ISPRS International Journal of Geo-Information

Humans’ activities in urban areas put a strain on local water resources. This paper introduces a method to accurately simulate the stress urban water demand in Germany puts on local resources on a single-building level, and scalable to regional levels without loss of detail. The method integrates building geometry, building physics, census, socio-economy and meteorological information to provide a general approach to assessing water demands that also overcome obstacles on data aggregation and processing imposed by data privacy guidelines. Three German counties were used as validation cases to prove the feasibility of the presented approach: on average, per capita water demand and aggregated water demand deviates by less than 7% from real demand data. Scenarios applied to a case region Ludwigsburg in Germany, which takes the increment of water price, aging of the population and the climate change into account, show that the residential water demand has the change of −2%, +7% and −0.4% respectively. The industrial water demand increases by 46% due to the development of economy indicated by GDP per capita. The rise of precipitation and temperature raise the water demand in non-residential buildings (excluding industry) of 1%. Full article

For more than ten years, Central Chile has faced drought conditions, which impact crop production and quality, increasing food security risk. Under this scenario, implementing management practices that allow increasing water use efficiency is urgent. The study was carried out on kiwifruit trees, located in the O’Higgins region, Chile for season 2018–2019 and 2019–2020. We evaluate the time-series of nine vegetation indices in the VNIR and SWIR regions derived from Sentinel-2 (A/B) satellites to establish how much variability in the canopy water status there was. Over the study’s site, eleven sensors were installed in five trees, which continuously measured the leaf’s turgor pressure (Yara Water-Sensor). A strong Spearman’s ($\rho$) correlation between turgor pressure and vegetation indices was obtained, having −0.88 with EVI and −0.81 with GVMI for season 2018–2019, and lower correlation for season 2019–2020, reaching −0.65 with Rededge1 and −0.66 with EVI. However, the NIR range’s indices were influenced by the vegetative development of the crop rather than its water status. The red-edge showed better performance as the vegetative growth did not affect it. It is necessary to expand the study to consider higher variability in kiwifruit’s water conditions and incorporate the sensitivity of different wavelengths. Full article

The death of George Floyd has brought a new wave of 2020 Black Lives Matter (BLM) protests into U.S. cities. Protests happened in a few cities accompanied by reports of violence over the first few days. The protests appear to be related to rising crime. This study uses newly collected crime data in 50 U.S. cities/counties to explore the spatiotemporal crime changes under BLM protests and to estimate the driving factors of burglary induced by the BLM protest. Four spatial and statistic models were used, including the Average Nearest Neighbor (ANN), Hotspot Analysis, Least Absolute Shrinkage, and Selection Operator (LASSO), and Binary Logistic Regression. The results show that (1) crime, especially burglary, has risen sharply in a few cities/counties, yet heterogeneity exists across cities/counties; (2) the volume and spatial distribution of certain crime types changed under BLM protest, the activity of burglary clustered in certain regions during protests period; (3) education, race, demographic, and crime rate in 2019 are related with burglary changes during BLM protests. The findings from this study can provide valuable information for ensuring the capabilities of the police and governmental agencies to deal with the evolving crisis. Full article

The epidemic of coronavirus-disease-2019 (COVID-19) started in Italy with the first official diagnosis on 21 February 2020; However, it is not known how many cases were already present in earlier days and weeks, thus limiting the possibilities of conducting any retrospective analysis. We hypothesized that an unbiased representation of COVID-19 diffusion in these early phases could be inferred by the georeferenced calls to the emergency number relevant to respiratory problems and by the following emergency medical services (EMS) interventions. Accordingly, the aim of this study was to identify the beginning of anomalous trends (change in the data morphology) in emergency calls and EMS ambulances dispatches and reconstruct COVID-19 spatiotemporal evolution on the territory of Lombardy region. Accordingly, a signal processing method, previously used to find morphological features on the electrocardiographic signal, was applied on a time series representative of territorial clusters of about 100,000 citizens. Both emergency calls and age- and gender-weighted ambulance dispatches resulted strongly correlated to COVID-19 casualties on a provincial level, and the identified local starting days anticipated the official diagnoses and casualties, thus demonstrating how these parameters could be effectively used as early indicators for the spatiotemporal evolution of the epidemic on a certain territory. Full article

Average speed information, which is essential for routing applications, is often missing in the freely available OpenStreetMap (OSM) road network. In this contribution, we propose an estimation framework, including different machine learning (ML) models that estimate rural roads’ average speed based on current road information in OSM. We rely on three datasets covering two regions in Chile and Australia. Google Directions API data serves as reference data. An appropriate estimation framework is presented, which involves supervised ML models, unsupervised clustering, and dimensionality reduction to generate new input features. The regression performance of each model with different input feature modes is evaluated on each dataset. The best performing model results in a coefficient of determination $R^2=80.43\%$, which is significantly better than previous approaches relying on domain-knowledge. Overall, the potential of the ML-based estimation framework to estimate the average speed with OSM road network data is demonstrated. This ML-based approach is data-driven and does not require any domain knowledge. In the future, we intend to focus on the generalization ability of the estimation framework concerning its application in different regions worldwide. The implementation of our estimation framework for an exemplary dataset is provided on GitHub. Full article

Spatial data of regional populations are indispensable in studying the impact of human activities on resource utilization and the ecological environment. Because the differences between datasets and their spatial distribution are still unclear, this has become a puzzle in data selection and application. This study is based on four mainstream spatialized population datasets: the History Database of the Global Environment version 3.2.000 (HYDE), Gridded Population of the World version 4 (GPWv4), Global Human Settlement Layer (GHSL), and WorldPop. In view of possible influences of geographical factors, this study analyzes the differences in accuracy of population estimation by computing relative errors and population spatial distribution consistency in different regions by comparing datasets pixel by pixel. The results demonstrate the following: (1) Source data, spatialization methods, and case area features affect the precision of datasets. As the main data source is statistical data and the spatialization method maintains the population in the administrative region, the populations of GPWv4 and GHSL are closest to the statistical data value. (2) The application of remote sensing, mobile communication, and other geospatial data makes the datasets more accurate in the United Kingdom, with rich information, and the absolute value of relative errors is less than 4%. In the Tibet Autonomous Region of China, where data are hard to obtain, the four datasets have larger relative errors. However, the area where the four datasets are completely consistent is as high as 84.73% in Tibet, while in the UK it is only 66.76%. (3) The areas where the spatial patterns of the four datasets are completely consistent are mainly distributed in areas with low population density, or with developed urbanization and concentrated population distribution. Areas where the datasets have poor consistency are mainly distributed in medium population density areas with high urbanization levels. Therefore, in such areas, a more careful assessment should be made during the data application process, and more emphasis should be placed on improving data accuracy when using spatialization methods. Full article

Map-based dashboards are among the most popular tools that support the viewing and understanding of a large amount of geo-data with complex relations. In spite of many existing design examples, little is known about their impacts on users and whether they match the information demand and expectations of target users. The authors first designed a novel map-based dashboard to support their target users’ spatiotemporal knowledge acquisition and analysis, and then conducted an experiment to assess the feasibility of the proposed dashboard. The experiment consists of eye-tracking, benchmark tasks, and interviews. A total of 40 participants were recruited for the experiment. The results have verified the effectiveness and efficiency of the proposed map-based dashboard in supporting the given tasks. At the same time, the experiment has revealed a number of aspects for improvement related to the layout design, the labeling of multiple panels and the integration of visual analytical elements in map-based dashboards, as well as future user studies. Full article

Chinese address element segmentation is a basic and key step in geocoding technology, and the segmentation results directly affect the accuracy and certainty of geocoding. However, due to the lack of obvious word boundaries in Chinese text, the grammatical and semantic features of Chinese text are complicated. Coupled with the diversity and complexity in Chinese address expressions, the segmentation of Chinese address elements is a substantial challenge. Therefore, this paper proposes a method of Chinese address element segmentation based on a bidirectional gated recurrent unit (Bi-GRU) neural network. This method uses the Bi-GRU neural network to generate tag features based on Chinese word segmentation and then uses the Viterbi algorithm to perform tag inference to achieve the segmentation of Chinese address elements. The neural network model is trained and verified based on the point of interest (POI) address data and partial directory data from the Baidu map of Beijing. The results show that the method is superior to previous neural network models in terms of segmentation performance and efficiency. Full article

Catchment division constitutes the foundation for urban water flood forecasting but represents a technically challenging task. The accurate division of catchments is significant for precisely forecasting urban waterlogging. However, existing catchment division methods usually lead to produce results that do not accurately reflect the actual land-use distributions. In recent years, most research has been performed in smaller study areas (less than 10 km²), in residential areas, parks and campuses, and usually focused on a single landscape type. However, for large highly urbanized areas with complex land uses, due to the spatial heterogeneity and complexity of such areas in terms of building, traffic network and hydrology, etc., there is few studies on sub-catchment division. Moreover, the division results by using existing method usually have deviate with the actual land-type distributions. To address the above-mentioned issues, a sub-catchment division method was here proposed that accounts for land-use types and flow directions, and it is suitable for large urban areas by introducing an auto-adaptive threshold adjustment in a novel algorithm. First, the study area is divided into first- and second-level (FL and SL, respectively) catchments according to the macroscale features such as natural landforms, canals, and pipe network. Second, an amended DEM (Digital Elevation Model) and flow direction data are used to divide the SL catchments into third-level direction-based (D-B) catchments. Finally, a novel land use-based algorithm is proposed to divide the D-B catchments into the “smallest” catchments (S-catchments). A large-scale area (44 km²) in Dongying City of China was employed to validate the proposed method. The experiment showed that the proposed method is suitable for subcatchment divisions in large regions and can ensure that the subcatchments are consistent with the actual distribution of land uses and runoff directions. Full article

Viewshed computation of a digital elevation model (DEM) plays an important role in a geographic information system, but the required high computational time is a serious problem for a practical application. Hitherto, the mainstream methods of viewshed computing include line-of-sight method, reference planes method, etc. Based on these classical algorithms, a new algorithm for viewshed computation is proposed in this paper: the Matryoshka doll algorithm. Through a pooling operation, the minimum expected height of the DEM is introduced as max-pooling with minimum expected height in the viewshed computing optimization. This is to increase the efficiency and adaptability of the computation of the visibility range. The experimental results demonstrate that the algorithm has obvious advantages in computing speed, but with the accuracy only slightly reduced. Full article

Social Media Big Data (SMBD) is widely used to serve the economic and social development of human beings. However, as a young research and practice field, the understanding of SMBD in academia is not enough and needs to be supplemented. This paper took Web of Science (WoS) core collection as the data source, and used traditional statistical methods and CiteSpace software to carry out the scientometrics analysis of SMBD, which showed the research status, hotspots and trends in this field. The results showed that: (1) More and more attention has been paid to SMBD research in academia, and the number of journals published has been increased in recent years, mainly in subjects such as Computer Science Engineering and Telecommunications. The results were published primarily in IEEE Access Sustainability and Future Generation Computer Systems the International Journal of eScience and so on; (2) In terms of contributions, China, the United States, the United Kingdom and other countries (regions) have published the most papers in SMBD, high-yield institutions also mainly from these countries (regions). There were already some excellent teams in the field, such as the Wanggen Wan team at Shanghai University and Haoran Xie team from City University of Hong Kong; (3) we studied the hotspots of SMBD in recent years, and realized the summary of the frontier of SMBD based on the keywords and co-citation literature, including the deep excavation and construction of social media technology, the reflection and concerns about the rapid development of social media, and the role of SMBD in solving human social development problems. These studies could provide values and references for SMBD researchers to understand the research status, hotspots and trends in this field. Full article

A new method for selecting optimal scales when mapping topographic or hydrographic features is introduced. The method employs rank-size partition of heavy-tailed distributions to detect nodes of rescaling invariance in the underlying hierarchy of the dataset. These nodes, known as head/tail breaks, can be used to indicate optimal scales. Then, the Fractal Net Evolution Assessment (FNEA) segmentation algorithm is applied with the topographic or hydrographic surfaces to produce optimally scaled objects. A topological transformation allows linking the two processes (partition and segmentation), while fractal dimension of the rescaling process is employed as an optimality metric. The new method is experimented with the two biggest river basins in Greece, namely Pinios and Acheloos river basins, using a digital elevation model as the only input dataset. The method proved successful in identifying a set of optimal scales for mapping elevation, slope, and flow accumulation. Deviation from the ideal conditions for implementing head/tail breaks are discussed. Implementation of the method requires an object-based analysis program and few common geospatial functions embedded in most GIS programs. The new method will assist in revealing underlying environmental processes in a variety of earth science fields and, thus, assist in land management decision-making and mapping generalization. Full article

The concept of urban area is complex and has been discussed for many years by several authors and organisations through different perspectives and methodological approaches. For administrative and comparison purposes statistical institutions, both at the national and international levels, classify territories according to a certain degree of urbanisation defining typologies from which indicators and certain public policies are applied. The purpose of this study is to discuss the relevance and suitability of different urban typologies. Through mapping and measuring the data of official documents, the urban dimension of Mainland Portuguese territory is discussed and its usefulness concerning the allocation of resources for promoting territorial cohesion is stressed. Results show the inadequacy of these classifications to inform planning actions, decision making, and to promote territorial policies. It also provides evidence of inaccuracies that distort the reading of the territorial reality of the case study. Full article

Offline stores are seriously challenged by online shops. To attract more customers to compete with online shops, the patterns of customer flows and their influence factors are important knowledge. To address this issue, we collected indoor positioning data of 534,641 and 59,160 customers in two shopping malls (i.e., Dayuecheng (DYC) in Beijing and Longhu (LH) in Chongqing, China) for one week, respectively. The temporal patterns of the customer flows show that (1) total customer flows are high on weekends and low midweek and (2) peak hourly flow is related to mealtimes for LH and only on weekdays for DYC. The difference in temporal patterns between the two malls may be attributed to the difference in their locations. The customer flows to stores reveal that the customer flows to clothing, food and general stores are the highest; specifically, in DYC, the order is clothing, food and general, while in LH, it is food, clothing and general. To identify the factors influencing customer flow, we applied linear regression to the inflow density of stores (customers per square meter) of two major classes (clothing and food stores), with 10 locational and social factors as independent variables. The results indicate that flow density is significantly influenced by store location, visibility (except for food stores in DYC) and reputation. Besides, the difference between the two store classes is that clothing stores are influenced by more convenience factors, including distance to an elevator and distance to the floor center (only for LH). Overall, the two shopping malls demonstrate similar customer flow patterns and influencing factors with some obvious differences also attributed to their layout, functions and locations. Full article

Natural resource management requires reliable and timely information available at local, regional, national, and global scales. Geo-informatics, by remote sensing, global navigation satellite systems, geographical information systems, and related technologies, provides information for natural resource management, environmental protection, and support related to sustainable development. Geo-informatics has proven to be a powerful technology for studying and monitoring natural resources as well as in generating predictive models, making it an important decision-making tool. The manuscripts included in this Special Issue focus on disciplines that advance the field of resource management in geomatics. The manuscripts showcased here provide different examples of challenges in resource management. Full article

WiFi-based indoor positioning methods have attracted extensive attention due to the wide installation of WiFi access points (APs). Recently, the WiFi standard was modified and introduced into a new two-way approach based on round trip time (RTT) measurement, which brings some changes for indoor positioning based on WiFi. In this work, we propose a WiFi RTT positioning method based on line of sight (LOS) identification and range calibration. Given the complexity of the indoor environment, we design a non-line of sight (NLOS) and LOS identification algorithm based on scenario recognition. The positioning scenario is recognized to assist NLOS and LOS distances identification, and gaussian process regression (GPR) is utilized to construct the scenario recognition model. Meanwhile, the calibration model for LOS distance is presented to correct the measuring distance and the scenario information is utilized to constrain the estimated position. When there is a positioning request, the positioning scenario is identified with the scenario recognition model, and LOS measuring distance is obtained based on the recognized scenario. The LOS range measurements are first calibrated and then utilized to estimate the position of the smartphone. Finally, the positioning scenario is used to constrain the estimation location to avoid it beyond the scenario. The experimental results show that the positioning effect of the proposed method is far better than that of the Least Squares (LS) algorithm, achieving a mean error (ME) of 0.862 m and root-mean-square error (RMSE) of 0.989 m. Full article

In this paper, we present an implementation of a research data management system that features structured data storage for spatio-temporal experimental data (environmental perception and navigation in the framework of autonomous driving), including metadata management and interfaces for visualization and parallel processing. The demands of the research environment, the design of the system, the organization of the data storage, and computational hardware as well as structures and processes related to data collection, preparation, annotation, and storage are described in detail. We provide examples for the handling of datasets, explaining the required data preparation steps for data storage as well as benefits when using the data in the context of scientific tasks. Full article

Recently, increasing amounts of multi-source geospatial data (raster data of satellites and textual data of meteorological stations) have been generated, which can play a cooperative and important role in many research works. Efficiently storing, organizing and managing these data is essential for their subsequent application. HBase, as a distributed storage database, is increasingly popular for the storage of unstructured data. The design of the row key of HBase is crucial to improving its efficiency, but large numbers of researchers in the geospatial area do not conduct much research on this topic. According the HBase Official Reference Guide, row keys should be kept as short as is reasonable while remaining useful for the required data access. In this paper, we propose a new row key encoding method instead of conventional stereotypes. We adopted an existing hierarchical spatio-temporal grid framework as the row key of the HBase to manage these geospatial data, with the difference that we utilized the obscure but short American Standard Code for Information Interchange (ASCII) to achieve the structure of the grid rather than the original grid code, which can be easily understood by humans but is very long. In order to demonstrate the advantage of the proposed method, we stored the daily meteorological data of 831 meteorological stations in China from 1985 to 2019 in HBase; the experimental result showed that the proposed method can not only maintain an equivalent query speed but can shorten the row key and save storage resources by 20.69% compared with the original grid codes. Meanwhile, we also utilized GF-1 imagery to test whether these improved row keys could support the storage and querying of raster data. We downloaded and stored a part of the GF-1 imagery in Henan province, China from 2017 to 2018; the total data volume reached about 500 GB. Then, we succeeded in calculating the daily normalized difference vegetation index (NDVI) value in Henan province from 2017 to 2018 within 54 min. Therefore, the experiment demonstrated that the improved row keys can also be applied to store raster data when using HBase. Full article

Understanding the relationship between the built environment and the risk of COVID-19 transmission is essential to respond to the pandemic. This study explores the relationship between the built environment and COVID-19 risk using the confirmed cases data collected in Hong Kong. Using the information on the residential buildings and places visited for each case from the dataset, we assess the risk of COVID-19 and explore their geographic patterns at the level of Tertiary Planning Unit (TPU) based on incidence rate (R1) and venue density (R2). We then investigate the associations between several built-environment variables (e.g., nodal accessibility and green space density) and COVID-19 risk using global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) models. The results indicate that COVID-19 risk tends to be concentrated in particular areas of Hong Kong. Using the incidence rate as an indicator to assess COVID-19 risk may underestimate the risk of COVID-19 transmission in some suburban areas. The GPR and GWPR models suggest a close and spatially heterogeneous relationship between the selected built-environment variables and the risk of COVID-19 transmission. The study provides useful insights that support policymakers in responding to the COVID-19 pandemic and future epidemics. Full article