Designing an integrated urban growth prediction model: a scenario-based approach for preserving scenic landscapes

Abstract

This study demonstrates the integration of landscape aesthetic quality and probable urban growth patterns in urban landscape modelling. This was performed using SLEUTH as a scenario-based urban growth model in Gorgan City of Iran. Future urbanization was predicted under developing three different scenarios including historical, managed and aesthetically sound urban growth up to the year 2030. Multi-Layer Perceptron neural network model was conducted for mapping the aesthetic suitability of the study area. The aesthetic suitability layer was used in the third scenario of SLEUTH model as the excluded layer to protect the scenic patches in future. The results showed that by correct implementation of urban growth policies, 323 ha in the second scenario and 650 ha in the third scenario would be saved. This integrated model would help the planners for a better management of urban landscapes as a Spatial Decision Support System.     

Study on forecast scenarios for simulation of future urban growth in Shenyang City based on SLEUTH model

The SLEUTH urban growth model was used to simulate future urban growth patterns and to explore potential environ-mental impacts of urban development under different conditions of development in Shenyang City, China. The SLEUTH model was calibrated with historical data (1988-2004) extracted from a time series of TM satellite images, and the future growth was pro-jected out to 2030 assuming three different policy scenarios: (1) current trends scenario (Scenario CT), (2) regional policy and ur-ban planning sce...     

Measuring the effect of an ongoing urbanization process on biodiversity conservation suitability index: integrating scenario-based urban growth modelling with Conservation Assessment and Prioritization System (CAPS)

The present study adopts an integrative modelling methodology, which combines the strengths of the SLEUTH model and the Conservation Assessment and Prioritization System (CAPS) method. By developing a scenario-based geographic information system simulation environment for Hashtpar City, Iran, the manageability of the landscape under each urban growth scenario is analysed. In addition, the CAPS approach was used for biodiversity conservation suitability mapping. The SLEUTH model was implemented to generate predictive urban layers of the years 2020, 2030, 2040 and 2050 for each scenario (dynamic factors for conservation suitability mapping). Accordingly, conservation suitability surface of the area is updated for each time point and under each urban development storyline. Two-way analysis of variance and Duncan’s new multiple range tests were employed to compare the functionality of the three scenarios. Based on results, the managed urban growth scenario depicted better results for manageability of the landscape and less negative impact on conservation suitability values.     

An analysis of changes in the urban landscape composition and configuration in the Sancaktepe District of Istanbul Metropolitan City,Turkey using landscape metrics and satellite data

Changes in landscape composition and configuration patterns of Sancaktepe Municipal District in the Asian side of Istanbul Metropolitan City of Turkey were analysed using landscape metrics. Class-level and landscape-level metrics were calculated from the land cover/land use data using Patch Analyst, an extension in the Arc View GIS. The land cover/land use data were derived from classified satellite images of Landsat Thematic Mapper of 2002 and 2009 for Sancaktepe District. There was evidence of increase in agglomeration process of built-up patches as indicated by the increases in mean patch size, decrease in total edge and number of patches between 2002 and 2009. The urban expansion pattern experienced overall was not fragmented but concentrated due to infilling around existing patches. Changes in Area-Weighted Mean Shape Index and Area-Weighted Patch Fractal Dimension Index indicated that the physical shapes within built-up, forest and bareland areas were relatively complex and irregular. A conclusion is made in this study that spatial metrics are useful tools to describe the urban landscape composition and configuration in its various aspects and certain decisions whether to approve a specific development in urban planning could, for example, be based on some measures of urban growth form or pattern in terms of uniformity and irregularity, attributable to the dynamic processes of agglomeration and fragmentation of land cover/land use patches caused by urban expansion.     

Effects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptation

The urban heat island (UHI) refers to the phenomenon of higher atmospheric and surface temperatures occurring in urban areas than in the surrounding rural areas. Mitigation of the UHI effects via the configuration of green spaces and sustainable design of urban environments has become an issue of increasing concern under changing climate. In this paper, the effects of the composition and configuration of green space on land surface temperatures (LST) were explored using landscape metrics including percentage of landscape (PLAND), edge density (ED) and patch density (PD). An oasis city of Aksu in Northwestern China was used as a case study. The metrics were calculated by moving window method based on a green space map derived from Landsat Thematic Mapper (TM) imagery, and LST data were retrieved from Landsat TM thermal band. A normalized mutual information measure was employed to investigate the relationship between LST and the spatial pattern of green space. The results showed that while the PLAND is the most important variable that elicits LST dynamics, spatial configuration of green space also has significant effect on LST. Though, the highest normalized mutual information measure was with the PLAND (0.71), it was found that the ED and PD combination is the most deterministic factors of LST than the unique effects of a single variable or the joint effects of PLAND and PD or PLAND and ED. Normalized mutual information measure estimations between LST and PLAND and ED, PLAND and PD and ED and PD were 0.7679, 0.7650 and 0.7832, respectively. A combination of the three factors PLAND, PD and ED explained much of the variance of LST with a normalized mutual information measure of 0.8694. Results from this study can expand our understanding of the relationship between LST and street trees and vegetation, and provide insights for sustainable urban planning and management under changing climate.     

Urbanization between compactness and dispersion: designing a spatial model for measuring 2D binary settlement landscape configurations

Measuring spatial patterns is a crucial task in spatial sciences. Multiple indicators have been developed to measure patterns in a quantitative manner. However, most comparative studies rely on relative comparisons, limiting their explanatory power to specific case studies. Motivated by advancements in earth observation providing unprecedented resolutions of settlement patterns, this paper suggests a measurement technique for spatial patterns to overcome the limits of relative comparisons. We design a model spanning a feature space based on two metrics – largest patch index and number of patches. The feature space is defined as ‘dispersion index’ and covers the entire spectrum of possible two-dimensional binary (settlement) patterns. The model configuration allows for an unambiguous ranking of each possible pattern with respect to spatial dispersion. As spatial resolutions of input data as well as selected areas of interest influence measurement results, we test dependencies within the model. Beyond, common other spatial metrics are selected for testing whether they allow unambiguous rankings. For scenarios, we apply the model to artificially generated patterns representing all possible configurations as well as to real-world settlement classifications differing in growth dynamics and patterns.     

Spatiotemporal variability of urban growth factors: A global and local perspective on the megacity of Mumbai

The rapid growth of megacities requires special attention among urban planners worldwide, and particularly in Mumbai, India, where growth is very pronounced. To cope with the planning challenges this will bring, developing a retrospective understanding of urban land-use dynamics and the underlying driving-forces behind urban growth is a key prerequisite. This research uses regression-based land-use change models – and in particular non-spatial logistic regression models (LR) and auto-logistic regression models (ALR) – for the Mumbai region over the period 1973–2010, in order to determine the drivers behind spatiotemporal urban expansion. Both global models are complemented by a local, spatial model, the so-called geographically weighted logistic regression (GWLR) model, one that explicitly permits variations in driving-forces across space. The study comes to two main conclusions. First, both global models suggest similar driving-forces behind urban growth over time, revealing that LRs and ALRs result in estimated coefficients with comparable magnitudes. Second, all the local coefficients show distinctive temporal and spatial variations. It is therefore concluded that GWLR aids our understanding of urban growth processes, and so can assist context-related planning and policymaking activities when seeking to secure a sustainable urban future.