supplemental expansion to the east and north. In contrast, construction land in the counties would experience a nominal increase, and a future ecological corridor would develop along the route south of Chaohu County–Chaohu Waters–Lujiang County–south of Feixi County.
“…Although the scope of government power at varying organizational levels of unitary, federal countries may differ in detail, they generally have similar planning logic and execution frameworks [1][2][3][4][5]. There are many types of spatial planning, however, and this can lead to contradictions, and even conflicts, in the implementation process [6][7][8], as some departments may not consider the interests of others in their plans. The innovation of China's territorial spatial planning lies in the construction of a multiple-planning integration system, which would integrate economic and social development goals and balance the interests of different departments.…”
Section: Discussionmentioning
confidence: 99%
“…For example, Lopes et al thought that city planning encompasses disciplines related to socio-economic, land-use, transport, environment, and others, but these disciplines face communication difficulties and objectives divergence due to contradictory interests and isolated evolution [6]. Sangawongse et al took Wat Ket, Chiang Mai, Thailand as an example to study its transition between planning modes, from centralized planning to collaborative urban land use planning [7]. Kaczmarek et al developed a machine learning approach for the integration of spatial development plans based on natural language processing [8].…”
Optimization of the territorial spatial patterns can promote the functional balance and utilization efficiency of space, which is influenced by economic, social, ecological, and environmental factors. Consequently, the final implementation of spatial planning should address the issue of sustainable optimization of territorial spatial patterns, driven by multiple objectives. It has two components—the territorial spatial scale prediction and its layout simulation. Because a one-sided study of scale or layout is divisive, it is necessary to combine the two to form complete territorial spatial patterns. This paper took Hefei city as an example and optimized its territorial spatial scale using the multiple objective programming (MOP) model, with four objective functions. A computer simulation of the territorial spatial layout was created, using the patch-generating land use simulation (PLUS) model, with spatial driving factors, conversion rules, and the scale optimization result. To do this, statistical, empirical, land utilization, and spatially driven data were used. The function results showed that carbon accumulation and economic and ecological benefits would be ever-increasing, and carbon emissions would reach their peak in 2030. The year 2030 was a vital node for the two most important land use types in the spatial scale—construction land and farmland. It was projected that construction land would commence its transition from reduced to negative growth after that time, and farmland would start to rebound. The simulation results indicated that construction land in the main urban area would expand primarily to the west, with supplemental expansion to the east and north. In contrast, construction land in the counties would experience a nominal increase, and a future ecological corridor would develop along the route south of Chaohu County–Chaohu Waters–Lujiang County–south of Feixi County.
“…At present, Chiang Mai is a provincial city that serves as an economic, educational, and tourist hub of northern Thailand. City development and rapid urbanization have transformed the city's land usage from that of an agricultural society to one with a focus on capitalist economic growth, leading to an increase in population and tourists [41][42][43]. The square-shaped city wall area stands as the city center, surrounded by marketplaces and commercial districts [44] (see Figure 2) that attract low-income migrants from marginalized areas, such as the mountainous regions, other provinces in Thailand, and neighboring countries, in search of better job opportunities.…”
Beyond conserving urban heritage, the concept of historic urban landscapes (HULs) aims to tackle poverty and inequality, as well as to improve the quality of human settlements, through a people-centered approach in the context of rapid urbanization. This paper demonstrates the adaption of HUL tools and methodologies to investigate a slum in a historical city—the informal settlements along the Mae Kha Canal in Chiang Mai. An on-site field survey of the characteristics of the settlement’s composition and interviews with stakeholders, local authorities, and inhabitants were conducted. The analysis revealed that there is a gap between the desired strategy of the Mae Kha Canal agenda and the actual conditions of the location. The aforementioned contrast is discussed to suggest alternate options for upgrading the informal community while preserving its ancient walls, in line with HUL principles. The conclusion highlights the benefits of introducing the HUL approach in a slum setting and provides recommendations for deteriorated neighborhoods elsewhere that are either surrounded by or adjacent to historical features needing resilience.
“…Urbanization is a global phenomenon that creates and enhances challenges around the world and raises the question, of how cities should be designed (Sangawongse et al 2021). This and the growing belief of governments and planning authorities, that citizens, as they have to live with the results of urban projects should be included in the development and design of the project's vision, led to the increased application of citizen participation in urban planning (Krätzig and Warren-Kretzschmar 2014).…”
As a result of the increasing requirements for urban planning, a paradigm shift towards citizen participation has evolved to collaboratively address enhancing urban challenges and social conflicts. Past projects have examined urban citizen participation processes and methods to support citizen participation. However, the challenges in the domain of informing, encouraging, and enabling participation at any time are not sufficiently examined and less attention was devoted to urban participation through mobile applications, even if required devices are widely used and can enable permanent communication channels between citizens and planning authorities. Therefore, a design science research project was initiated to examine how to design mobile applications to support citizen participation in urban planning projects. In this paper, the findings of the first cycle are presented including issues, meta-requirements, design principles, the development of a mock-up, and its evaluation to provide insight into the design of mobile applications for citizen participation.
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