ISPRS Int. J. Geo-Inf., Volume 12, Issue 8 (August 2023) – 49 articles

The comprehensive and accurate assessment of the indoor air quality (IAQ) in large spaces, such as offices or multipurpose facilities, is essential for IAQ management. It is widely recognized that various IAQ factors affect the well-being, health, and productivity of indoor occupants. In indoor environments, it is important to assess the IAQ in places where it is difficult to install sensors due to space constraints. Spatial interpolation is a technique that uses sample values of known points to predict the values of other unknown points. Unlike in outdoor environments, spatial interpolation is difficult in large indoor spaces due to various constraints, such as being separated into rooms by walls or having facilities such as air conditioners or heaters installed. Therefore, it is necessary to identify independent or related regions in indoor spaces and to utilize them for spatial interpolation. In this paper, we propose a spatial interpolation technique that groups points with similar characteristics in indoor spaces and utilizes the characteristics of these groups for spatial interpolation. We integrated the IAQ data collected from multiple locations within an office space and subsequently conducted a comparative experiment to assess the accuracy of our proposed method in comparison to commonly used approaches, such as inverse distance weighting (IDW), kriging, natural neighbor interpolation, and the radial basis function (RBF). Additionally, we performed experiments using the publicly available Intel Lab dataset. The experimental results demonstrate that our proposed scheme outperformed the existing methods. The experimental results show that the proposed method was able to obtain better predictions by reflecting the characteristics of regions with similar characteristics within the indoor space. Full article

Traffic prediction plays a significant part in creating intelligent cities such as traffic management, urban computing, and public safety. Nevertheless, the complex spatio-temporal linkages and dynamically shifting patterns make it somewhat challenging. Existing mainstream traffic prediction approaches heavily rely on graph convolutional networks and sequence prediction methods to extract complicated spatio-temporal patterns statically. However, they neglect to account for dynamic underlying correlations and thus fail to produce satisfactory prediction results. Therefore, we propose a novel Self-Adaptive Spatio-Temporal Graph Convolutional Network (SASTGCN) for traffic prediction. A self-adaptive calibrator, a spatio-temporal feature extractor, and a predictor comprise the bulk of the framework. To extract the distribution bias of the input in the self-adaptive calibrator, we employ a self-supervisor made of an encoder–decoder structure. The concatenation of the bias and the original characteristics are provided as input to the spatio-temporal feature extractor, which leverages a transformer and graph convolution structures to learn the spatio-temporal pattern, and then applies a predictor to produce the final prediction. Extensive trials on two public traffic prediction datasets (METR-LA and PEMS-BAY) demonstrate that SASTGCN surpasses the most recent techniques in several metrics. Full article

Historical maps represent a unique and irreplaceable source of information about the history of a country, be it large (historical) regions, individual geomorphological units or specifically defined sites. Using a methodologically correct, critical historical analysis, old maps provide both the horizontal and vertical analysis of a landscape and its transformation in different time periods. These maps represent some of the oldest, but relatively easily accessible, historical pictorial documents (plausibly) depicting historical landscapes. This study provides the methodology for processing and georeferencing old mine maps with the possibility of their further use for the purposes of mining tourism. The 1696 Marsigli mine map has been chosen for the case study in question. It depicts a cross-section of the copper mines in Smolník and shows in detail the process of cementation water mining. Through an analysis and a detailed study, two-dimensional parts of a georeferenced historical map have been plotted in Google Earth’s three-dimensional space. Full article

Using a people-centered approach to new urbanization, China has committed to building high-quality living environments through improving urban livability and promoting a stronger sense of belonging among residents. Retail stores serve as one of the most immediate and accessible destinations for residents’ consumption, and their spatial configuration has a direct impact on residents’ satisfaction and happiness in their daily lives. In this context, for the present study we selected the central urban area of Lanzhou City as the case study area. Based on POI data and using the daily life circle as the basic unit, we applied methods such as kernel density analysis, hotspot analysis, and the Shannon–Weaver index to analyze spatial distribution patterns of the retail industry. Furthermore, we applied Geodetector to analyze the impacts of four factors that are closely related to the retail industry: economic level, convenience level, market demand, and location. The conclusions are as follows: In the central urban area of Lanzhou, the retail industry exhibits a belt distribution pattern along the Yellow River. The density of distribution gradually decreases from the city center toward the outskirts, forming four prominent agglomeration centers. Overall, within the central urban area of Lanzhou, the spatial distribution of the retail industry at the scale of daily living circles shows that only a small proportion of the industry demonstrates noticeable clustering effects. In terms of spatial patterns, the retail industry at the scale of the daily living circles demonstrates similar characteristics in terms of diversity and agglomeration distribution. It exhibits a decreasing trend from the urban core toward the peripheral areas. The agglomeration distribution pattern of the retail industry in the central urban area of Lanzhou is considerably influenced by market demand, economic level, convenience, and location. The spatial distribution of the retail industry in the central urban area is primarily influenced by economic factors and convenience, while market demand plays a major role and location has a relatively minimal impact. Full article

With rapid urbanization, urban functional zones have become important for rational government and resource allocation. Points of interest (POIs), as informative and open-access data, have been widely used in studies of urban functions. However, most existing studies have failed to address unevenly or sparsely distributed POIs. In addition, the spatial adjacency of analysis units has been ignored. Therefore, we propose a new method for identifying urban functional zones based on POI density and marginalized graph autoencoder (MGAE). First, kernel density analysis was utilized to obtain the POI density and spread the effects of POIs to the surroundings, which enhanced the data from unevenly or sparsely distributed POIs considering the barrier effects of main roads and rivers. Second, MGAE performed feature extraction in view of the spatial adjacency to integrate features from the POIs of the surrounding units. Finally, the k-means algorithm was used to cluster units into zones, and semantic recognition was applied to identify the function category of each zone. A case study of Changzhou indicates that this method achieved an overall accuracy of 90.33% with a kappa coefficient of 0.88, which constitutes considerable improvement over that of conventional methods and can improve the performance of urban function identification. Full article

Accurate land use and land cover (LULC) mapping is essential for scientific and decision-making purposes. The objective of this paper was to map LULC classes in the northern region of Mozambique between 2011 and 2020 based on Landsat time series processed by the Random Forest classifier in the Google Earth Engine platform. The feature selection method was used to reduce redundant data. The final maps comprised five LULC classes (non-vegetated areas, built-up areas, croplands, open evergreen and deciduous forests, and dense vegetation) with an overall accuracy ranging from 80.5% to 88.7%. LULC change detection between 2011 and 2020 revealed that non-vegetated areas had increased by 0.7%, built-up by 2.0%, and dense vegetation by 1.3%. On the other hand, open evergreen and deciduous forests had decreased by 4.1% and croplands by 0.01%. The approach used in this paper improves the current systematic mapping approach in Mozambique by minimizing the methodological gaps and reducing the temporal amplitude, thus supporting regional territorial development policies. Full article

A tropical cyclone is a significant natural phenomenon that results in substantial socio-economic and environmental damage. These catastrophes impact millions of people every year, with those who live close to coastal areas being particularly affected. With a few coastal cities with large population densities, Tamil Nadu’s coast is the third-most cyclone-prone state in India. This study involves the generation of a cyclone risk map by utilizing four distinct components: hazards, exposure, vulnerability, and mitigation. The study employed a Geographical Information System (GIS) and an Analytical Hierarchical Process (AHP) technique to compute an integrated risk index considering 16 spatial variables. The study was validated by the devastating cyclone GAJA in 2018. The resulting risk assessment shows the cyclone risk is higher in zones 1 and 2 in the study area and emphasizes the variations in mitigation impact on cyclone risk in zones 4 and 5. The risk maps demonstrate that low-lying areas near the coast, comprising about 3%, are perceived as having the adaptive capacity for disaster mitigation and are at heightened risk from cyclones regarding population and assets. The present study can offer valuable guidance for enhancing natural hazard preparedness and mitigation measures in the coastal region of Tamil Nadu. Full article

Analyzing the influencing factors of PM_2.5 concentration, scenario simulations, and countermeasure research to address the problem of PM_2.5 pollution in Guangdong Province is of great significance for governments at all levels for formulating relevant policies. In this study, the ChinaHighPM_2.5 dataset and economic and social statistics for Guangdong Province from 2010 to 2019 were selected, and a PM_2.5 pollution management compliance path formulation method based on the multi-scenario simulation was proposed by combining the differences in city types and PM_2.5 concentration prediction. Based on the prediction model of PM_2.5 concentration constructed by the Ridge and SVM models and facing the PM_2.5 pollution control target in 2025, the urban PM_2.5 pollution control scenario considering the characteristics of urban development was constructed. According to the scenario simulation results of the PM_2.5 prediction model, the PM_2.5 pollution control path suitable for Guangdong Province during the 14th Five-Year Plan period was explored. The coupling coordination model was used to explore the spatial and temporal pattern evolution of PM_2.5 pollution collaborative governance in various prefecture-level cities under the standard path, and the policy recommendations for PM_2.5 pollution control during the 14th Five-Year Plan period are proposed. The results showed the following: ① in the case of small samples, the model can provide effective simulation predictions for the study of urban pollutant management compliance pathways. ② Under the scenario of PM_2.5 management meeting the standard, in 2025, the annual average mass concentration of PM_2.5 in all prefecture-level cities in Guangdong Province will be lower than 22 μg/m³, and the annual average concentration of PM_2.5 in the whole province will drop from 25.91 μg/m³ to 21.04 μg/m³, which will fulfil the goal of reducing the annual average concentration of PM_2.5 in the whole province to below 22 μg/m³, as set out in the 14th Five-Year Plan for the Ecological Environmental Protection of Guangdong Province. ③ Under the path of PM_2.5 control and attainment, the regional coordination relationship among prefecture-level cities in Guangdong Province is gradually optimized, the number of intermediate-level coordinated cities will increase, and the overall spatial distribution pattern will be low in the middle and high in the surrounding area. Based on the characteristics of the four city types, it is recommended that a staggered development strategy be implemented to achieve synergy between economic development and environmental quality. Urban type I should focus on restructuring freight transportation to reduce urban pollutant emissions. City type II should focus on urban transportation and greening. For city type III, the focus should be on optimizing the industrial structure, adjusting the freight structure, and increasing the greening rate of the city. For city type IV, industrial upgrading, energy efficiency, freight structure, and management of industrial pollutant emissions should be strengthened. Full article

Animal movements are realizations of complex spatiotemporal processes. Central to these processes are the varied environmental contexts in which animals move, which fundamentally impact the movement trajectories of individuals at fine spatial and temporal scales. An emerging perspective in time geography is the direct examination of the influence that varying contexts may have on observed movements. An approach that considers environmental context can yield actionable information for wildlife management, planning, and conservation; for instance, identifying areas of probable occupancy by an animal may improve the efficiency of fieldwork. This research develops the first known practical application of a new cost-distance-based, probabilistic voxel space–time prism (CDBPSTP) in efforts to more realistically characterize the unobserved habitat occupancies of animals occurring between known positions provided by location-aware technologies. The CDBPSTP method is applied to trajectory data collected for a group of red deer (Cervus elaphus) tracked near Banff National Park, Alberta, Canada. As a demonstration of the added value from examining how context influences movement, CDBPSTP habitat occupancy results are compared to the earlier PSTP method in context with empirical and theoretical understandings of red deer habitat preference and space-use behaviors. This comparison reveals that with CDBPSTP, variation present in the mover’s environment is explicitly considered as an influence on the mover’s probable path and occupancies between observations of its location. With the increasing availability of high-resolution geolocational and associated environmental data, this study highlights the potential for CDBPSTP to be leveraged as a broadly applicable tool in animal movement analysis. Full article

It is important to capture passengers’ public transit behavior and their mobility to create profiles, which are critical for analyzing human activities, understanding the social and economic structure of cities, improving public transportation, assisting urban planning, and promoting smart cities. In this paper, we develop a generative adversarial machine learning network to characterize the temporal and spatial mobility behavior of public transit passengers, based on massive smart card data and road network data. The Apriori algorithm is extended with spatio-temporal constraints to extract frequent transit mobility patterns of individual passengers based on a reconstructed personal trip dataset. This individual-level pattern information is used to construct personalized feature vectors. For regular and frequent public transit passengers, we identify similar transit mobility groups using spatio-temporal constraints to construct a group feature vector. We develop a generative adversarial network to embed public transit mobility of passengers. The proposed model’s generator consists of an auto-encoder, which extracts a low-dimensional and compact representation of passenger behavior, and a pre-trained sub-generator containing generalization features of public transit passengers. Shenzhen City is taken as the study area in this paper, and experiments were carried out based on smart card data, road network data, and bus GPS data. Clustering analysis of embedding vector representation and estimation of the top K transit destinations were conducted, verifying that the proposed method can profile passenger transit mobility in a comprehensive and compact manner. Full article

From an extensive search of papers related to the comparison of Global Digital Elevation Models (hereinafter GDEMs), an analysis is carried out that aims to answer several questions such as: Which GDEMs have been compared? Where have the comparisons been made? How many comparisons have been made? How have the assessments been carried out? Which is the GDEM option with the lowest RMSE? Analysis shows that SRTM and ASTER are the most popular GDEMs, that the countries where more comparisons have been made are Brazil, India, and China, and that the main type of reference data for evaluations is the use of points surveyed by GNSS techniques. A variety of criteria have been found for the comparison of GDEMs, but the most used are the RMSE and the standard deviation of the elevation error. There are numerous criteria with a more user-centric character in thematic areas, such as morphometry, geomorphology, erosion, etc. However, in none of the thematic areas does there exist a standard method of comparison. This limits the possibilities of establishing a ranking of GDEMs based on their user-focused quality. In addition, the methods and reference data set are not adequately explained or shared, which limits the interoperability of the studies carried out and the ability to make robust comparisons between them. Full article

As an essential role in cartographic generalization, road network selection produces basic geographic information across map scales. However, the previous selection methods could not simultaneously consider both attribute characteristics and spatial structure. In light of this, an intelligent road network selection method based on a graph neural network (GNN) is proposed in this paper. Firstly, the selection case is designed to construct a sample library. Secondly, some neighbor sampling and aggregation rules are developed to update road features. Then, a GNN-based selection model is designed to calculate classification labels, thus completing road network selection. Finally, a few comparative analyses with different selection methods are conducted, verifying that most of the accuracy values of the GNN model are stable over 90%. The experiments indicate that this method could aggregate stroke nodes and their neighbors together to synchronously preserve semantic, geometric, and topological features of road strokes, and the selection result is closer to the reference map. Therefore, this paper could bridge the distance between deep learning and cartographic generalization, thus facilitating a more intelligent road network selection method. Full article

The use of smartphone tracking is seen as the way forward in data collection for travel behavior studies. It overcomes some of the weaknesses of the classical approach (which uses paper trip diaries) in terms of accuracy and user annoyance. This article evaluates if these benefits hold in the practical application of smartphone tracking and compares the findings of a travel behavior survey using smartphone tracking to the findings of a previous paper survey. We compare three phases of the travel behavior study. In the recruitment phase, we expect smartphone tracking to make people more willing to participate in surveys, given the innovative nature and reduced burden to participants. However, we found the recruitment of participants equally challenging as for classical methods. In the data collection phase, however, we observe that participants entering the smartphone tracking survey are much more likely to complete the data collection period than when using paper trip diaries. Because of the limited burden, the risk of drop-out from the survey is significantly lower, making the actual data collection more efficient, even for longer survey periods. Finally, in the data analysis phase, the travel behavior indicators derived from smartphone tracking data result in higher average trip rates, shorter average trip lengths and a higher share of active modes (bike, walking) than the results from the paper survey. Although this is explained by more complete and more consistent trip registration, this finding is problematic for comparability between surveys based on different methods, both for longitudinal monitoring (comparability over consequent surveys) and for benchmarking (comparability over geographical areas). Therefore, it is crucial to clearly report the applied data collection methods when describing or comparing travel indicators. In surveys, a combined approach of both written trip diaries and smartphone tracking is advised, where each method can complement the shortcomings of the other. Full article

The Ultra-Wideband (UWB) indoor positioning method is widely used in areas where no satellite signals are available. However, during the measurement process of UWB, the collected data contain random errors. To alleviate the effect of random errors on positioning accuracy, an improved adaptive sparrow search algorithm (IASSA) based on the sparrow search algorithm (SSA) is proposed in this paper by introducing three strategies, namely, the two-step weighted least squares algorithm, adaptive adjustment of search boundary, and producer–scrounger quantity adaptive adjustment. The simulation and field test results indicate that the IASSA algorithm achieves significantly higher localization accuracy than previous methods. Meanwhile, the IASSA algorithm requires fewer iterations, which overcomes the problem of the long computation time of the swarm intelligence optimization algorithm. Therefore, the IASSA algorithm has advantages in indoor positioning accuracy and robustness performance. Full article

This study compares the environmental sustainability of two cities in Saudi Arabia, Abha, and Bisha, through their green spaces, by analyzing green spaces in both cities. And the application of spatial statistics tools in the Arc Map program, to measure the spatial relationship between the green areas depending on the measurement of the location, shape, dimensions and areas, as this distribution is linked to statistical laws leading to the construction of a spatial model for the green areas in the two cities, and among these methods is the spatial average, The central phenomenon, the distribution trend, the standard circle, and finally the neighborhood analysis. The study seeks to recognize the parameters that contribute to environmental sustainability through green spaces. Understanding the effectiveness of green spaces in promoting environmental sustainability is crucial for policymakers to make informed decisions about urban planning and development. Sustainability in the environment is making responsible use of natural resources while also taking measures to safeguard the surrounding area to maintain high standards of environmental quality over the long term. The concept entails the preservation of equilibrium among economic, social, and environmental considerations to guarantee the satisfaction of current societal requirements while safeguarding the capacity of forthcoming generations to fulfill their own necessities. Environmental sustainability is crucial for the well-being of the planet and all living beings that inhabit it. Green spaces play a vital role in environmental sustainability. The provision of green spaces is associated with a multitude of advantages, including but not limited to the mitigation of air and noise pollution, temperature regulation, and enhancement of the overall visual appeal of urban areas. The study employed Geographic Information System (GIS) and spatial statistical analysis to investigate the spatial arrangement of environmental sustainability in the two urban areas. The study also relied on fieldwork, including a questionnaire, to gather data from the residents of the cities. The research study found that the standard distance measures the average distance between each green space and the mean center. In this case, the standard distance indicates how dispersed or clustered the green spaces are around the mean center. A smaller standard distance value suggests that the green spaces are more clustered around the mean center, while a larger value suggests a more dispersed distribution. Full article

With an increase in the amount of natural disasters, the combined use of cloud-penetrating Synthetic Aperture Radar and deep learning becomes unavoidable for their monitoring. This article proposes a methodology for forest fire detection using unsupervised location-expert autoencoders and Sentinel-1 SAR time series. The models are trained on SAR multitemporal images over a specific area using a reference period and extract any deviating time series over that same area for the test period. We present three variations of the autoencoder, incorporating either temporal features or spatiotemporal features, and we compare it against a state-of-the-art supervised autoencoder. Despite their limitations, we show that unsupervised approaches are on par with supervised techniques, performance-wise. A specific architecture, the fully temporal autoencoder, stands out as the best-performing unsupervised approach by leveraging temporal information of Sentinel-1 time series using one-dimensional convolutional layers. The approach is generic and can be applied to many applications, though we focus here on forest fire detection in Canadian boreal forests as a successful use case. Full article

Personalized mappings become popular among the public with the support of data diversity and device diversity. To develop personalized maps, constructing map symbols through automated ways is beneficial. The formal representation of map symbols (i.e., expressing map symbols by mathematical operators) is fundamental to the automated construction of map symbols. A previous study to evaluate the feasibility of structures of Chinese characters for representing map symbols shows that 77.5% of map symbols can be represented by them, although there are imperfections in some cases. It means that: (1) the other 22.5% of symbols should be formally represented by other mathematical solutions, and (2) those imperfect cases should be made perfect through some modification or refinements. In this study, we solve the representation problems of these two types of map symbols (i.e., the map symbol did not or imperfectly fit the structures of Chinese characters) by employing additional basic operators and proposing some metric and color modifications. To validate these proposed solutions, experiments have been carried out by using eight sets of symbols that are publicly available (e.g., Google Icons). The results indicated that almost all the map symbols can be formally represented with additional operators and metric and color modifications. The percentages of map symbols that did not fit structures of Chinese characters solved by these operators and modifications are 2.4% and 20.1%, respectively. The percentages of map symbols that imperfectly fit them solved by these operators and modifications are 8.7% and 8%, respectively. This work could not only enrich cartographic theory but also prompt the mathematization of map symbol construction. Full article

Map matching of trajectory data has wide applications in path planning, traffic flow analysis, and intelligent driving. The process of map matching involves matching GPS trajectory points to roads in a roadway network, thereby converting a trajectory sequence into a segment sequence. However, GPS trajectories are frequently incorrectly matched during the map-matching process, leading to matching errors. Considering that few studies have focused on the causes of map-matching errors, as well as the distribution of these errors, the study aims to investigate the spatiotemporal characteristics and the contributing factors that cause map-matching errors. The study employs the Hidden Markov Model (HMM) algorithm to match the trajectories and identifies the four types of map-matching errors by examining the relationship between the matched trajectories and the driving routes. The map-matching errors consist of Off-Road Error (ORE), Wrong-match on Road Error (WRE), Off-Junction Error (OJE), and Wrong-match in Junction Error (WJE). The kernel density method and multinomial logistic model are further exploited to analyze the spatiotemporal patterns of the map-matching errors. The results indicate that the occurrence of map-matching errors substantially varies in time and space, with variation significantly influenced by intersection features and road characteristics. The findings provide a better understanding of the contributing factors associated with map-matching errors and serve to improve the accuracy of map matching for commercial vehicles. Full article

Based on a study of the spatial distribution of coffee shops in the main urban area of Beijing, the main influencing factors were selected based on the multi-source space data. Subsequently, three regression models were compared, and the best site selection model was found. A comparison was performed between the prediction model functioning with a buffer and without one, and the accuracy of the location model was verified by comparing the actual change trend and the predicted trend in two years. The following conclusions were obtained: (1) coffee shops in the main urban area of Beijing are clustered in an area within 12 km of the main urban center, and also around the core commercial agglomeration area; (2) the random forest (RF) model is the best model in this study, and the accuracy values before and after buffer analysis were 0.915 and 0.929, respectively; and (3) after verifying the accuracy of the model through two years of data, we recommend the establishment of a main road buffer zone for site selection, and the success rate of site selection was found to reach 72.97%. This study provides crucial insight for coffee shop prediction model selection and potential store location selection, which is significant to improving the layout of leisure spaces and promoting economic development. Full article

This paper addresses two common challenges in analyzing spatial epidemiological data, specifically disease incidence rates recorded over small areas: filtering noise caused by small local population sizes and deriving estimates at different spatial scales. Geostatistical techniques, including Poisson kriging (PK), have been used to address these issues by accounting for spatial correlation patterns and neighboring observations in smoothing and changing spatial support. However, PK has a limitation in that it can generate unrealistic rates that are either negative or greater than 100%. To overcome this limitation, an alternative method that relies on soft indicator kriging (IK) is presented. The performance of this method is compared to PK using daily COVID-19 incidence rates recorded in 2020–2021 for each of the 581 municipalities in Belgium. Both approaches are used to derive noise-filtered incidence rates for four different dates of the pandemic at the municipality level and at the nodes of a 1 km spacing grid covering the country. The IK approach has several attractive features: (1) the lack of negative kriging estimates, (2) the smaller smoothing effect, and (3) the better agreement with observed municipality-level rates after aggregation, in particular when the original rate was zero. Full article

The urban road network is a large and complex system characterized by significant heterogeneity arising from different spatial structures and traffic demands. To facilitate effective management and control, it is necessary to partition the road network into homogeneous sub-areas. In this regard, we aim to propose a hybrid method for partitioning sub-areas with intra-area homogeneity, inter-area heterogeneity, and similar sizes, called CSDRA. It is specifically designed for bidirectional road networks with segment weights that encompass traffic flow, speed, or roadside facility evaluation. Based on community detection and spectral clustering, this proposed method comprises four main modules: initial partition, partitioning of large sub-areas, reassignment of small sub-areas, and boundary adjustment. In the preliminary partitioning work, we also design a road network reconstruction method which further helps to enhance the intra-area homogeneity and inter-area heterogeneity of partitioning results. Furthermore, to align with the requirement for comparable work units in practical traffic management and control, we control the similarity in the size of sub-areas by enforcing upper and lower bound constraints on the size of the sub-areas. We verify the outperformance of the proposed method by an experiment on the partitioning of an urban road network in Guangzhou, China, where we employ sidewalk barrier-free score data as segment weights. The results demonstrate the effectiveness of both the road network reconstruction method and the CSDRA proposed in this paper, as they significantly improve the partitioning outcomes compared with other methods using different evaluation indicators corresponding to the partitioning objectives. Finally, we investigate the influence of constraint parameters on the evaluation indicator. Our findings indicate that appropriately configuring these constraint parameters can effectively minimize sub-region size variations while having minimal impact on other aspects. Full article

This article summarizes the Special Issue of Cartography and Geomedia. Here, Cartography and Geomedia presents a view of cartography as a combination of technology, science, and art, with a focus on the development of geomedia in a geomatic and design-based context. Individual considerations are presented according to the following topics: efficiency of mapping techniques; historical cartographic works in a geomedial context; cartographic pragmatics for cultural heritage, teaching, and tourism; and pragmatism in gaming cartography. The main conclusion is that the two approaches to learning, revealing, and understanding geographic phenomena—starting from a specific geographical phenomenon and starting from maps and geomedia to understand geographical space—have their pragmatic strengths. Full article

Historical geographic data play an important supporting role in the study of long-term geographic studies, such as climate change, urban expansion and land-use and land-cover change. These data vary in source, format and accuracy and are widely found in historical documents, old maps, produced vector data, aerial photographs, old photographs, etc. The complex nature of data makes it difficult for researchers to organize, store and manage in a unified manner. Thus, GIS practitioners and social scientists will collectively face the challenge of integrating historical data into spatial databases. Herein, we introduced the concept of a multi-level spatial grid, selecting Shanghai as the study area, to construct the Shanghai historical geographic database and give the conceptual model and processing method. The experiment was performed using the China Historical Geographic Information System (CHGIS), which showed the historical evolution of Shanghai more conveniently. Meanwhile, we simulated one million rows of historical geographic data in Shanghai and compared the retrieval efficiency of the encoding method with the latitude–longitude and geometric object indexing methods, which demonstrated that our method was very effective. This research is important for the construction of a historical urban database, which can better preserve historical resources and promote urban culture with information science and technology. Full article

Achieving seamless integration between virtual objects and real scenes has always been an important issue in augmented reality (AR) research. To achieve this, it is necessary to provide virtual objects with real-time and accurate lighting conditions from a real scene. Therefore, the purpose of this study is to realize lighting consistency rendering for real-time AR systems in outdoor environments, aiming to enhance the user’s sense of immersion. In this paper, we propose a lighting consistency technique for real-time AR systems in outdoor environments based on multi-source geographical information (MGI). Specifically, we introduce MGI into the study of lighting consistency and construct a comprehensive database to store and manage the acquired MGI data. Based on this, we proposed a sky radiance model driven using the MGI. Finally, we utilized the sky radiance model along with light sensor data to render the virtual objects in outdoor scenes. The experimental results show that the shadow angular error is reduced to 5.2°, and the system frame rate is increased to 94.26. This means that our method achieves a high level of realism in the fusion of virtual objects and real scenes while ensuring a high frame rate in the system. With this technology, users can conveniently and extensively realize the lighting consistency rendering of real-time AR systems in outdoor scenes using mobile devices. Full article

The evaluation of anthropogenic impacts on the landscape is an issue that has traditionally been carried out from a descriptive or at least somewhat qualitative perspective. However, in recent years, the technological improvements provided by geographic information systems (GIS) and spatial statistics have led to more objective methodological frameworks for analysis based on quantitative approaches. This study proposes an innovative methodological framework for the evaluation of landscape impacts of the usual anthropization phenomena, using a retrospective spatiotemporal analysis based on geostatistical indicators. Various territorial indices have been used to assess the spatiotemporal evolution of fragmentation of the built-up urban fabric, the construction of roads or linear communication works and the changes in land use. These phenomena have been statistically correlated with objective indicators of the landscape’s intrinsic value. The analysis of said spatial statistical correlation has been applied to three different but neighboring environments in the region of Murcia, located in the southeast of Mediterranean Spain, providing interesting results on the objective impact of each of these phenomena on the landscape and depending on the boundary conditions. Full article

In this research, we focused on armed conflicts and related violence. The study reviewed the use of machine learning to predict the likelihood of conflict escalation and the role of conditioning factors. The results showed that machine learning and predictive models could help identify conflict-prone locations and geospatial factors contributing to conflict escalation. The study found 46 relevant papers and emphasized the importance of considering unique predictors and conditioning factors for each conflict. It was found that the conflict susceptibility of a region is influenced principally by its socioeconomic conditions and its political/governance factors. We concluded that machine learning has the potential to be a valuable tool in conflict analysis and, therefore, it can be an asset in conflict mitigation and prevention, but the accuracy of the models depends on data quality and the careful selection of conditioning factors. Future research should aim to refine the methodology for more accurate prediction of the models. Full article

Rural tourism has long been recognized as a significant strategy for promoting rural revitalization in China. Excessive development has had a number of negative consequences for rural tourism. As a result, there is a growing need to optimize the developmental framework of rural tourism in order to ensure its sustainable growth. This study focuses on key tourism villages and employs geostatistical analysis and the random forest methodology to elucidate the spatial mechanisms underlying rural tourism and identify potential areas for its development in China. The research findings reveal several important insights: (1) Key tourism villages exhibit a concentrated spatial distribution, characterized by pronounced regional disparities. (2) The intrinsic characteristics of rural areas and the conditions conducive to tourism development play pivotal roles in shaping rural tourism. Notably, cultural resources, tourism resources, rural accessibility, and tourism potential are identified as the primary influential factors. (3) Predictive modeling using random forest analysis indicates that densely populated areas in the eastern region retain the highest level of suitability for rural tourism. In contrast, the development of rural tourism in western and border regions encounters certain constraints. Additionally, the northern region encompasses larger expanses with high suitability, whereas the southern region is generally moderate. This comprehensive nationwide investigation provides valuable insights into the key aspects of rural tourism development and offers practical guidance for achieving sustainable rural tourism practices in China. Full article

(1) Background: This work analyzes socioeconomic equity in bike-sharing systems. Specifically, we study the effect of income on bike use in an innovative way by analyzing the frequency of bike routes connecting areas with different mean incomes. (2) Methods: We use Social Network Analysis tools to estimate the probability of connection between two stations depending on income and controlling for other predictors. The method was applied to a bike-sharing system located in the city of Las Palmas de Gran Canaria, Spain. (3) Results: Stations located in lower-income neighborhoods have a lower probability of generating routes, and stations located in higher-income areas are more likely to be connected to each other. (4) Conclusions: The frequency of bike routes is more influenced by income than other socioeconomic characteristics of the area, such as commercial and leisure use. Since socioeconomic inequities are corroborated by the work, policies for lower-income users should be promoted. Full article

With the advent of the information age and rapid population growth, the urban transportation environment is deteriorating. Travel-route planning is a key issue in modern sustainable transportation systems. When conducting route planning, identifying the spatiotemporal disparities between planned routes and the routes chosen by actual drivers, as well as their underlying reasons, is an important method for optimizing route planning. In this study, we explore the spatial–temporal differences between planned routes and actual routes by studying the popular roads which are avoided by drivers (denoted as: PRAD) from car-hailing trajectories. By applying an improved Hidden Markov Model (HMM) map-matching algorithm to the original trajectories, we obtain the Origin-Destination (OD) matrix of vehicle travel and its corresponding actual routes, as well as the planned routes generated by the A* routing algorithm. We utilize the Jaccard index to quantify the similarity between actual and planned routes for the same OD pairs. The causes of PRADs are detected and further analyzed from the perspective of traffic conditions. By analyzing ride-hailing trajectories provided by DiDi, we examine the route behavior of drivers in Wuhan city on weekdays and weekends and discuss the relationship between traffic conditions and PRADs. The results indicate that the average accuracy of GNSS trajectory point-to-road map-matching reaches 88.83%, which is approximately 12% higher than the accuracy achieved by the HMM map-matching method proposed by Hu et al. Furthermore, the analysis of PRAD causes reveals that PRADs occurring on weekdays account for approximately 65% and are significantly associated with traffic congestion and accidents during that time. The findings of this study provide insights for future research on sustainable transportation systems and contribute to the development of improved route-planning strategies. Full article

It is essential to understand the spatial equity of healthcare services to achieve the Sustainable Development Goals. Spatial and non-spatial factors affect access to healthcare, resulting in inequality in the hierarchical medical treatment system. Thus, to provide a comprehensive equity evaluation, it is indispensable to investigate the extent to which spatial accessibility to healthcare services varies due to various factors. This study attempted to analyze the determinants of healthcare accessibility under multi-trip modes and integrate them into Theil index, as a demand index to evaluate spatial equity in the system. The results reveal an inadequate and inequitable distribution of healthcare resources. While access to primary hospitals is limited (47.37% of residential locations cannot access them on foot), 96.58% of residential locations can access general and tertiary hospitals via public transport or driving. Furthermore, inequitable access to the three-tiered medical system was evaluated on a more granular scale, with primary hospitals being closest to achieving equity (inequitable for only 48.83% of residential locations), followed by general and tertiary hospitals (82.01% and 89.20%, respectively). The unequal residential locations brought on by an abundance of medical resources are far from those with a shortage of resources (66.86% > 5.34%). It is thus suggested that services be expanded or resources be transferred to move toward a more equitable system. Our findings provide policymakers with insights into how to increase accessibility to public health. Full article