Land Use Simulation Research Articles

High-resolution urban change modeling and flood exposure estimation at a national scale using open geospatial data: A case study of the Philippines

Many developing countries in Asia are experiencing rapid urban expansion in climate hazard prone areas. To support climate resilient urban planning efforts, here we present an approach for simulating future urban land-use changes and evaluating potential flood exposure at a high spatial resolution (30 m) and national scale. As a case study, we applied this model to the Philippines – a country frequently affected by extreme rainfall events. Urban land-use changes were simulated to the year 2050 using a trend-based logistic regression cellular automata model, considering three different scenarios of urban expansion (assuming low/medium/high population growth). Flood exposure assessment was then conducted by overlaying the land-use simulation results onto a global floodplain map. We found that approximately 6040–13,850 ha of urban land conversion is likely to be located in flood prone regions between 2019 and 2050 (depending on the scenario), affecting approximately 2.5–5.8 million additional urban residents. In locations with high rates of future urban development in flood prone areas (Mindanao Island, in particular), climate resilient land-use plans should be developed/enforced, and flood mitigation infrastructure protected (in the case of “nature-based” infrastructure) or constructed. The data selected for our land-use change modeling and flood exposure assessment were all openly and (near-)globally available, with the intention that our methodology can potentially be applied in other countries where rapid urban expansion is occurring. The 2050 urban land-use maps generated in this study are available for download at https://www.iges.or.jp/en/pub/ph-urban2050/en to allow for their use in future works.

Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model

The simulation of future land use can provide decision support for urban planners and decision makers, which is important for sustainable urban development. Using a cellular automata-random forest model, we considered two scenarios to predict intra-land use changes in Kumamoto City from 2018 to 2030: an unconstrained development scenario, and a planning-constrained development scenario that considers disaster-related factors. The random forest was used to calculate the transition probabilities and the importance of driving factors, and cellular automata were used for future land use prediction. The results show that disaster-related factors greatly influence land vacancy, while urban planning factors are more important for medium high-rise residential, commercial, and public facilities. Under the unconstrained development scenario, urban land use tends towards spatially disordered growth in the total amount of steady growth, with the largest increase in low-rise residential areas. Under the planning-constrained development scenario that considers disaster-related factors, the urban land area will continue to grow, albeit slowly and with a compact growth trend. This study provides planners with information on the relevant trends in different scenarios of land use change in Kumamoto City. Furthermore, it provides a reference for Kumamoto City’s future post-disaster recovery and reconstruction planning.

How to reconcile land use conflicts in mega urban agglomeration? A scenario-based study in the Beijing-Tianjin-Hebei region, China

With the development of global urbanization, land use conflicts have become one of the major issues hindering sustainable land use and human-environment coordination in urbanized areas. In this context, reconciliation of land use conflicts requires urgent attention. By taking the Beijing-Tianjin-Hebei (BTH) urban agglomeration as a case study area, the spatial comprehensive conflict index (SCCI) was constructed to identify and evaluate land use conflicts. Besides, the impacts of rapid urbanization and terrain restriction on land use conflicts were also explored using the coupling coordination degree (CCD) model and terrain index, respectively. Then, the Dyna-CLUE model was adopted to simulate land use conflicts under three adaptive scenarios in 2030. Results show that: (1) During 2000–2015, land use conflicts in the BTH region demonstrated an overall mitigating trend, and their spatial patterns remained relatively stable, characterized by significant cluster and belt agglomeration. (2) Land use conflicts were significantly intensified in areas experiencing rapid urban-rural transformation and terrain transition, and two typical conflict zones were identified, i.e. the urban-rural interface of the Beijing-Tianjin region and the terrain transition area located in the Taihang Mountains, Yan Mountains and Bashang Plateau. (3) In 2030, land use conflicts in the BTH region manifest overall mitigation under the ecological security (ES) scenario, while demonstrating an intensifying trend under the business as usual (BAU) scenario and cropland protection (CP) scenario. Based on simulation results, land use spatial optimization modes at county level for the BTH region were formulated. In face of increasingly prominent land use conflicts globally, this study will provide a scientific reference for policymaking in pursuit of sustainable land use management for the BTH region and urban agglomerations in other parts of the world.

Coupled SSPs-RCPs scenarios to project the future dynamic variations of water-soil-carbon-biodiversity services in Central Asia

Global climate change and human activities are expected to have far-reaching implications for the associations between ecosystem services (ESs), especially in arid regions. Here, Central Asia (CA) was taken as a case study to describe the complex relationship among key ESs under the combined effects of future climate change and socioeconomic development. We propose a new framework that integrates the future land-use simulation (FLUS) model and integrated valuation of ESs and trade-offs (InVEST) model. A four-model ensemble mean from the Coupled Model Intercomparison Project 6 (CMIP6) was chosen to project future (2021–2100) variations in water yield (WY), soil conservation (SC), carbon storage (CS) and habitat quality (HQ). Spearman correlation was adopted to analyze the trade-offs and synergies between multiple ESs. Results showed that cropland degradation (−4.11% to −19.93%) and urban (+33.14% to +127.96%) and forestland (+5.31% to +25.52%) expansion will be the main forms of future land-use change in CA. Compared with the reference period (1995–2015), four ESs will exhibit different decreasing trends across CA under the three scenarios. We observed that the risk of soil erosion will increase in the mountainous areas of Kyrgyzstan and Tajikistan; cropland degradation and urban expansion would lead to a sharp reduction of CS and HQ in the Amu Darya Basin, Syr Darya Basin and southern Turkmenistan, especially in SSP245 scenario. We found that the weak pairwise correlations between HQ, SC and CS will be strengthened (R = 0.22–0.58; p < 0.05) in Kyrgyzstan and Tajikistan, whereas the significant positive correlation (R = 0.47–0.60; p < 0.01) between WY and SC will be weakened. An important information/recommendation provided by this study is that different management strategies should be designed in accordance with the major interactions among water, soil, carbon and biodiversity services.

Linking the past, present and future scenarios of soil erosion modeling in a river basin

BACKGROUND AND OBJECTIVE: Soil erosion is considered one of the major indicators of soil degradation in our environment. Extensive soil erosion process leads to erosion of nutrients in the topsoil and decreases in fertility and hence productivity. Moreover, creeping erosion leads to landslides in the hilly regions of the study area that affects the socio-economics of the inhabitants. The current study focuses on the estimation of soil erosion rate for the year 2011 to 2019 and projection for the years 2021, 2023 and 2025.METHODS: In this study, the Revised Universal Soil Loss Equation is used for estimation of soil erosion in the study area for the year 2011 to 2019. Using Artificial Neural Network-based Cellular Automata simulation, the Land Use Land Cover is projected for the future years 2021, 2023 and 2025. Using the projected layer as one of the spatial variables and applying the same model, Soil Erosion based on Revised Universal soil loss equation is projected for a corresponding years.FINDINGS: For both cases of projection, simulated layers of 2019 (land use land cover and soil erosion) are correlated with the estimated layer of 2019 using actual variables and validated. The agreement and accuracy of the model used in the case land use are 0.92 and 96.21% for the year 2019. The coefficient of determination of the model for both simulations is also observed to be 0.875 and 0.838. The simulated future soil erosion rate ranges from minimum of 0 t/ha/y to maximum of 524.271 t/ha/y, 1160.212 t/ha/y and 783.135 t/ha/y in the year 2021, 2023 and 2025, respectively.CONCLUSION: The study has emphasized the use of artificial neural network-based Cellular automata model for simulation of land use and land cover and subsequently estimation of soil erosion rate. With the simulation of future soil erosion rate, the study describes the trend in the erosion rate from past to future, passing through present scenario. With the scarcity of data, the methodology is found to be accurate and reliable for the region under study.

Integration of Neural Network, Markov Chain and CA Markov Models to Simulate Land use Change Region of Behbahan County

Purpose- Important land use typical of human impact on the environment and in the past half century, land use changes has been the largest and most complete. the modeling of land cover changes provides valuable information for understanding changes process, determining effective factors and forecasting areas which are exposed to changes. Therefore, the present study aims at (i) using the Landsat imagery to prepare land use- cover (LULC) maps for 2000 and 2014; (ii) assessing Land use changes on the basis of land change modeler (LCM) for the period from 2000 and 2014, and (iii) predicting the plausible land cover pattern in the region of Behbahan using an algorithm based on ANN for 2028. Design/methodology/approach- Hybrid model consisting of neural network model, Markov chain (MC), and cellular automata (CA Markov) were designed to improve the performance of the standard network model. The modeling of transfer power is done by multilayer Perceptron of artificial neural network and six variables. The change allocated to each use and the forecasting is computed by Markov chain and CA Markov. Operation model calibration and verification of land use data at two points, was conducted in 2000 and 2014 Finding- Modeling results indicate that the model validation phase, has a good ability to predict land use change on the horizon is 14 years old (2028). The comparison between modeling map and map related to 2013 show that residential area and agricultural land continue to their own growth trend so that residential area will be increased from 3157 hectares in 2014 to 4180 hectares in 2028 and it has 2% growth that this level of growth has been 2% from 2000 to 2014. Results of this study can provide suitable perspective for planners to manage land use with regard to land use changes in past, present and future. They are also can be used for development assessment projects, the cumulative effects assessment, and the vulnerable and sensitive zone recognition

Scenario simulation of land use and land cover change in mining area

In this study, we selected 11 townships with severe ground subsidence located in Weishan County as the study area. Based on the interpretation data of Landsat images, the Binary logistic regression model was used to explore the relationship between land use and land cover (LULC) change and the related 7 driving factors at a resolution of 60 m. Using the CLUE-S model, combined with Markov model, the simulation of LULC under three scenarios—namely, natural development scenario, ecological protection scenario and farmland protection scenario—were explored. Firstly, using LULC map in 2005 as input data, we predicted the land use spatial distribution pattern in 2016. By comparing the actual LULC map in 2016 with the simulated map in 2016, the prediction accuracy was evaluated based on the Kappa index. Then, after validation, the spatial distribution pattern of LULC in 2025 under the three scenarios was simulated. The results showed the following: (1) The driving factors had satisfactory explanatory power for LULC changes. The Kappa index was 0.82, which indicated good simulation accuracy of the CLUE-S model. (2) Under the three scenarios, the area of other agricultural land and water body showed an increasing trend; while the area of farmland, urban and rural construction land, subsided land with water accumulation, and tidal wetland showed a decreasing trend, and the area of urban and rural construction land and tidal wetland decreased the fastest. (3) Under the ecological protection scenario, the farmland decreased faster than the other two scenarios, and most of the farmland was converted to ecological land such as garden land and water body. Under the farmland protection scenario, the area of tidal wetland decreased the fastest, followed by urban and rural construction land. We anticipate that our study results will provide useful information for decision-makers and planners to take appropriate land management measures in the mining area.

Limitations of WRF land surface models for simulating land use and land cover change in Sub-Saharan Africa and development of an improved model (CLM-AF v. 1.0)

Abstract. Land use and land cover change (LULCC) impacts local and regional climates through various biogeophysical processes. Accurate representation of land surface parameters in land surface models (LSMs) is essential to accurately predict these LULCC-induced climate signals. In this work, we test the applicability of the default Noah, Noah-MP, and Community Land Model (CLM) LSMs in the Weather Research and Forecasting (WRF) model over Sub-Saharan Africa. We find that the default WRF LSMs do not accurately represent surface albedo, leaf area index, and surface roughness in this region due to various flawed assumptions, including the treatment of the MODIS woody savanna land use and land cover (LULC) category as closed shrubland. Consequently, we developed a WRF CLM version with more accurate African land surface parameters (CLM-AF), designed such that it can be used to evaluate the influence of LULCC. We evaluate meteorological performance for the default LSMs and CLM-AF against observational datasets, gridded products, and satellite estimates. Further, we conduct LULCC experiments with each LSM to determine if differences in land surface parameters impact the LULCC-induced climate responses. Despite clear deficiencies in surface parameters, all LSMs reasonably capture the spatial pattern and magnitude of near-surface temperature and precipitation. However, in the LULCC experiments, inaccuracies in the default LSMs result in illogical localized temperature and precipitation changes. Differences in thermal changes between Noah-MP and CLM-AF indicate that the temperature impacts from LULCC are dependent on the sensitivity of evapotranspiration to LULCC in Sub-Saharan Africa. Errors in land surface parameters indicate that the default WRF LSMs considered are not suitable for LULCC experiments in tropical or Southern Hemisphere regions and that proficient meteorological model performance can mask these issues. We find CLM-AF to be suitable for use in Sub-Saharan Africa LULCC studies, but more work is needed by the WRF community to improve its applicability to other tropical and Southern Hemisphere climates.

Sediment yield prediction and prioritization of sub-watersheds in the Upper Subarnarekha basin (India) using SWAT

The present study deals with the estimation of soil loss from the Upper Subarnarekha catchment in Odisha (India) using Soil and Water Assessment Tool (SWAT). Sequential uncertainty fitting (SUFI-2) algorithm of the SWAT calibration uncertainty programs (SWAT-CUP) was used for model simulation. The model was calibrated with the observed data for the period from 1996 to 2008 with first 3 years (1996–1998) as warm-up period. Further, validation of the model was done using 5-year data from 2009 to 2013. Reliable evaluation of the model performance during calibration has been substantiated by the coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percentage bias (PBIAS) as 0.81, 0.81, and −0.15, respectively, and the respective values for the validated model were found to be 0.79, 0.78, and −0.19. The values of P-factor and R-factor were found to be 0.80 and 0.75 and 0.66 and 0.74, respectively, for model calibration and validation. Average annual soil loss from the catchment was 4.84 Mg ha−1. The watershed indexed as SW18 resulted in highest soil loss in the range of 10–15 Mg ha−1year−1. Further, prioritization was done at the level of sub-watersheds using the data of simulated sediment yield, soil texture, land use, and slope for identifying vulnerable sub-watersheds that need immediate attention. The study inferred that sub-watersheds having index numbers SW17, SW18, and SW19 are highly vulnerable, and hence top priority should be given to these sub-watersheds for reduction in soil erosion through the implementation of suitable soil and water conservation measures.

The impacts of historical land-use on phosphorus movement in the Calhoun Critical Zone Observatory in the southeastern US Piedmont

Human actions through land-use can alter soil phosphorus (P) distribution over time and space as vegetation is altered and added fertilizer P is translocated downslopes by runoff and erosion, or through the soil profile by leaching. In the southeastern US Piedmont, a more than 100-year period of human land-use of forest clearing and farming, which included P fertilization, caused severe surface erosion before reforestation. This history resulted in elevated surface soil P in farmed ridge tops, even after 70 years of reforestation, but little data exists on redistribution of this P downslope or down profile during these 70 years. We aimed to investigate the effect of different land-use histories on soil P losses over time. Combined with multiple years of current soils, soil solution, and stream water data from two small watersheds in the Calhoun Critical Zone Observatory (Calhoun CZO) in South Carolina, USA, we use the soil and water assessment tool (SWAT) to simulate a trajectory of seven different land-uses on P movement. Results indicated annual solution total P loss under 100% agriculture (3.7 kg ha−1 year−1) was six times greater than under 100% forest while mixed forest and agriculture experienced about one-third the loss observed under agricultural land. Furthermore, the model predicted P leaching that ranged from 0.036 to 0.1 kg ha−1 year−1 with the highest rates during early reforestation. These increased leaching rates could account for 7–20 kg ha−1 of vertical P movement through the soil profile over 200 years. This content of P leaching is consistent with observed differences in the content of extractable soil P in the 2 m of soil under farmed vs. never farmed locations. Simulation under high rainfall indicated wet periods or events could augment P mobilization and better approximated observed increases in soil extractable P content. These land-use change simulations indicate reforestation reduces surface runoff and erosion but increases vertical leaching and, despite soil P retention mechanisms, may move P deep into the soil profile.

Trade-Off and Projecting Effects of Land Use Change on Ecosystem Services under Different Policies Scenarios: A Case Study in Central China

Predicting the spatio-temporal evolution characteristics and trade-off/synergy relationships of ecosystem service value (ESV) under different policy scenarios is of great significance for realizing regional sustainable development. This study established a framework and used the geographical simulation and optimization systems-future land use simulation (GeoSOS-FLUS) model and bivariate local autocorrelation analysis to stimulate and predict the impact of land use change on the ESV of Anyang City from 1995 to 2025. We also explored the trade-offs and synergy among ecosystem services under three policy scenarios (natural evolution, cultivated land protection, and ecological protection) in 2025. Results show that (1) the land use change in Anyang from 1995 to 2025 was significant, and the degree of land use change under the cultivated land and ecological protection scenarios was more moderate than that under the natural evolution scenario; (2) The total ESV decreased between 1995 and 2015, amounting to losses of 1126 million yuan, and the decline from 2015 to 2025 under the natural evolution scenario was more significant than those under the cultivated land protection and ecological protection scenarios; and (3) an obvious synergy was observed between various ecosystem services in Anyang City under different scenarios in 2025, and the most significant synergy was observed under the natural evolution scenario. In terms of spatial distribution, the agglomeration of “high–high” synergy in the west and “low–low” synergy in the central region was significant. Local areas showed “high–low” and “low–high” trade-off relationships scattered between their built land and woodland or cultivated land. The proposed framework can provide certain scientific support for regulating land use and ecosystem services in rapidly urbanized areas.

Simulating land use changes, sediment yields, and pesticide use in the Upper Paraguay River Basin: Implications for conservation of the Pantanal wetland

As a consequence of accelerated and excessive use of pesticides in tropical regions, wilderness areas are under threat; this includes the Pantanal wetlands in the Upper Paraguay River Basin (UPRB). Using a Land Cover Land Use Change (LCLUC) modelling approach, we estimated the expected pesticide load in the Pantanal and the surrounding highlands region for 2050 under three potential scenarios: i) business as usual (BAU), ii) acceleration of anthropogenic changes (ACC), and iii) use of buffer zones around protected areas (BPA). The quantity of pesticides used in the UPRB is predicted to vary depending on the scenario, from an overall increase by as much as 7.4% in the UPRB in the BAU scenario (increasing by 38.5% in the floodplain and 6.6% in the highlands), to an increase of 11.2% in the UPRB (over current use) under the AAC scenario (increasing by 53.8% in the floodplain and 7.5% in the highlands). Much higher usage of pesticides is predicted in sub-basins with greater agricultural areas within major hydrographic basins. Changing the current trajectory of land management in the UPRB is a complex challenge. It will require a substantial shift from current practices, and will involve the implementation of a number of strategies, ranging from the development of new technologies to achieve changes in land use policies, to increasing dialogue between farmers, ranchers, the scientific community, and local or traditional communities through participatory learning processes and outreach.

Land Use Dynamics and Optimization from 2000 to 2020 in East Guangdong Province, China

Anthropogenic land-use change is one of the main drivers of global environmental change. China has been on a fast track of land-use change since the Reform and Opening-up policy in 1978. In view of the situation, this study aims to optimize land use and provide a way to effectively coordinate the development and ecological protection in China. We took East Guangdong (EGD), an underdeveloped but populous region, as a case study. We used land-use changes indexes to demonstrate the land-use dynamics in EGD from 2000 to 2020, then identified the hot spots for fast-growing areas of built-up land and simulated land use in 2030 using the future land-use simulation (FLUS) model. The results indicated that the cropland and the built-up land changed in a large proportion during the study period. Then we established the ecological security pattern (ESP) according to the minimal cumulative resistance model (MCRM) based on the natural and socioeconomic factors. Corridors, buffer zones, and the key nodes were extracted by the MCRM to maintain landscape connectivity and key ecological processes of the study area. Moreover, the study showed the way to identify the conflict zones between future built-up land expansion with the corridors and buffer zones, which will be critical areas of consideration for future land-use management. Finally, some relevant policy recommendations are proposed based on the research result.

A Cellular Automata Model for Integrated Simulation of Land Use and Transport Interactions

Cellular automata (CA) models have been used in urban studies for dealing with land use change. Transport and accessibility are arguably the main drivers of urban change and have a direct influence on land use. Land use and transport interaction models deal with the complexity of this relationship using many different approaches. CA models incorporate these drivers, but usually consider transport (and accessibility) variables as exogenous. Our paper presents a CA model where transport variables are endogenous to the model and are calibrated along with the land use variables to capture the interdependent complexity of these phenomena. The model uses irregular cells and a variable neighborhood to simulate land use change, taking into account the effect of the road network. Calibration is performed through a particle swarm algorithm. We present an application of the model to a comparison of scenarios for the construction of a ring road in the city of Coimbra, Portugal. The results show the ability of the CA model to capture the influence of change of the transport network (and thus in accessibility) in the land use dynamics.

Trade-Offs and Synergies of Multiple Ecosystem Services for Different Land Use Scenarios in the Yili River Valley, China

Environmental managers and policymakers increasingly discuss trade-offs between ecosystem services (ESs). However, few studies have used nonlinear models to provide scenario-specific land-use planning. This study determined the effects of different future land use/land cover (LULC) scenarios on ESs in the Yili River Valley, China, and analyzed the trade-offs and synergistic response characteristics. We simulated land-use changes in the Yili River Valley during 2020–2030 under three different scenarios using a patch-generating land-use simulation (PLUS) model—business as usual (BAU), economic development (ED), and ecological conservation (EC). Subsequently, we evaluated the water yield (WY), carbon storage (CS), soil retention (SR), and nutrient export (NE) ESs by combining the PLUS and integrated valuation of ecosystem services and trade-offs (InVEST) models, thus exploring multiple trade-offs among these four ESs at a regional scale. For the BAU scenario, there are some synergistic effects between WY and SR in the Yili River Valley, in addition to significant trade-off effects between CS and NE. For the ED scenario, the rapid expansion of cropland and constructed land is at the expense of forested grassland, leading to a significant decline in ESs. For the EC scenario, the model predicted that the cumulative regional net future carbon storage, cumulative water retention, and cumulative soil conservation would all increase due to ecological engineering and the revegetation of riparian zones and that formerly steep agricultural land can be effective in improving ESs. Meanwhile, the trade-off effect would be significantly weakened between CS and NE. These results can inform decision makers on specific sites where ecological engineering is implemented. Our findings can enhance stakeholders’ understanding of the interactions between ESs indicators in different scenarios.

Urbanization impacts on flood risks based on urban growth data and coupled flood models

Urbanization increases regional impervious surface area, which generally reduces hydrologic response time and therefore increases flood risk. The objective of this work is to investigate the sensitivities of urban flooding to urban land growth through simulation of flood flows under different urbanization conditions and during different flooding stages. A sub-watershed in Toronto, Canada, with urban land conversion was selected as a test site for this study. In order to investigate the effects of urbanization on changes in urban flood risk, land use maps from six different years (1966, 1971, 1976, 1981, 1986, and 2000) and of six simulated land use scenarios (0%, 20%, 40%, 60, 80%, and 100% impervious surface area percentages) were input into coupled hydrologic and hydraulic models. The results show that urbanization creates higher surface runoff and river discharge rates and shortened times to achieve the peak runoff and discharge. Areas influenced by flash flood and floodplain increases due to urbanization are related not only to overall impervious surface area percentage but also to the spatial distribution of impervious surface coverage. With similar average impervious surface area percentage, land use with spatial variation may aggravate flash flood conditions more intensely compared to spatially uniform land use distribution.

Exploring regional land use dynamics under shared socioeconomic pathways: A case study in Inner Mongolia, China

Land use change scenarios can contribute to the development of improved policies to tackle climate change. In this study, we developed an integrated modeling framework to simulate land use dynamics in Inner Mongolia, China under the Shared Socio-economic Pathways (SSPs) during 2015–2050. A computable general equilibrium of land use change model was developed to simulate the regional socio-economic effects on future land use demand. The Dynamics of Land System model was applied to simulate the spatial patterns of land use change. Land use change varied considerably among SSPs. Increased agricultural productivity reduced the demand for cultivated land. Increased livestock system efficiency and decreased animal product consumption reduced grassland demand, benefiting natural grassland recovery. Built-up land increased in all SSPs, with positive relation to GDP growth. Under SSP1, forest area, grassland, and built-up land increased by 19940 km² (12.05%), 9882 km² (1.88%), and 20844 km² (129.19%), respectively, while cultivated land decreased by 10995 km² (−9.80%). Grassland decreased considerably under SSP3 and SSP4, and distributed discretely, mainly concentrated on Xilin Gol League and northwestern Ulanqab. Built-up land sprawled considerably under SSP1 and SSP5, and was mainly concentrated in eastern Tongliao and northern Ordos. This integrated modeling framework provides an effective and reliable way to simulate regional land use dynamics under a range of alternative future pathways.

Lessons Learned from Applying an Integrated Land Use Transport Planning Model to Address Issues of Social and Economic Exclusion of Marginalised Groups: The Case of Cape Town, South Africa

The Group Areas Act of 1950 has resulted in post-apartheid South African cities being characterised by spatial patterns with limited access to social and economic opportunities for the black and coloured population. Typically, high-density low-income housing is located peripherally, while low density high-income housing is located in accessible central areas. With increased rural-to-urban migration, the demand for formal housing has historically surpassed supply, which has increased the growth of informal settlements. Current discourse within South African land use policy suggests that in-situ upgrading of informal housing is a viable response to integrate informal settlements into the formal city. In parallel, it is proposed that new low-income residential areas and employment-generating land uses should be located along transport corridors to improve access to transport, its infrastructure and the opportunities it provides for previously marginalised groups. This study uses Cape Town as a case city to explore two land-use driven development strategies directed at informal settlements and low-income housing. A dynamic land use transport model based on a cellular automata land use model and a four-stage transport model was used to simulate land use and transport changes. Specifically, in-situ upgrading of informal settlements and strategically locating new low-income residential and employment generating land uses along transport corridors were considered. The results from the analysis suggest that in-situ upgrading is a viable option only if new informal settlements are in areas with easy access to economic centres. With regards to low-income housing, targeted interventions aimed at ‘unlocking’ low-income housing activities along transport corridors were found to be useful. However, it was also observed that middle-income residential development and employment generating activities were also attracted to the same corridors, thus, resulting in mixed land uses, which is beneficial but can potentially result in rental bids between low and middle-income earners thus displacing low-income earners away from these areas.

Research on an Analytical Framework for Urban Spatial Structural and Functional Optimisation: A Case Study of Beijing City, China

A number of severe ecological problems, and the altered structure of urban spaces, are ascribed to rapid urbanisation. Hence, an analytical framework for urban spatial structure and functional optimisation is highly beneficial to balance the contradiction between developing urban areas and protecting their ecosystems. In this paper, the proposed analytical framework included three parts. We first delineated the ecological suitability zones (ESZs) of Beijing City by applying the minimum cumulative resistance (MCR) model. Subsequently, considering various socioeconomic and natural environmental factors, the Markov chain model and future land-use simulation (FLUS) model were utilised to predict the urban spatial structure of Beijing in 2031. Finally, taking the ESZ results as a constraint, three scenarios were designed to optimise the extent of city sprawl: the business as usual (BAU) scenario, ecological security (ES) scenario and ecological priority (EP) scenario. We found that the ESZs contained three zones: an ecological control zone (63%), a restricted development zone (22%), and a concentrated development zone (15%). After comparing the three scenarios, we discovered that the ES scenarios ensured the bottom line in terms of Beijing’s ecological security. Additionally, under the EP scenario, the urban spatial structure and function were further optimised. Our study can provide new ideas and technical support for the reasonable layout of urban spatial structure.

Predicting the surface urban heat island intensity of future urban green space development using a multi-scenario simulation

The distribution and morphology of urban green space (UGS) have a significant impact on its ability to mitigate surface urban heat island intensity (SUHII). In this study, the Future Land Use Simulation model was used to predict land use in Xuchang City by 2030 under three different scenarios. Landsat Operational Land Imager images from 2014 were used to calculate SUHII. And, the relationships and regression models between summer SUHII and land use data were analyzed and developed. The SUHII in 2030 based on simulated land use was predicted using regression models in 2014. Results showed that reductions in SUHII were greater under present land use scenario with a greater proportion of UGS. Furthermore, the degree of aggregation, average patch area, maximum patch shape index, and complexity of the green space also impacted the effectiveness of SUHII mitigation. In future land use scenarios with different constraints, the ecological service function guided scenario was the most effective at mitigating SUHII. These findings provide a reference for the reasonable allocation of urban land and the optimization of UGS in terms of SUHII reduction in small- and medium-sized cities where there is a need for expansion.