Since the 20th century, the global urbanization has led to a series of pollution issues, posing a severe threat to the habitat quality of human habitat. The quality of habitat determines whether ecosystems can provide suitable living conditions for humans and other species. Therefore, systematic study of the habitat quality is essential for the maintenance of sustainable development. In this study, we coupled models such as SD, InVEST and PLUS with a series of indicators to analyze the characteristics of land cover and habitat quality evolution in the Guangdong-HongKong-Macao Greater Bay Area (GBA) from 2000 to 2020 and deconstruct the driving mechanisms of habitat quality. Then simulate the evolution of land cover and habitat quality under different scenarios in 2030. The results show that: 1) Over the historical research period, the GBA exhibited "rapid expansion of artificial surfaces and rapid shrinkage of ecological land". Artificial surfaces increased by approximately 4878.95km2,while ecological land, such as agricultural land, decreased by about 3095.93km2.2) The degradation of habitat quality gradually accelerated and the habitat quality was characterized by "stepwise decline from the periphery to the interior", which was directly related to the land cover changes brought about by the topographic gradient effect in the Bay Area.3) Pollution control driven by environmental investments has had a moderating effect on habitat degradation, but it has not been able to change the overall degradation trend. 4) Scenario analysis suggests that future habitat quality in the GBA will degrade to a certain extent due to the impact of artificial surface expansion. We deduce that this will affect the structure of the city's ecological network as well as the conservation function of the ecological zones. This study provides a scientific basis for understanding the historical and future trends of habitat quality in the GBA, offering new insights into the intrinsic driving mechanisms of habitat quality. It also provides a theoretical support for relevant authorities to undertake sustainable development initiatives. 

Research on the evolution characteristics, driving mechanisms and multi-scenario simulation of habitat quality in the Guangdong-Hong Kong-Macao Greater Bay based on multi-model coupling

The spatial difference between urban and rural areas is the direct result of urban-rural relations. Accurate identification of urban-rural area is helpful to judge the urban-rural mechanism and promote the integration development of urban-rural area. Previous studies only used single nighttime light (NTL) data to identify urban and rural areas, which is likely to have an impact on the identification results due to the large brightness difference of lights. Therefore, based on NTL data and combine with data level fusion algorithm, this study separately fuses point of interest (POI) data that representing the quantity distribution of urban infrastructure and Baidu migration big (BM)data that representing the change relationship of regional population mobility to identify urban and rural areas by using deep learning method. The results show that the highest accuracy of urban-rural spatial identification with single NTL data is 84.32% and kappa is 0.6952, while the highest accuracy identified by data fusion is 95.02% and kappa is 0.8259. It can be seen that the differences caused by light brightness are effectively corrected after data fusion, which greatly improves the accuracy of urban and rural spatial identification. By comparing the results of NTL data modified by different big data, this study analyzes and identifies the accuracy of urban and rural area by using deep learning method, which not only enriches the study of data fusion in urban area, but also provides a basis for analyzing regional urban-rural relations and urban-rural development. Therefore, this study is believed to have important practical value for the coordinated development of urban and rural areas.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09874787.pdf

Using POI Data and Baidu Migration Big Data to Modify Nighttime Light Data to Identify Urban and Rural Area

Urbanization and climate change have led to rising urban temperatures, increasing heat-related health risks. Assessing urban heat risk is crucial for understanding and mitigating these risks. Many studies often overlook the impact of block types on heat risk, which limits the development of mitigation strategies during urban planning. This study aims to investigate the influence of various spatial factors on the heat risk at the block scale. Firstly, a GIS approach was used to generate a Local Climate Zones (LCZ) map, which represents different block types. Secondly, a heat risk assessment model was developed using hazard, exposure, and vulnerability indicators. Thirdly, the risk model was demonstrated in Guangzhou, a high-density city in China, to investigate the distribution of heat risk among different block types. An XGBoost model was used to analyze the impact of various urban spatial factors on heat risk. Results revealed significant variations in heat risk susceptibility among different block types. Specifically, 33.9% of LCZ 1–4 areas were classified as being at a high-risk level, while only 23.8% of LCZ 6–9 areas fell into this level. In addition, the pervious surface fraction (PSF) had the strongest influence on heat risk level, followed by the height of roughness elements (HRE), building surface fraction (BSF), and sky view factor (SVF). SVF and PSF had a negative impact on heat risk, while HRE and BSF had a positive effect. The heat risk assessment model provides valuable insights into the spatial characteristics of heat risk influenced by different urban morphologies. This study will assist in formulating reasonable risk mitigation measures at the planning level in the future. 

Quantifying the Influence of Different Block Types on the Urban Heat Risk in High-Density Cities

Towards SDG 11: Large-scale geographic and demographic characterisation of informal settlements fusing remote sensing, POI, and open geo-data

A novel urban population-land coupling simulation model: Integrating spatial equilibrium and macro-micro strategies

Siting transportation infrastructures such highway and highspeed railway connects cities and benefits the coordinate development in urban agglomerations. Previous infrastructure siting models rely on the expert experience or spatial-support decision using spatial data describing current conditions. The future development after the infrastructure construction is less considered. This study presents an alternative spatial cooperative simulation-based infrastructure siting model. The spatial cooperative scenario simulation model is developed to simulate the land, population, and economy variations and assess the benefits of the infrastructure construction. The experiment in the high-speed railway in Guangdong-Hongkong-Macao Greater Bay Area, China demonstrates the effectiveness of the presented model. The results provide valuable insights into the spatial-support siting decision for the new high-speed railway in the urban agglomerations.

Spatial cooperative scenario simulation for infrastructure siting in Guangdong-HongKong-Macao Greater Bay Area

How spatial scales enhance prediction: an interpretable multi-scale framework for bike-sharing demand prediction

Fast urbanization brings great challenges to sustainable development goals, such as excessive exploitation and population explosion. Classical cellular automata (CA) have been widely used to independently simulate the change of spatial features, i.e. land use, population, economic production, etc. However, most CA models rely on historical data as static driving factors to simulate future scenarios while ignoring the inter-wined influences among multiple features in the development process. To address this issue, this study proposes a spatial cooperative simulation (SCS) approach to simulate the land use, population, and economy changes. The SCS approach starts with a separate CA model to obtain the initial scenes of each feature. Then, the simulation results of each other two features are used as dynamically updated driving factors, rather than the static historical data, to capture the inter-wined influence of multiple features during the development process. This step is iteratively performed until the changes of each feature converge and the final simulation results will be reported. The simulation experiment in Greater Bay Area demonstrates that the SCS approach can well capture the simultaneous development process and outperforms baseline approaches. The SCS approach is capable of forecasting future development scenarios and facilitates spatial planning and infrastructure synergies.

Spatial cooperative simulation of land use-population-economy in the Greater Bay Area, China

Exploring geo-transferability of deep neural network by developing comprehensive metrics

Nighttime light (NTL) radiance can reflect human settlements and activities. A lot of studies indicate that NTL brightness can be a sound proxy of socioeconomic factors on large scales, such as the population size, gross domestic product, electric power consumption, etc. However, few studies have been dedicated to these topics on fine scales. In this study, we examined the correlation between the Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (S-NPP/VIIRS) NTL intensity (NLI) and population density, as well that between NLI and per capita income at two census units’ levels, census tract and block group, in two research areas. The result shows that the NLI has a moderate or weak positive correlation with the population density at both scales. However, when land use type is integrated with population density, the correlation becomes very strong. The NLI and per capita income have a weak or very weak negative correlation at both scales. Moreover, we find that the correlation coefficient is positively correlated with the unit’s size. The larger scale also has a higher correlation coefficient. The research conducted here could be beneficial for the application of S-NPP/VIIRS NTL data in studying socioeconomic activities in human settlement areas.

Correlation Analysis Between Nighttime Light Data and Socioeconomic Factors on Fine Scales

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