ISPRS Int. J. Geo-Inf., Volume 8, Issue 6 (June 2019) – 47 articles

Flight conflict detection is fundamental to flight dispatch, trajectory planning, and flight safety control. An ever-increasing aircraft population and higher speeds, particularly the emergence of hypersonic/supersonic aircrafts, are challenging the timeliness and accuracy of flight conflict detection. Traditional trajectory conflict detection algorithms rely on traversing multivariate equations of every two trajectories, in order to yield the conflict result and involve extensive computation and high algorithmic complexity; these algorithms are often unable to provide the flight conflict solutions required quickly enough. In this paper, we present a novel, low-altitude flight conflict detection algorithm, based on the multi-level grid spatiotemporal index, that transforms the traditional trajectory-traversing multivariate conflict computation into a grid conflict state query of distributed grid databases. Essentially, this is a method of exchanging "storage space" for "computational time". First, we build the spatiotemporal subdivision and encoding model based on the airspace. The model describes the geometries of the trajectories, low-altitude obstacles, or dangerous fields and identifies the grid with grid codes. Next, we design a database table structure of the grid and create a grid database. Finally, we establish a multilevel grid spatiotemporal index, design a query optimization scheme, and examine the flight conflict detection results from the grid database. Experimental verification confirms that the computation efficiency of our algorithm is one order of magnitude higher than those of traditional methods. Our algorithm can perform real-time (dynamic/static) conflict detection on both individual aircraft and aircraft flying in formation with more efficient trajectory planning and airspace utilization. Full article

Accurately and precisely knowing the location of the vehicle is a critical requirement for safe and successful autonomous driving. Recent studies suggest that error for map-based localization methods are tightly coupled with the surrounding environment. Considering this relationship, it is therefore possible to estimate localization error by quantifying the representation and layout of real-world phenomena. To date, existing work on estimating localization error have been limited to using self-collected 3D point cloud maps. This paper investigates the use of pre-existing 2D geographic information datasets as a proxy to estimate autonomous vehicle localization error. Seven map evaluation factors were defined for 2D geographic information in a vector format, and random forest regression was used to estimate localization error for five experiment paths in Shinjuku, Tokyo. In the best model, the results show that it is possible to estimate autonomous vehicle localization error with 69.8% of predictions within 2.5 cm and 87.4% within 5 cm. Full article

The tremendous advance in information technology has promoted the rapid development of location-based services (LBSs), which play an indispensable role in people’s daily lives. Compared with a traditional LBS based on Point-Of-Interest (POI), which is an isolated location point, an increasing number of demands have concentrated on Region-Of-Interest (ROI) exploration, i.e., geographic regions that contain many POIs and express rich environmental information. The intention behind the POI is to search the geographical regions related to the user’s requirements, which contain some spatial objects, such as POIs and have certain environmental characteristics. In order to achieve effective ROI exploration, we propose an ROI top-k keyword query method that considers the environmental information of the regions. Specifically, the Word2Vec model has been introduced to achieve the distributed representation of POIs and capture their environmental semantics, which are then leveraged to describe the environmental characteristic information of the candidate ROI. Given a keyword query, different query patterns are designed to measure the similarities between the query keyword and the candidate ROIs to find the k candidate ROIs that are most relevant to the query. In the verification step, an evaluation criterion has been developed to test the effectiveness of the distributed representations of POIs. Finally, after generating the POI vectors in high quality, we validated the performance of the proposed ROI top-k query on a large-scale real-life dataset where the experimental results demonstrated the effectiveness of our proposals. Full article

Urban greenspace can provide physical and mental health benefits to residents, potentially reducing health inequalities associated with socioeconomic deprivation. The distribution of urban greenspace is an important social justice issue, and consequently is increasingly studied. However, there is little consistency between studies in terms of methods and definitions. There is no consensus on what comprises the most appropriate geographic units of analysis or how to capture residents’ experience of their neighbourhood, leading to the possibility of bias. Several complementary aspects of distribution equity have been defined, yet few studies investigate more than one of these. There are also alternative methods for measuring each aspect of distribution. All of these can lead to conflicting conclusions, which we demonstrate by calculating three aspects of equity for two units of aggregation and three neighbourhood sizes for a single study area. We make several methodological recommendations, including taking steps to capture the relevant neighbourhood as experienced by residents accurately as possible, and suggest that using small-area aggregations may not result in unacceptable levels of information loss. However, a consideration of the local context is critical both in interpreting individual studies and understanding differing results. Full article

In recent decades, 3D acquisition by laser scanning or digital photogrammetry has become one of the standard methods of documenting cultural heritage, because it permits one to analyze the shape, geometry, and location of any artefact without necessarily coming into contact with it. The recording of three-dimensional metrical data of an asset allows one to preserve and monitor, but also to understand and explain the history and cultural heritage shared. In essence, it constitutes a digital archive of the state of an artefact, which can be used for various purposes, be remodeled, or kept safely stored. With the introduction of 3D printing, digital data can once again take on material form and become physical objects from the corresponding mathematical models in a relatively short time and often at low cost. This possibility has led to a different consideration of the concept of virtual data, no longer necessarily linked to simple visual fruition. The importance of creating high-resolution physical copies has been reassessed in light of different types of events that increasingly threaten the protection of cultural heritage. The aim of this research is to analyze the critical issues in the production process of the replicas, focusing on potential problems in data acquisition and processing and on the accuracy of the resulting 3D printing. The metric precision of the printed model with 3D technology are fundamental for everything concerning geomatics and must be related to the same characteristics of the digital model obtained through the survey analysis. Full article

With the rapid development of global positioning technologies and the pervasiveness of intelligent mobile terminals, trajectory data have shown a sharp growth trend both in terms of data volume and coverage. In recent years, increasing numbers of LBS (location based service) applications have provided us with trajectory data services such as traffic flow statistics and user behavior pattern analyses. However, the storage and query efficiency of massive trajectory data are increasingly creating a bottleneck for these applications, especially for large-scale spatiotemporal query scenarios. To solve this problem, we propose a new spatiotemporal indexing method to improve the query efficiency of massive trajectory data. First, the method extends the GeoSOT spatial partitioning scheme to the time dimension and forms a global space–time subdivision scheme. Second, a novel multilevel spatiotemporal grid index, called the GeoSOT ST-index, was constructed to organize trajectory data hierarchically. Finally, a spatiotemporal range query processing method is proposed based on the index. We implement and evaluate the index in MongoDB. By comparing the range query efficiency and scalability of our index with those of the other two space–time composite indexes, we found that our approach improves query efficiency levels by approximately 40% and has better scalability under different data volumes. Full article

It is meaningful to analyze urban spatial structure by identifying urban subcenters, and many methods of doing so have been proposed in the published literature. Although these methods are widely applied, they exhibit obvious shortcomings that limit their further application. Therefore, it is of great value to propose a new urban subcenter identification method that can overcome these shortcomings. In this paper, we propose the density contour tree (DCT) method for detecting urban polycentric structures and their spatial distributions. Conceptually, this method is based on an analogy between urban spatial structure and terrain. The point-of-interest (POI) density is visualized as a continuous mathematical surface representing the urban terrain. Peaks represent the regions of the most frequent human activity, valleys represent regions with small population densities in the city, and slopes represent spatial changes in urban land-use intensity. Using this method, we have detected the urban “polycentric” structure of Beijing and determined the corresponding spatial relationships. In addition, several important properties of the urban centers have been identified. For example, Beijing has a typical urban polycentric structure with an urban center area accounting for 5.9% of the total urban area, and most of the urban centers in Beijing serve comprehensive functions. In general, the method and the results can serve as references for the later research on analyzing urban structure. Full article

The grouping of parcel data based on proximity is a pre-processing step of GIS and a key link of urban structure recognition for regional function discovery and urban planning. Currently, most literature abstracts parcels into points and clusters parcels based on their attribute similarity, which produces a large number of coarse granularity functional regions or discrete distribution of parcels that is inconsistent with human cognition. In this paper, we propose a novel parcel grouping method to optimise this issue, which considers both the urban morphology and the urban functional connectivity. Infiltration behaviours of urban components provide a basis for exploring the correlation between morphology mechanism and functional connectivity of urban areas. We measured the infiltration behaviours among adjacent parcels and concluded that the occurrence of infiltration behaviours often appears in the form of groups, which indicated the practical significance of parcel grouping. Our method employed two parcel morphology indicators: the similarity of the line segments and the compactness of the distribution. The line segment similarity was used to establish the adjacent relationship among parcels and the compactness was used to optimise the grouping result in obtain a satisfactory visual expression. In our study, constrained Delaunay triangulation, Hausdorff distance, and graph theory were employed to construct the proximity, delineate the parcel adjacency matrix, and implement the grouping of parcels. We applied this method for grouping urban parcel data of Beijing and verified the rationality of grouping results based on the quantified results of infiltration behaviours. Our method proved to take a good account of infiltration behaviours and satisfied human cognition, compared with a k-means++ method. We also presented a case using Xicheng District in Beijing to demonstrate the practicability of the method. The result showed that our method obtained fine-grained groups while ensuring functional regions-integrity. Full article

Many kinds of spatial–temporal data collected by transportation systems, such as user order systems or automated fare-collection (AFC) systems, can be discretized and converted into time-series data. With the technique of time-series data mining, certain travel-demand patterns of different areas in the city can be detected. This study proposes a data-mining model for understanding the patterns and regularities of human activities in urban areas from spatiotemporal datasets. This model uses a grid-based method to convert spatiotemporal point datasets into discretized temporal sequences. Time-series analysis technique dynamic time warping (DTW) is then used to describe the similarity between travel-demand sequences, while the clustering algorithm density-based spatial clustering of applications with noise (DBSCAN), based on modified DTW, is used to detect clusters among the travel-demand samples. Four typical patterns are found, including balanced and unbalanced cases. These findings can help to understand the land-use structure and commuting activities of a city. The results indicate that the grid-based model and time-series analysis model developed in this study can effectively uncover the spatiotemporal characteristics of travel demand from usage data in public transportation systems. Full article

Geographic information metadata provides a detailed description of geographic information resources. Well before digital data emerged, metadata were shown in the margins of paper maps to inform the reader of the name of the map, the scale, the orientation of the magnetic North, the projection used, the coordinate systems, the legend, and so on. Metadata were used to communicate practical information for the proper use of maps. When geographic information entered the digital era with geographic information systems, metadata was also collected digitally to describe datasets and the dataset collections for various purposes. Initially, metadata were collected and saved in digital files by data producers for their own specific needs. The sharing of geographic datasets that required producers to provide metadata with the dataset to guide proper use of the dataset—map scale, data sources, extent, datum, coordinate reference system, etc. Because of issues with sharing and no common understanding of metadata requirements, the need for metadata standardization was recognized by the geographic information community worldwide. The ISO technical committee 211 was created in 1994 with the scope of standardization in the field of digital geographic information to support interoperability. In the early years of the committee, standardization of metadata was initiated for different purposes, which culminated in the ISO 19115:2003 standard. Now, there are many ISO Geographic information standards that covers the various aspect of geographic information metadata. This paper traces an illustration of the development and evolution of the requirements and international standardization activities of geographic information metadata standards, profiles and resources, and how these attest to facilitating the discovery, evaluation, and appropriate use of geographic information in various contexts. Full article

Point-of-interest (POI) recommendations in location-based social networks (LBSNs) allow online users to discover various POIs for social activities occurring in the near future close to their current locations. Research has verified that people’s preferences regarding POIs are significantly affected by various internal and external contextual factors, which are therefore worth extensive study for POI recommendation. However, although psychological effects have also been demonstrated to be significantly correlated with an individual’s preferences, such effects have been largely ignored in previous studies on POI recommendation. For this paper, inspired by the famous memory theory in psychology, we were interested in whether memory-based preferences could be derived from users’ check-in data. Furthermore, we investigated how to incorporate these memory-based preferences into an effective POI recommendation scheme. Consequently, we refer to Ebbinghaus’s theory on memory, which describes the attenuation of an individual’s memory in the form of a forgetting curve over time. We first created a memory-based POI preference attenuation model and then adopted it to evaluate individuals’ check-ins. Next, we employed the memory-based values of check-ins to calculate the POI preference similarity between users in an LBSN. Finally, based on this memory-based preference similarity, we developed a novel POI recommendation method. We experimentally evaluated the proposed method on a real LBSN data set crawled from Foursquare. The results demonstrate that our method, which incorporates the proposed memory-based preference similarity for POI recommendation, significantly outperforms other methods. In addition, we found the best value of the parameter H in the memory-based preference model that optimizes the recommendation performance. This value of H implies that an individual’s memory usually has an effect on their daily travel choices for approximately 300 days. Full article

Boundary representation models are data models that represent the topology of a building or city model. This leads to an issue in combination with geometry, as the geometric model necessarily has an underlying topology. In order to allow topological queries to rely on the incidence graph only, a new notion of topological consistency is introduced that captures possible topological differences between the incidence graph and the topology coming from geometry. Intersection matrices then describe possible types of topological consistency and inconsistency. As an application, it is examined which matrices can occur as intersection matrices, and how matrices from topologically consistent data look. The analysis of CityGML data sets stored in a spatial database system then shows that many real-world data sets contain many topologically inconsistent pairs of polygons. It was observed that even if data satisfy the val3dity test, they can still be topologically inconsistent. On the other hand, it is shown that the ISO 19107 standard is equivalent to our notion of topological consistency. In the case when the intersection is a point, topological inconsistency occurs because a vertex lies on a line segment. However, the most frequent topological inconsistencies seem to arise when the intersection of two polygons is a line segment. Consequently, topological queries in present CityGML data cannot rely on the incidence graph only, but must always make costly geometric computations if correct results are to be expected. Full article

Reductions in fire frequency (RFF) are known to occur in the area adjacent to the rigid-boundary of simulated forest landscapes. Few studies, however, have removed those edge effected regions (EERs), and many others may, thus, have misinterpreted their simulated forest conditions within those unidentified edges. We developed three methods to detect and remove EERs with RFF and applied them to fire frequency maps of 2900 × 2900 grids developed using between 1000 and 1200 fire-year maps. The three methods employed different approaches: scanning, agglomeration, and division, along with the consensus of two and three of those methods. The detected EERs with RFF ranged in mean width from 5.9 to 17.3 km, and occupied 4.9 to 21.3% of the simulated landscapes. Those values are lower than the 40 km buffer width, which occupied 47.5% of the simulated landscape, used in a previous study in this area that based buffer width on length of the largest fire. The maximum width of the EER covaried with wind predominance, indicating it is not possible to prescribe a standard buffer width for all simulation studies. The three edge detection methods differ in their optimality, with the best results provided by a consensus of the three methods. We suggest that future landscape forest simulation studies should, to ensure their results near the rigid boundary are not misrepresentative, simulate an appropriately enlarged study area and then employ edge detection methods to remove the EERs with RFF. Full article

In this paper, we propose a data augmentation method for ship detection. Inshore ship detection using optical remote sensing imaging is a challenging task owing to an insufficient number of training samples. Although the multilayered neural network method has achieved excellent results in recent research, a large number of training samples is indispensable to guarantee the accuracy and robustness of ship detection. The majority of researchers adopt such strategies as clipping, scaling, color transformation, and flipping to enhance the samples. Nevertheless, these methods do not essentially increase the quality of the dataset. A novel data augmentation strategy was thus proposed in this study by using simulated remote sensing ship images to augment the positive training samples. The simulated images are generated by true background images and three-dimensional models on the same scale as real ships. A faster region-based convolutional neural network (Faster R-CNN) based on Res101netwok was trained by the dataset, which is composed of both simulated and true images. A series of experiments is designed under small sample conditions; the experimental results show that better detection is obtained with our data augmentation strategy. Full article

Gunshot detection technology (GDT) has been increasingly adopted by law enforcement agencies to tackle the problem of underreporting of crime via 911 calls for service, which undoubtedly affects the quality of crime mapping and spatial analysis. This article investigates the spatial and temporal patterns of gun violence by comparing data collected from GDT and 911 calls in Louisville, Kentucky. We applied hot spot mapping, near repeat diagnosis, and spatial regression approaches to the analysis of gunshot incidents and their associated neighborhood characteristics. We observed significant discrepancies between GDT data and 911 calls for service, which indicate possible underreporting of firearm discharge in 911 call data. The near repeat analysis suggests an increased risk of gunshots in nearby locations following an initial event. Results of spatial regression models validate the hypothesis of spatial dependence in frequencies of gunshot incidents and crime underreporting across neighborhoods in the study area, both of which are positively associated with proportions of African American residents, who are less likely to report a gunshot. This article adds to a growing body of research on GDT and its benefits for law enforcement activity. Findings from this research not only provide new insights into the spatiotemporal aspects of gun violence in urban areas but also shed light on the issue of underreporting of gun violence. Full article

In order to estimate the seismic vulnerability of a densely populated urban area, it would in principle be necessary to evaluate the dynamic behaviour of individual and aggregate buildings. These detailed seismic analyses, however, are extremely cost-intensive and require great processing time and expertise judgment. The aim of the present study is to propose a new methodology able to combine information and tools coming from different scientific fields in order to reproduce the effects of a seismic input in urban areas with known geological features and to estimate the entity of the damages caused on existing buildings. In particular, we present a new software called ABES (Agent-Based Earthquake Simulator), based on a Self-Organized Criticality framework, which allows to evaluate the effects of a sequence of seismic events on a certain large urban area during a given interval of time. The integration of Geographic Information System (GIS) data sets, concerning both geological and urban information about the territory of Avola (Italy), allows performing a parametric study of these effects on a real context as a case study. The proposed new approach could be very useful in estimating the seismic vulnerability and defining planning strategies for seismic risk reduction in large urban areas Full article

Spatial-interaction networks are an important factor in geography that could help in the exploration of both human spatial-temporal behavior and the structure of urban areas. This paper analyzes changes in the spatio-temporal characteristics of the Spatial-Interaction Networks of Beijing (SINB) in three consecutive steps. To begin with, we constructed 24 sequential snapshots of spatial population interactions on the basis of points of interest (POIs) collected from Dianping.com and various taxi GPS data in Beijing. Then, we used Jensen–Shannon distance and hierarchical clustering to integrate the 24 sequential network snapshots into four clusters. Finally, we improved the weighted k-core decomposition method by combining the complex network method and weighted distance in a geographic space. The results showed: (1) There are three layers in the SINB: a core layer, a bridge layer, and a periphery layer. The number of places greatly varies, and the SINB show an obvious hierarchical structure at different periods. The core layer contains fewer places that are between the Second and Fifth Ring Road in Beijing. Moreover, spatial distribution of places in the bridge layer is always in the same location as that of the core layer, and the quantity in the bridge layer is always superior to that in the core layer. The distributions of places in the periphery layer, however, are much greater and wider than the other two layers. (2) The SINB connected compactly over time, bearing much resemblance to a small-world network. (3) Two patterns of connection, each with different connecting ratios between layers, appear on weekdays and weekends, respectively. Our research plays a vital role in understanding urban spatial heterogeneity, and helps to support decisions in urban planning and traffic management. Full article

Analysis of trajectory such as detection of an outlying trajectory can produce inaccurate results due to the existence of noise, an outlying point-locations that can change statistical properties of the trajectory. Some trajectories with noise are repairable by noise filtering or by trajectory-simplification. We herein propose the application of a trajectory-simplification approach in both batch and streaming environments, followed by benchmarking of various outlier-detection algorithms for detection of outlying trajectories from among simplified trajectories. Experimental evaluation in a case study using real-world trajectories from a shipyard in South Korea shows the benefit of the new approach. Full article

This study describes the integration and analysis of travel smart card data (SCD) with points of interest (POIs) from social media for a case study in Shenzhen, China. SCD ticket price with tap-in and tap-out times was used to identify different groups of travellers. The study examines the temporal variations in mobility, identifies different groups of users and characterises their trip purpose and identifies sub-groups of users with different travel patterns. Different groups were identified based on their travel times and trip costs. The trip purpose associated with different groups was evaluated by constructing zones around metro station locations and identifying the POIs in each zone. Each POI was allocated to one of six land use types, and each zone was allocated a set of land use weights based on the number of POI check-ins for the POIs in that zone. Trip purpose was then inferred from trip time linked to the land use at the origin and destination zones using a novel “land use change rate” measure. A cluster analysis was used to identify sub-groups of users based on individual temporal travel patterns, which were used to generate a novel “boarding time profile”. The results show how different groups of users can be identified and the differences in trip times and trip purpose quantified between and within groups. Limitations of the study are discussed and a number of areas for further work identified, including linking to socioeconomic data and a deeper consideration of the timestamps of POI check-ins to support the inference of dynamic and multiple land uses at one location. The methods and metrics developed by this research use social media POI data to semantically contextualise information derived from the SCD and to overcome the drawbacks and limitations of traditional travel survey data. They are novel and generalizable to other studies. They quantify spatiotemporal mobility patterns for different groups of travellers and infer how their purposes of their journeys change through the day. In so doing, they support a more nuanced and detailed view of who, where, when and why people use city spaces. Full article

Documenting the paleontological process includes data produced by different techniques and protocols, which are used by paleontologists to prospect and eventually find a new fossil. Nowadays, together with the aforementioned data, a great amount of information is also available in terms of georeferenced systems, including contextual as well as descriptive information. However, the use of this information into a model capable of recognizing similarities and differences is still an open issue within the Natural Heritage community. From the software engineering field, software product lines are models that focus on reusing common assets, in such a way that new software developments are only concern on differentiation relying on already modeled (and implemented) systems. This synergy leads us to apply our taxonomy-oriented domain analysis for Software Product Line (SPL) development, when building systems for documenting the paleontological process. In this paper, we introduce the approach for building such software systems, and illustrate its use through a case study in North Patagonia. Findings show promissory results in terms of reuse. Full article

Geographically weighted regression (GWR) is a spatial statistical technique that recognizes that traditional ‘global’ regression models may be limited when spatial processes vary with spatial context. GWR captures process spatial heterogeneity by allowing effects to vary over space. To do this, GWR calibrates an ensemble of local linear models at any number of locations using ‘borrowed’ nearby data. This provides a surface of location-specific parameter estimates for each relationship in the model that is allowed to vary spatially, as well as a single bandwidth parameter that provides intuition about the geographic scale of the processes. A recent extension to this framework allows each relationship to vary according to a distinct spatial scale parameter, and is therefore known as multiscale (M)GWR. This paper introduces mgwr, a Python-based implementation of MGWR that explicitly focuses on the multiscale analysis of spatial heterogeneity. It provides novel functionality for inference and exploratory analysis of local spatial processes, new diagnostics unique to multi-scale local models, and drastic improvements to efficiency in estimation routines. We provide two case studies using mgwr, in addition to reviewing core concepts of local models. We present this in a literate programming style, providing an overview of the primary software functionality and demonstrations of suggested usage alongside the discussion of primary concepts and demonstration of the improvements made in mgwr. Full article

On a global scale, with the acceleration of urbanization and the continuous expansion of cities, the problem of urban flooding has become increasingly prominent. An increasing number of experts and scholars have begun to focus on this phenomenon and build corresponding models to solve the problem. The storm water management model 5 (SWMM5) is a dynamic rainfall-runoff simulation model developed by the US Environmental Protection Agency (EPA); this model simulates urban flooding and drainage well and is widely favored by researchers. However, the use of SWMM5 is relatively cumbersome and limited by the operational platform, and these factors hinder the further promotion and sharing of SWMM5. Based on the OpenGMS platform, this study first encapsulates, deploys, and publishes SWMM5 and further builds the Web-SWMM system for the model. With Web-SWMM, the user can conveniently use network data resources online and call SWMM5 to carry out calculations, avoiding the difficulties caused by the localized use of SWMM5 and enabling the sharing and reuse of SWMM5. Full article

In the dual context of coastal hazard intensification and the growing number of stakes exposed to these hazards, coastal observatories are in demand to provide a structured framework dedicated to long-term monitoring. This article describes the drone-based photogrammetry monitoring performed since 2006 on Porsmilin Beach (Brittany, France) in the framework of the DYNALIT (Littoral and Coastline Dynamics) observatory, focusing on data quality and the consistency of long-term time series under the influence of multiple technological evolutions: Unmanned Aerial Vehicles (UAV) platforms with the arrival of electric multirotor drones, processing tools with the development of structure-from-motion (SfM) photogrammetry, and operational modes of survey. A study case is presented to show the potential of UAV monitoring to study storm impacts and beach resilience. The relevance of high-accuracy monitoring is also highlighted. With the current method, an accuracy of 3 cm can be achieved on the digital elevation model (DEM) and the orthophotograph. The question of the representativity and frequency of DEM time points is raised. Full article

Based on geographic information system (GIS) technology in conjunction with two methods for assessing landslide susceptibility (LS)—namely, a method using experts’ knowledge and experience, and a mathematical/statistical method—the LS of southern Anhui, China is assessed using an analytic hierarchy process (AHP) via an AHP-weighted information content method. Landslide-affecting factors are categorized into three main types and 10 subtypes. The values of spatial characteristics of the landslide-affecting factors are obtained using GIS technology. The AHP method is then employed to compare the importance and weights of landslide-affecting factors. The information content method is used to convert the measured values of the landslide-affecting factors in the study area to data reflecting regional stability. The closeness of the relationships between the classification levels of each landslide-affecting factor and landslide occurrence are calculated. The LS of the study area is assessed using the proposed method. The LS assessment shows that high LS, relatively high LS, moderate LS, relatively low LS and low LS regions account for 21.3%, 20.6%, 20.1%, 11.7% and 26.3% of the study area, respectively. Finally, the accuracy of the LS assessment results is analyzed using two methods: the assessment, including an analysis of random landslide sites for the validating models; and the area below a receiver operating characteristic (ROC) curve of area under curve (AUC) value. The results show that the proportion of landslide sites in the regions of each LS level determined using the AHP-weighted information content method increases as the LS level increases, and that the accuracies of the AHP-weighted information content method were 8.1% and 5.7% higher than those of the AHP method and information content method, respectively. Full article

A continued shift of human mobility towards sustainable and active mobility modes is a major concern for society in order to reduce the human contribution to climate change as well as to improve liveability and health in urban environments. For this change to succeed, non-motorized modes of transport need to become more attractive. Cycling can play a substantial role for short to medium distances, but perceived safety and stress levels are still major concerns for cyclists. Therefore, a quantitative assessment of cyclists’ stress sensations constitutes a valuable input for urban planning and for optimized routing providing low-stress routes. This paper aims to investigate stress sensations of cyclists through quantifying physiological measurements and their spatial correlation as an intersubjective indicator for perceived bikeability. We developed an automated workflow for stress detection and aggregation, and validated it in a case study in the city of Salzburg, Austria. Our results show that measured stress generally matches reported stress perception and can thus be considered a valuable addition to mobility planning processes. Full article

The results of road congestion detection can be used for the rational planning of travel routes and as guidance for traffic management. The trajectory data of moving objects can record their positions at each moment and reflect their moving features. Utilizing trajectory mining technology to effectively identify road congestion locations is of great importance and has practical value in the fields of traffic and urban planning. This paper addresses the issue by proposing a novel approach to detect road congestion locations based on trajectory stay-place clustering. First, this approach estimates the speed status of each time-stamped location in each trajectory. Then, it extracts the stay places of the trajectory, each of which is denoted as a seven-tuple containing information such as starting and ending time, central coordinate, average direction difference, and so on. Third, the time-stamped locations included in stay places are partitioned into different stay-place equivalence classes according to the timestamps. Finally, stay places in each equivalence class are clustered to mine the congestion locations of multiple trajectories at a certain period of time. Visual representation and experimental results on real-life cab trajectory datasets show that the proposed approach is suitable for the detection of congestion locations at different timestamps. Full article

Over the last 25 years, the potential benefits of sharing and reusing geographic information for national development programs have led many countries to establish their own national spatial data infrastructure (NSDI). Indonesia is among the early adopters; however, despite its early introduction of NSDI concepts, the implementation has encountered some difficulties. The main objective of this study is to understand the evolution of NSDI development in Indonesia and then develop strategic directions for future implementation. We first characterized periods of current NSDI development based on the use of technology and identified problems that have occurred. To understand the problems’ causes, we conducted a stakeholder analysis utilizing questionnaire surveys. In addition, we analyzed cost components allocated for NSDI operation. The results showed that stakeholders’ low participation was caused by insufficient technological, financial, and human resources to manage geographic information. Subsequently, a strengths-weaknesses-opportunities-threats analysis was conducted to determine proposed directions of the institutional and technical aspects. This research provides the framework for analyzing NSDI evolution in one country—Indonesia. The proposed directions can be applied in other countries to ensure effective NSDI development and implementation. Full article

South Korea has witnessed a remarkable decline in birth rates in the last few decades. Although there has been a large volume of literature exploring the determinants of low fertility in South Korea, studies on spatial variations in fertility are scarce. This study compares the Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models to investigate the potential role of the spatially heterogeneous response of the total fertility rate (TFR) to sociodemographic factors. The study finds that the relationships between sociodemographic factors and TFRs in South Korea vary across 252 sub-administrative areas in terms of both magnitude and direction. This study therefore demonstrates the value of using spatial analysis for providing evidence-based local-population policy options in pursuit of a fertility rebound in South Korea. Full article

We show that training activities conducted through the National Aeronautics and Space Administration (NASA)’s Applied Remote-Sensing Training (ARSET) program led to a significant increase in remote-sensing data use for decision-making. Our findings are based on survey data collected from 1041 ARSET participants from 117 countries who attended ARSET trainings between 2013 and 2016. To assess the impact of the ARSET program, we analyzed changes in three metrics. Results show that 83% of all respondents increased their knowledge of remote-sensing data products at least moderately, 79% increased their ability to access data, and 73% increased their ability to make decisions. We also examined how respondents are using remote-sensing data across 40 specific work tasks ranging from research to decision support applications. More than 50% of respondents reported an increase in data use for all except two of the tasks. ARSET will use these findings, together with participant data on future training needs, to set future directions for the program. Full article

The idea behind patrol activity is that police officers should be the persons best acquainted with the events and people in their patrol area. This implies that they should have access to relevant data and information (e.g., where and how to pay attention, when and how crimes are committed) in order to effectively perform their police duties. To what extent their perceptions of the places prone to crime (hotspots) are accurate and what the implications are for police efficiency if they are incorrect is an important question for law enforcement officials. This paper presents the results of a study on police practice in Serbia. The study was conducted on a sample of 54 police officers and aimed to determine the accuracy of the perception of residential burglary hotspots and to evaluate the ways police officers are informed about crimes. The results of the study have shown that the situational awareness of police officers is not at a desired level, with ineffective dissemination of relevant data and information as one of the possible reasons. Full article