A new type of cities for liveable futures. Isobenefit Urbanism morphogenesis

Coupling Relationship of Urban Development and the Eco-Environment in Guanzhong Region, China

Since the 21st century, China’s rapid urban development has had profound impacts on the regional eco-environment and posed severe threats to regional sustainable development. In order to scientifically evaluate the coupling relationship between urban development and the eco-environment in the Guanzhong region, in this paper, by combining nighttime lighting data and MODIS data in 2001, 2010 and 2018, we applied an overall coupling model and a coordination model to discuss the spatial–temporal coupling and coordination relationship between urban development and the eco-environment. The results showed the following: (1) From 2001 to 2018, the urbanization development in the Guanzhong region significantly improved and the links between cities were continuously strengthened, but the degree of contact still needed to be improved. (2) The eco-environment quality in the Guanzhong region slightly increased, but the overall level was low. The structure of the eco-environment quality grade changed greatly, and “Good” grades changed to “Very Good”. (3) During 2001–2018, the overall coupling situation between urban development and the eco-environment strengthened and the degree of coordination increased. The coupling coordinator subtype gradually transformed from system balanced development into system balanced development, the ecology lag type, and the urban development lag type coexisting phenomenon. The results of the study suggest that future urban development planning and ecological protection policies need to take the coordinated coupling between urban development and the eco-environment into account.

Green and Compact: A Spatial Planning Model for Knowledge-Based Urban Development in Peri-Urban Areas

A seemingly unresolved debate in urban planning is the call for compactness and the provision of intra-urban green spaces. This article defines a multi-scalar spatial planning model for peri-urban areas and urban voids able to reconcile medium to high building densities with the provision of ecosystem services. The research is framed within design science research, and the theoretical definition of the model was followed by its application to the International Hub for Sustainable Development (HIDS) proposed by the University of Campinas, Brazil. The model’s parameters and indicators derive from a literature review, case studies, and GIS spatial analyses. A series of expert workshops and a survey were carried out to test and validate the model. The results show that the model can support knowledge-based development in peri-urban areas with high levels of population density while ensuring good accessibility to green spaces and productive landscapes. The model can serve as a planning and design tool and support the development of public policies for other contexts committed to more resilient and sustainable development.

Monitoring and Modeling the Patterns and Trends of Urban Growth Using Urban Sprawl Matrix and CA-Markov Model: A Case Study of Karachi, Pakistan

Understanding the spatial growth of cities is crucial for proactive planning and sustainable urbanization. The largest and most densely inhabited megapolis of Pakistan, Karachi, has experienced massive spatial growth not only in the core areas of the city, but also in the city’s suburbs and outskirts over the past decades. In this study, the land use/land cover (LULC) in Karachi was classified using Landsat data and the random forest algorithm from the Google Earth Engine cloud platform for the years 1990, 2000, 2010, and 2020. Land use/land cover classification maps as well as an urban sprawl matrix technique were used to analyze the geographical patterns and trends of urban sprawl. Six urban classes, namely, the primary urban core, secondary urban core, sub-urban fringe, scatter settlement, urban open space, and non-urban area, were determined for the exploration of urban landscape changes. Future scenarios of LULC for 2030 were predicted using a CA–Markov model. The study found that the built-up area had expanded in a considerably unpredictable manner, primarily at the expense of agricultural land. The increase in mangroves and grassland and shrub land proved the effectiveness of afforestation programs in improving vegetation coverage in the study area. The investigation of urban landscape alteration revealed that the primary urban core expanded from the core districts, namely, the Central, South, and East districts, and a new urban secondary core emerged in Malir in 2020. The CA–Markov model showed that the total urban built-up area could potentially increase from 584.78 km2 in 2020 to 652.59 km2 in 2030. The integrated method combining remote sensing, GIS, and an urban sprawl matrix has proven invaluable for the investigation of urban sprawl in a rapidly growing city.

Evaluation of Livable City Based on GIS and PSO-SVM: A Case Study of Hunan Province

Under the background of accelerating urbanization and increasing stress of ecological environment, the construction of livable city has attracted extensive attention and become a hot spot in the study of urban problems in the world. The evaluation of livable city is a reference for the comparison of urban development and also one of the evaluation criteria for the comparison of urban competitiveness. This paper focuses on three different evaluation factors of ecological environment, economic development and public service to construct an evaluation model of environmental quality of livable cities. Then particle swarm optimization (PSO) is introduced to optimize the parameters of support vector machine (SVM), and a SVM algorithm based on PSO (PSO-SVM) is proposed to solve the livable city evaluation model. Finally, the spatial analysis combined with ArcGIS software obtained the livable city evaluation and division results of Hunan Province. The results show that PSO-SVM algorithm is superior to SVM, BA-SVM, GA-SVM, and has the advantages of faster speed and higher classification accuracy.

Motivations and built environment factors associated with campus walkability in the tropical settings

Recognizing and mainstreaming pertinent walkability elements into the university campus planning is crucial to materialise green mandates of the campus, while enhancing social and economic sustainability. In one of such attempts, this transverse study investigated the walking motivations, built environment factors associated with campus walkability and the relative importance of the studied built environment factors in reference to the sociodemographic attributes from the viewpoint of the campus community in a tropical university campus in Kuala Lumpur, Malaysia. An online survey using a structured questionnaire was conducted between May and September 2019. The built environment factors associated with campus walkability were expressed and ranked as adjusted scores (AS). Meanwhile, multivariable logistic regression was deployed to examine the relative importance of the studied built environment factors in reference to the sociodemographic attributes of the campus community. Among 504 total responses acquired, proximity between complementary land uses (90.7%) was reported as the main motivation for walking. On the other hand, street connectivity and accessibility (AS: 97.62%) was described as the most opted built environment factor, followed by land use (AS: 96.76%), pedestrian infrastructure (AS: 94.25%), walking experience (AS: 87.07%), traffic safety (AS: 85.28%) and campus neighbourhood (AS: 59.62%), respectively. Among the sociodemographic attributes, no regular monthly income (OR = 3.162; 95% CI = 1.165-8.379; p < 0.05) and willingness to walk more than 60 min inside the campus per day (OR = 0.418; 95% CI = 0.243-0.720; p < 0.05) were significantly associated with the expression of higher importance towards the reported built environment factors in the multivariate analysis. In brief, the findings of this study were envisaged to elicit valuable empirical evidence for informed interventions and strengthening campus sustainable mobility policies.