ISPRS Int. J. Geo-Inf., Volume 11, Issue 12 (December 2022) – 52 articles

Mapping slums is vital for monitoring the Sustainable Development Goal (SDG) indicators. In the absence of reliable data, Remote Sensing (RS)-based approaches, particularly the Deep Learning (DL) methods, have gained recognition and high accuracies for slum mapping. However, using RS alone has its limitation in complex urban environments. Previous studies showed the added value of combining ground-level information with RS. Therefore, this research aims to integrate Remote Sensing Imagery (RSI) and Street View Images (SVI) for slum mapping. Jakarta city is the study area representing the challenge of distinguishing between slum and non-slum kampungs, and these kampungs accommodate approximately 60% of the population of Jakarta. This research compares the mapping results obtained by four DL networks: FCN-DK6 used only RSI, a VGG16 used only SVI, and two networks combined RSI and SVI (FCN-DK6-i and Modified FCN-DK6). Further, the Modified FCN-DK6 network was explored by integrating SVI at each convolutional layer, i.e., Modified FCN-DK6_1, Modified FCN-DK6_2, Modified FCN-DK6_3, Modified FCN-DK6_4, and Modified FCN-DK6_5. Experimental results demonstrate that combining RSI and SVI improves the accuracy, depending on how and at what level in the FCN network they are integrated. The Modified FCN-DK6_2 outperforms the rest in Modified FCN-DK6 experiments and FCN-DK6-i. Full article

Visual comfort has a critical effect that significantly influences public appreciation of urban environments. Although colour is an integral part of billboard design, little empirical evidence exists to support some of the popularly held ideas about the effects of colour on task performance and human psychological wellbeing. Thus, attempting to set a threshold level of allowed undesirable visual stimuli in each urban setting is considered to be essential in achieving a satisfactory level of visual quality. Therefore, this research investigates the effects of colour content of outdoor advertisement billboards on the appreciation of urban scenes by the public. This research utilises pictorial survey, R.G.B bivariate histogram technique, and an areal cumulative analysis of a group of collected pictures within one of Kuala Lumpur’s high streets. Results of the pictorial survey are cross analysed against the results of the pictorial RGB content analysis and pictorial outdoor advertisement (OA) cumulative areal analysis to indicated a strong correlation between environmental colour content, OAs’ cumulative area, and visual comfort. The study suggests that the lack of guidelines and regulations of the color content of outdoor billboard advertisement design could potentially be detrimental for the public’s appreciation of urban environments. Future research initiatives are encouraged to develop a visual quality assessment framework that contributes to the image and identity of the city of Kuala Lumpur. Full article

With the rapid development of web service technology, automatic land cover web service composition has become one of the key challenges in solving complex geoprocessing tasks of land cover. Service composition requires the creation of service chains based on semantic information about the services and all the constraints that should be respected. Artificial intelligence (AI) planning algorithms have recently significantly progressed in solving web service composition problems. However, the current approaches lack effective constraints to guarantee the accuracy of automatic land cover service composition. To address this challenge, the paper proposes a domain constraints-driven automatic service composition approach for online land cover geoprocessing. First, a land cover service ontology was built to semantically describe land cover tasks, data, and services, which assist in constructing domain constraints. Then, a constraint-aware GraphPlan algorithm was proposed, which constructs a service planning graph and searches services based on the domain constraints for generating optimal web service composition solutions. In this paper, the above method was integrated into a web prototype system and a case study for the online change detection automatic geoprocessing was implemented to test the accuracy of the method. The experimental results show that with this method, a land cover service chain can generate automatically by user desire objective and domain constraints, and the service chain execution result is more accurate. Full article

Urban street space is a critical reflection of a city’s vitality and image and a critical component of urban planning. While visual perceptual information about an urban street space can reflect the composition of place elements and spatial relationships, it lacks a unified and comprehensive quantification system. It is frequently presented in the form of element proportions without accounting for realistic factors, such as occlusion, light and shadow, and materials, making it difficult for the data to accurately describe the complex information found in real scenes. The conclusions of related studies are insufficiently focused to serve as a guide for designing solutions, remaining merely theoretical paradigms. As such, this study employed semantic segmentation and information entropy models to generate four visual perceptual information quantity (VPIQ) measures of street space: (1) form; (2) line; (3) texture; and (4) color. Then, at the macro level, the streetscape coefficient of variation (SCV) and K-means cluster entropy (HCK) were proposed to quantify the street’s spatial variation characteristics based on VPIQ. Additionally, we used geographically weighted regression (GWR) to investigate the relationship between VPIQ and street elements at the meso level as well as its practical application. This method can accurately and objectively describe and detect the current state of street spaces, assisting urban planners and decision-makers in making decisions about planning policies, urban regeneration schemes, and how to manage the street environment. Full article

Among the most prevalent natural hazards, flooding has been threatening human lives and properties. Robust flood simulation is required for effective response and prevention. Machine learning is widely used in flood modeling due to its high performance and scalability. Nonetheless, data pre-processing of heterogeneous sources can be cumbersome, and traditional data processing and modeling have been limited to a single resolution. This study employed an Icosahedral Snyder Equal Area Aperture 3 Hexagonal Discrete Global Grid System (ISEA3H DGGS) as a scalable, standard spatial framework for computation, integration, and analysis of multi-source geospatial data. We managed to incorporate external machine learning algorithms with a DGGS-based data framework, and project future flood risks under multiple climate change scenarios for southern New Brunswick, Canada. A total of 32 explanatory factors including topographical, hydrological, geomorphic, meteorological, and anthropogenic were investigated. Results showed that low elevation and proximity to permanent waterbodies were primary factors of flooding events, and rising spring temperatures can increase flood risk. Flooding extent was predicted to occupy 135–203% of the 2019 flood area, one of the most recent major flooding events, by the year 2100. Our results assisted in understanding the potential impact of climate change on flood risk, and indicated the feasibility of DGGS as the standard data fabric for heterogeneous data integration and incorporated in multi-scale data mining. Full article

In China’s fourteenth five-year plan, urban regeneration has become one of the most crucial strategies for activating the existing cities. Since creating vibrant urban spaces is a critical component of urban regeneration, understanding the patterns of community vitality helps formulate reactive regeneration policies and design interventions. However, the lack of local-scale measurement criteria and data collection methods has posed significant constraints to assessing and rejuvenating community vitality. Taking Suzhou Nanhuan New Village as a study area, our research involved a comparative study approach to investigate the fundamental driving mechanism of urban vitality with the support of a theoretical model (5D theory), multi-source data input, real-time photography technologies, and statistical analysis tools (Analytic Hierarchy Process). The result shows at the community level, the original ‘3d’ dimensions (‘Density’, ‘Diversity’, ‘Design’) remain key elements for forming vibrant spatial quality and functionality, and density factors matter significantly. This study intends to provide a new paradigm for small-scale community vitality assessment, verification, and regeneration by combining urban morphology with people-oriented and environmental-oriented perspectives. This research could support quantitative research on creating vibrant high-density communities in the urban regeneration process and bring insights to academics and design practitioners. Full article

The recommendation system is one of the hotspots in the field of artificial intelligence that can be applied to recommend suitable ecological patterns for the countryside. Countryside ecological patterns mean advanced patterns that can be recommended to those developing areas which have similar geographical features, which provides huge benefits for countryside development. However, current recommendation methods have low recommendation accuracy due to some limitations, such as data-sparse and ‘cold start’, since they do not consider the complex geographical features. To address the above issues, we propose a geographical Knowledge Graph Convolutional Networks method for Countryside Ecological Patterns Recommendation (KGCN4CEPR). Specifically, a geographical knowledge graph of countryside ecological patterns is established first, which makes up for the sparsity of countryside ecological pattern data. Then, a convolutional network for mining the geographical similarity of ecological patterns is designed among adjacent countryside, which effectively solves the ‘cold start’ problem in the existing recommended methods. The experimental results show that our KGCN4CEPR method is suitable for recommending countryside ecological patterns. Moreover, the proposed KGCN4CEPR method achieves the best recommendation accuracy (60%), which is 9% higher than the MKR method and 6% higher than the RippleNet method. Full article

As a representative indicator for the level and sustainability of urban development, urban vitality has been widely used to assess the quality of urban development. However, urban vitality is too blurry to be accurately quantified and is often limited to a particular type of expression of vitality. Current regression models often fail to accurately express the spatial heterogeneity of vibrancy and drivers. Therefore, this paper took Nanjing as the study area and quantified the social, cultural, and economic vitality indicators based on mobile phone data, POI data, and night-light remote sensing data. We also mapped the spatial distribution of comprehensive urban vitality using an improved entropy method and analyzed the spatial heterogeneity of urban vitality and its influencing factors using a plot boundary-based neural network weighted regression (PBNNWR). The results show: (1) The comprehensive vitality in Nanjing is distributed in a “three-center” pattern with one large and two small centers; (2) PBNNWR can be used to investigate the local regression relationships among the driving factors and urban vitality, and the fitting accuracy (95.6%) of comprehensive vitality in weekdays is higher than that of ordinary least squares regression (OLS) (65.9%), geographically weighted regression (GWR) (89.9%), and geographic neural network weighted regression (GNNWR) (89.5%) models; (3) House price, functional diversity, building density, metro station accessibility, and residential facility density are factors that significantly affect urban vitality. The study’s findings can provide theoretical guidance for optimizing the urban spatial layout, resource allocation, and targeted planning strategies for areas with different vitality values. Full article

TOD (transit-oriented development) is a planning concept that uses public transportation stations as the center of development, and it aims to integrate land use efficiency and transportation planning linkages to encourage the use of public transportation. The impact of metro TOD projects on urban transportation is multifaceted and complex, and the promotion of metro TOD ridership is an important topic in academic circles. However, the theoretical analysis framework of the impact mechanism of metro TOD ridership is still not perfect. Most studies ignore the TOD characteristics of the stations and the interaction between the station area’s land use and the station area functional linkage. Moreover, a few studies have focused on the mechanisms of the impact of TOD built environment factors on the spatial differentiation of station ridership, and the interactive effects of built environment factors. In this paper, the factors of a metro TOD station built environment were selected based on the node–place–linkage model expanded by the 5D principle of TOD, and a solution is provided for the computable transformation of the 5D principle. The GeoDetector method was used to detect the individual and interactive effects of the TOD built environment factors. The results show that the spatial distribution of the metro TOD station area ridership shows a core–peripheral structure and spatial heterogeneity, both on weekdays and weekends. Moreover, the individual effects of each factor can explain up to 49% and 35% of the traffic distribution on weekdays and weekends, respectively. In addition, the two-factor interactive effect has a stronger influence on metro ridership. The interactive effect can explain up to 72% and 77% of the traffic distribution on weekdays and weekends, respectively. Furthermore, the individual effects of each factor exhibited spatial heterogeneity in the local spaces, showing spatial facilitation and inhibition, respectively. Finally, the main policy recommendations are as follows: One of the important ways to guide the development of cities toward polycentric structure is to promote a TOD model in the peripheral areas of the cities. Building more public open spaces in TOD station areas and improving the collection and distribution capacity of the bus transport systems can effectively stimulate the ridership of metro stations. Full article

Urban spatial form is a significant reference to getting to know cities and running the cities. The fractal theory is an effective means to quantify urban spatial form. Taking the buildings in the outer ring of Zhengzhou City as the research object, the basic architectural models are built by extracting their forms. The research site is subdivided into 199 regions. The distribution of architectural forms in Zhengzhou is analyzed by fractal theory and spatial autocorrelation from the perspective of two-dimensional(2D) and three-dimensional(3D). The results indicate that the architectural layout of Zhengzhou has distinct fractal characteristics; Both global spatial autocorrelation and local spatial autocorrelation show significant positive correlations; There are obvious spatial differences in architectural space forms in different regions. The refined grid analysis strengthens the understanding of the urban spatial structure and development rules in more detail. The study promotes the refinement and visualization of fractal theory effectively and improves the depth of urban spatial form cognition. Full article

Trajectory big data is suitable for distributed storage retrieval due to its fast update speed and huge data volume, but currently there are problems such as hot data writing, storage skew, high I/O overhead and slow retrieval speed. In order to solve the above problems, this paper proposes a trajectory big data model that incorporates data partitioning and spatio-temporal multi-perspective hierarchical organization. At the spatial level, the model partitions the trajectory data based on the Hilbert curve and combines the pre-partitioning mechanism to solve the problems of hot writing and storage skewing of the distributed database HBase; at the temporal level, the model takes days as the organizational unit, finely encodes them into a minute system and then fuses the data partitioning to build spatio-temporal hybrid encoding to hierarchically organize the trajectory data and solve the problems of efficient storage and retrieval of trajectory data. The experimental results show that the model can effectively improve the storage and retrieval speed of trajectory big data under different orders of magnitude, while ensuring relatively stable writing and query speed, which can provide an efficient data model for trajectory big data mining and analysis. Full article

Geographically neural network weighted regression is an improved model of GWR combined with a neural network. It has a stronger ability to fit nonlinear functions, and complex geographical processes can be modeled more fully. GNNWR uses the distance metric of Euclidean space to express the relationship between sample points. However, except for spatial location features, geographic entities also have many diverse attribute features. Incorporating attribute features into the modeling process can make the model more suitable for the real geographical process. Therefore, we proposed a spatial-attribute proximities deep neural network to aggregate data from the spatial feature and attribute feature, so that one unified distance metric can be used to express the spatial and attribute relationships between sample points at the same time. Based on GNNWR, we designed a spatial and attribute neural network weighted regression (SANNWR) model to adapt to this new unified distance metric. We developed one case study to examine the effectiveness of SANNWR. We used PM2.5 concentration data in China as the research object and compared the prediction accuracy between GWR, GNNWR and SANNWR. The results showed that the “spatial-attribute” unified distance metric is useful, and that the SANNWR model showed the best performance. Full article

Periodic traffic prediction and analysis is essential for urbanisation and intelligent transportation systems (ITS). However, traffic prediction is challenging due to the nonlinear flow of traffic and its interdependencies on spatiotemporal features. Traffic flow has a long-term dependence on temporal features and a short-term dependence on local and global spatial features. It is strongly influenced by external factors such as weather and points of interest. Existing models consider long-term and short-term predictions in Euclidean space. In this paper, we design an attention-based encoder–decoder with stacked layers of LSTM to analyse multiscale spatiotemporal dependencies in non-Euclidean space to forecast traffic. The attention weights are obtained adaptively and external factors are fused with the output of the decoder to evaluate region-wide traffic predictions. Extensive experiments are conducted to evaluate the performance of the proposed attention-based non-Euclidean spatiotemporal network (ANST) on real-world datasets. The proposed model has improved prediction accuracy over previous methods. The insights obtained from traffic prediction would be beneficial for daily commutation and logistics. Full article

Most fabrication methods for three-dimensional (3D) geological symbols are limited to two types: directly increasing the dimensionality of a 2D geological symbol or performing appropriate modeling for an actual 3D geological situation. The former can express limited vertical information and only applies to the three-dimensional symbol-making of point mineral symbols, while the latter weakens the difference between 3D symbols and 3D geological models and has several disadvantages, such as high dependence on measured data, redundant 3D symbol information, and low efficiency when displayed in a 3D scene. Generating a 3D geological symbol is represented by the process of constructing a 3D geological model. This study proposes a parametric modeling method for 3D fold symbols according to the complexity and diversity of the fold structures. The method involves: (1) obtaining the location of each cross-section in the symbol model, based on the location parameters; (2) constructing the middle cross-section, based on morphological parameters and the Bezier curve; (3) performing affine transformation according to the morphology of the hinge zone and the middle section to generate the sections at both ends of the fold; (4) generating transition sections of the 3D symbol model, based on morphing interpolation; and (5) connecting the point sets of each transition section and stitching them to obtain a 3D fold-symbol model. Case studies for different typical fold structures show that this method can eliminate excessive dependence on geological survey data in the modeling process and realize efficient, intuitive, and abstract 3D symbol modeling of fold structures based on only a few parameters. This method also applies to the 3D geological symbol modeling of faults, joints, intrusions, and other geological structures and 3D geological modeling of typical geological structures with a relatively simple spatial morphology. Full article

The outbreak of the Delta Variant of COVID-19 presents a natural experiment without modern precedent. As authorities scrambled to control the spread of the disease in Australia’s largest cities, construction workers were allowed to keep working on site without the benefit of mandatory vaccination, unlike their peers in healthcare, defense, education or aviation. Using publicly available COVID-19 surveillance data, we analyzed the geographic spread of the Delta Variant and its relationship with construction in both cities. The period of this study covers the identification of the first case of community transmission to the achievement of 90% full vaccination in the eligible population. We show how the risk profile of construction workers varies according to socio-economic status such that Machinery Operators and Drivers were most at risk, followed by Laborers, owing to where they tend to live in each city. Moreover, these highly mobile workers may unknowingly serve as vectors for the spread of infectious disease to the most vulnerable communities in an urban setting. Remarkably, we also found that the risk profile of construction businesses can also be described similarly in terms of annual income. Sole traders and small businesses were mostly located in vulnerable areas, which presents threats to business continuity that public policy must address. We observed that the first eight weeks of an outbreak are critical; after this time, vulnerable workers and most construction businesses will see steep rises in their exposure to the risk of infection until the disease is brought under control. Accordingly, we recommend short, sharp pauses of all construction works on site to control the spread of future pandemic outbreaks once cases of community transmission are detected. Fiscal policy must support workers and small business owners, so they are not forced to choose between their health and earning a living during these periods. The government and trade unions must commit to mandatory vaccination for construction workers to safeguard their communities. Health authorities must continuously engage with particularly vulnerable workers as immunity wanes and vaccine boosters become necessary. Digital disinformation must be tirelessly countered by consistent expert medical advice at all levels of the industry. Full article

Urban parks play an essential role in mitigating the effects of air pollution on human health in a healthy city construction process. However, due to the data limitations, little is known about the spatial distribution of real-time expressed air pollution-related health (APRH) across different urban parks and the contribution of the associated factors. To fill this research gap, this research was conducted based on social media Weibo data (Chinese Twitter) and other geographical data using semantic analyses and the Geo-Detector method by taking 169 urban parks in Beijing as the study area. The results showed that there were more Weibo items relating to APRH clustered within the third ring road and decreasing outward along the ring road. A total of 16 factors in three categories were introduced to analyze the driving forces of this spatial distribution. Accessibility was outstanding with a q-value of the number of subway stations (X14) as high as 0.79, followed by built environment and finally park attributes. Distinguished from those reports based on the traditional statistical data, this research demonstrated that although the urban parks improved the APRH, the exposure to air pollution also increased the health risks when visiting the urban park. It also provides a geographical understanding of the urban parks’ effect on APRH and theoretical guidance for urban park planning and construction. Full article

The ambient population has been regarded as an important indicator for analyzing or predicting thefts. However, the literature has taken it as a homogenous group and seldom explored the varied impacts of different kinds of ambient populations on thefts. To fill this gap, supported by mobile phone trajectory data, this research investigated the relationship between ambient populations of different social groups and theft in a major city in China. With the control variables of motivated offenders and guardianship, spatial-lag negative binominal models were built to explore the effects of the ambient populations of different social groups on the distribution of theft. The results found that the influences of ambient populations of different social groups on the spatial distribution of theft are different. Accounting for the difference in the “risk–benefit” characteristics among different activity groups to the offenders, individuals from the migrant population are the most likely to be potential victims, followed by suburban and middle-income groups, while college, affluent, and affordable housing populations are the least likely. The local elderly population had no significant impact. This research has further enriched the studies of time geography and deepened routine activity theory. It suggests that the focus of crime prevention and control strategies developed by police departments should shift from the residential space to the activity space. Full article

This study examined the urban structure of colonial Seoul in the 1930s, the capital city of Korea under the rule of the Japanese empire, by adopting quantitative geographical methods. We utilized a job accessibility index to operationalize the urban structure. We also used geographic information science (GIScience) analysis tools to digitize neighborhood-level sociodemographic and parcel-level business location information from historical materials. The results illustrated several findings that were not revealed by previous studies based on qualitative approaches. First, transit-based job accessibility (13.392) is significantly higher (p < 0.001) than walk-based job accessibility (10.575). Second, there is a Γ-shaped area with higher job accessibility, including the central part of colonial Seoul. Third, Japanese-dominant neighborhoods had significantly (p < 0.001) higher transit-based (27.156) job accessibility than Korean-dominant neighborhoods (9.319). Fourth, transit-based job accessibility is not significantly correlated with the unemployment rate overall. Although colonial Seoul was the seventh-largest city of the Japanese empire, few practical planning actions were taken to resolve urban issues, unlike the other large cities in mainland Japan. Full article

Protecting residents’ health and improving equality are important goals of the United Nations Sustainable Development Goals. The recent outbreak of COVID-19 has placed a heavy burden on the medical systems of many countries and been disastrous for the low-income population of the world, which has further increased economic, health, and lifelong inequality in society. One way to improve the population’s health is to equalize basic medical services. A scientific evaluation of the status quo or the equalization of basic medical services (EBMS) is the basic prerequisite and an important basis for realizing the equitable allocation of medical resources. Traditional evaluation methods ignore the spatial characteristics of medical services, mostly using the indicator of equal weight evaluation, which restricts the objectivity of the evaluation results. Given this, this research proposes a set of EBMS evaluation methods from a spatial perspective and takes the Xinjiang Uygur Autonomous Region of China (Xinjiang) as an example for studying the status quo of EBMS. This study puts forward a set of EBMS evaluation methods from a geospatial perspective and makes full use of spatial analysis and information theory techniques to construct a two-level evaluation indicator that takes into account the spatial characteristics of EBMS. The entropy weight method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method have been used to reveal the current status quo of EBMS in Xinjiang to objectively reflect the differences in EBMS. When using the entropy and TOPSIS methods, the evaluation is always based on the data so that the results can more objectively reveal the medical resources available to the residents. Therefore, the government can realize a reasonable allocation of medical resources. Full article

Timely mapping of flooded areas is critical to several emergency management tasks including response and recovery activities. In fact, flood crisis maps embed key information for an effective response to the natural disaster by delineating its spatial extent and impact. Crisis mapping is usually carried out by leveraging data provided by satellite or airborne optical and radar sensors. However, the processing of these kinds of data demands experienced visual interpretation in order to achieve reliable results. Furthermore, the availability of in situ observations is crucial for the production and validation of crisis maps. In this context, a frontier challenge consists in the use of Volunteered Geographic Information (VGI) as a complementary in situ data source. This paper proposes a procedure for flood mapping that integrates VGI and optical satellite imagery while requiring limited user intervention. The procedure relies on the classification of multispectral images by exploiting VGI for the semi-automatic selection of training samples. The workflow has been tested with photographs and videos shared on social media (Twitter, Flickr, and YouTube) during two flood events and classification consistency with reference products shows promising results (with Overall Accuracy ranging from 87% to 93%). Considering the limitations of social media-sourced photos, the use of QField is proposed as a dedicated application to collect metadata needed for the image classification. The research results show that the integration of high-quality VGI data and semi-automatic data processing can be beneficial for crisis map production and validation, supporting crisis management with up-to-date maps. Full article

Urban-scale green spaces have been a central topic as of late, but community-scale green spaces are overlooked in urban studies. This paper takes community green spaces in the main urban area of Beijing as the case to quantitatively interpret the spatial-temporal patterns of their service efficiency and distribution characteristics. The measurement section of the paper includes two parts: the first part compares the applicability of two major green space service efficiency measurement methods on the community scale and determines that the Shortest Time Distance method performs better in describing the spatial-temporal patterns of service efficiency. The second part applies the Time Distance Entropy method to initially identify the locational relationship between community green spaces and neighboring residential buildings, then proposes the Green Space Distribution Coefficient method based on this relationship to analyze the ‘courtyard’, ‘mixed’, and ‘centralized’ distribution types alongside the transition relationships between them, and the spatial-temporal patterns of distribution characteristics are measured. The results of service efficiency reveal that the community paradigms transform from ‘humanistic-oriented’ to ‘benefit-oriented’ as the Shortest Time Distance measurement values show an ascending trend with the passage of years and the outward expansion of the ring roads. The results of distribution characteristics reveal that the community residential culture transforms from ‘closeness’ to ‘detachment’ as Green Space Distribution Coefficient measurement values show a descending trend under the same conditions. Based on the measurements, this paper further provides several optimizing strategies for community green spaces in the central urban area of Beijing. Full article

Collapse is a common cartographic generalization operation in multi-scale representation and cascade updating of vector spatial data. During transformation from large- to small-scale, the dual-line river shows progressive collapse from narrow river segment to line. The demand for vector spatial data with various scales is increasing; however, research on the progressive collapse of dual-line rivers is lacking. Therefore, we proposed a progressive collapse method based on vector spatial data. First, based on the skeleton graph of the dual-line river, the narrow and normal river segments are preliminarily segmented by calculating the width of the river. Second, combined with the rules of cartographic generalization, the collapse and exaggeration priority strategies are formulated to determine the handling mode of the river segment. Finally, based on the two strategies, progressive collapse of dual-line rivers is realized by collapse and exaggeration of the river segment. Experimental results demonstrated that the progressive collapse results of the proposed method were scale-driven, and the collapse part had no burr and topology problems, whereas the remaining part was clearly visible. The proposed method can be better applied to progressive collapse of the dual-line river through qualitative and quantitative evaluation with another progressive collapse method. Full article

Tiled maps are one of the key GIS technologies used in the development and construction of WebGIS in the era of big data; there is an urgent need for high-performance tile map services hosted on big data GIS platforms. To address the current inefficiency of massive tile map data management and access, this paper proposes a massive tile map data access model that utilizes the jump consistent hash algorithm. Via the uniformity and consistency of a certain seed of a pseudo-random function, the algorithm can generate a storage slot for each tile data efficiently. By recording the slot information in the head of a row key, a uniform distribution of the tiles on the physical cluster nodes is achieved. This effectively solves the problem of hotspotting caused by the monotonicity of tile row keys in the data access process, thereby maximizing the random-access performance of a big data platform and greatly improving concurrent database access. Experiments show that this model can significantly improve the efficiency of tile map data access by more than 39% compared to a direct storage method, thereby confirming the model’s advantages in accessing massive tile map data on a big data GIS platform. Full article

Generating differentially private synthetic human mobility trajectories from real trajectories is a commonly used approach for privacy-preserving trajectory publishing. However, existing synthetic trajectory generation methods suffer from the drawbacks of poor scalability and suboptimal privacy–utility trade-off, due to continuous spatial space, high dimentionality of trajectory data and the suboptimal noise addition mechanism. To overcome the drawbacks, we propose DP-CSM, a novel differentially private trajectory generation method using coreset clustering and the staircase mechanism, to generate differentially private synthetic trajectories in two main steps. Firstly, it generates generalized locations for each timestamp, and utilizes coreset-based clustering to improve scalability. Secondly, it reconstructs synthetic trajectories with the generalized locations, and uses the staircase mechanism to avoid the over-perturbation of noises and maintain utility of synthetic trajectories. We choose three state-of-the-art clustering-based generation methods as the comparative baselines, and conduct comprehensive experiments on three real-world datasets to evaluate the performance of DP-CSM. Experimental results show that DP-CSM achieves better privacy–utility trade-off than the three baselines, and significantly outperforms the three baselines in terms of efficiency. Full article

Climate change, population growth and urban sprawl have put a strain on water supplies across the world, making it difficult to meet water demand, especially in city regions where more than half of the world’s population now reside. Due to the complex urban fabric, conventional techniques should be developed to diagnose water shortage risk (WSR) by engaging crowdsourcing. This study aims to develop a novel approach based on public participation (PP) with a geographic information system coupled with machine learning (ML) in the urban water domain. The approach was used to detect (WSR) in two ways, namely, prediction using ML models directly and using the weighted linear combination (WLC) function in GIS. Five types of ML algorithm, namely, support vector machine (SVM), multilayer perceptron, K-nearest neighbour, random forest and naïve Bayes, were incorporated for this purpose. The Shapley additive explanation model was added to analyse the results. The Water Evolution and Planning system was also used to predict unmet water demand as a relevant criterion, which was aggregated with other criteria. The five algorithms that were used in this work indicated that diagnosing WSR using PP achieved good-to-perfect accuracy. In addition, the findings of the prediction process achieved high accuracy in the two proposed techniques. However, the weights of relevant criteria that were extracted by SVM achieved higher accuracy than the weights of the other four models. Furthermore, the average weights of the five models that were applied in the WLC technique increased the prediction accuracy of WSR. Although the uncertainty ratio was associated with the results, the novel approach interpreted the results clearly, supporting decision makers in the proactive exploration processes of urban WSR, to choose the appropriate alternatives at the right time. Full article

Route choice is a complex issue in simulating individual behaviors and reproducing collective phenomena during evacuations. A growing concern has been given to the individual cognitive mechanism to investigate how routing decisions are made in specific situations. However, the essential role of multiple spatio-temporal scales has not been completely considered in the current cognitive frameworks, which leads to the inaccuracy of cognition representation in evacuation decisions. This study proposes a novel spatio-temporal cognitive framework integrated with multiple spatio-temporal scales for individual route choice. First, a complete spatio-temporal cognitive mechanism is constructed to depict the individual evacuation cognition process. Second, a spatio-temporal route choice strategy that emerges from agent-based simulation and extends into the spatio-temporal potential field is designed to represent the overall time-varying cost along routes in individual subjective estimation. Finally, a spatio-temporal A* algorithm is developed for individual optimal route planning in complex outdoor evacuation scenarios. The experimental results show that the proposed framework outperformed the conventional potential field model in evacuation performance, in both objective crowd evacuation evaluation metrics and individual subjectively estimated evacuation cost in cognition, and may provide more insights on crowd evacuation management and guidance. Full article

The development of high nature value farmland (HNVf) can effectively improve the problems of biodiversity reduction, non-point source pollution and carbon loss in intensive farmland. To this end, we developed a set of general indicators based on Landsat 8 OLI imagery, including land cover (LC), normalized difference vegetation index (NDVI), Shannon diversity (SH) and Simpson’s index (SI). Combined with a Kohonen neural network (KNN), we assigned weights and developed the first potential HNVf map of the Yellow River Delta in China. The results showed that the four indicators were very effective for the expression of HNVf characteristics in the study area, and that SH and SI, in particular, could reflect the potential characteristics of HNVf at the edge of intensive farmland. LC, NDVI, SH and SI were weighted as 0.45, 0.25, 0.15 and 0.15, respectively. It was found that the potential HNVf type 2 (i.e., low-intensity agriculture, and natural and structural elements such as shrubs, woodlands and small rivers) in the study area was concentrated at the edges of intensive farmland, the transition zones from farmland to rivers and the estuary wetland areas of northern and eastern rivers. LC played a leading role in identifying HNVf. Based on six randomly selected real-world verification data from Map World, it was found that the accuracy of the validation set for HNVf type 2 was 83.33%, which exhibited the good development potential of HNVf in the study area. This is the first potential HNVf type 2 map of the Yellow River Delta in China and could provide a great deal of potential guidance for the development and protection of farmland biodiversity and regional carbon sequestration. Full article

One of the most critical processes for the long-term management of groundwater resources is Groundwater Potential Zonation (GWPZ). Despite their importance, traditional groundwater studies are costly, difficult, complex, and time-consuming. This study aims to investigate GWPZ mapping for the Al-Qalamoun region, in the Western part of Syria. We combined the Multi-Influence Factor (MIF) and Analytic Hierarchy Process (AHP) methods with the Geographic Information Systems (GIS) to estimate the GWPZ. The weight and score factors of eight factors were used to develop the GWPZ including drainage density, lithology, slope, lineament density, geomorphology, land use/land cover, rainfall, and soil. According to the findings, about 46% and 50.6% of the total area of the Al-Qalamoun region was classified as suitable for groundwater recharge by the AHP and MIF methods, respectively. However, 54% and 49.4% of the area was classified as having poor suitability for groundwater recharge by the AHP and MIF methods, respectively. These areas with poor suitability can be utilized for gathering surface water. The validation of the results showed that the AHP and MIF methods have similar accuracy for the GWPZ; however, the accuracy and results depend on influencing factors and their weights assigned by experts. Full article

One hundred seventeen landslides occurred in Malang Regency throughout 2021, triggering the need for practical hazard assessments to strengthen the disaster mitigation process. In terms of providing a solution for investigating the location of landslides more precisely, this research aims to compare machine learning algorithms to produce an accurate landslide susceptibility model. This research applies three machine learning algorithms composed of RF (random forest), NB (naïve Bayes), and KNN (k-nearest neighbor) and 12 conditioning factors. The conditioning factors consist of slope, elevation, aspect, NDVI, geological type, soil type, distance from the fault, distance from the river, river density, TWI, land cover, and annual rainfall. This research performs seven models over three ratios between the training and testing dataset encompassing 50:50, 60:40, and 70:30 for KNN and NB algorithms and 70:30 for the RF algorithm. This research measures the performance of each model using eight parameters (ROC, AUC, ACC, SN, SP, BA, GM, CK, and MCC). The results indicate that RF 70:30 generates the best performance, witnessed by the evaluation parameters ACC (0.884), SN (0.765), GM (0.863), BA (0.857), CK (0.749), MCC (0.876), and AUC (0.943). Overall, seven models have reasonably good accuracy, ranging between 0.806 and 0.884. Furthermore, based on the best model, the study area is dominated by high susceptibility with an area coverage of 51%, which occurs in the areas with high slopes. This research is expected to improve the quality of landslide susceptibility maps in the study area as a foundation for mitigation planning. Furthermore, it can provide recommendations for further research in splitting ratio scenarios between training and testing data. Full article