ISPRS Int. J. Geo-Inf., Volume 6, Issue 2 (February 2017) – 25 articles

Recognition of transportation modes can be used in different applications including human behavior research, transport management and traffic control. Previous work on transportation mode recognition has often relied on using multiple sensors or matching Geographic Information System (GIS) information, which is not possible in many cases. In this paper, an approach based on ensemble learning is proposed to infer hybrid transportation modes using only Global Position System (GPS) data. First, in order to distinguish between different transportation modes, we used a statistical method to generate global features and extract several local features from sub-trajectories after trajectory segmentation, before these features were combined in the classification stage. Second, to obtain a better performance, we used tree-based ensemble models (Random Forest, Gradient Boosting Decision Tree, and XGBoost) instead of traditional methods (K-Nearest Neighbor, Decision Tree, and Support Vector Machines) to classify the different transportation modes. The experiment results on the later have shown the efficacy of our proposed approach. Among them, the XGBoost model produced the best performance with a classification accuracy of 90.77% obtained on the GEOLIFE dataset, and we used a tree-based ensemble method to ensure accurate feature selection to reduce the model complexity. Full article

This paper describes the first results of an image recognition based location (IRBL) for a mobile application focusing on the procedure to generate a database of range images (RGB-D). In an indoor environment, to estimate the camera position and orientation, a prior spatial knowledge of the surroundings is needed. To achieve this objective, a complete 3D survey of two different environments (Bangbae metro station of Seoul and the Electronic and Telecommunications Research Institute (ETRI) building in Daejeon, Republic of Korea) was performed using a LiDAR (Light Detection and Ranging) instrument, and the obtained scans were processed to obtain a spatial model of the environments. From this, two databases of reference images were generated using specific software realised by the Geomatics group of Politecnico di Torino (ScanToRGBDImage). This tool allows us to generate synthetically different RGB-D images centred in each scan position in the environment. Later, the external parameters (X, Y, Z, ω, $\mathsf{\varphi }$ , and κ) and the range information extracted from the retrieved database images are used as reference information for pose estimation of a set of acquired mobile pictures in the IRBL procedure. In this paper, the survey operations, the approach for generating the RGB-D images, and the IRB strategy are reported. Finally, the analysis of the results and the validation test are described. Full article

The spatial patterns of landscapes are complex. Highly dense urban centers are not just mirrowed in a dichotomic sense by rural environments; landscapes are a spatially variable continuum. In this logic, nation-states (or any political or administrative unit) spatially integrate different types and physical appearances of land cover. Understanding regions in the sense that similar physical characteristics may construct alternative (natural) spatial entities which may sub-divide or cross-over adminstrative boundaries allows us to overcome common map projections. However, which indicators and which regional logics define and delimit regions is conceptually vague. With this paper we aim to add an empirical study to identify regional phenomena in Europe. To do so, we take advantage of a new data set from remote sensing, the Global Urban Footprint. It features European-wide consistent spatial information on settlement patterns. We use density and distribution of settlements as indicators for delimiting regions by similar characteristics. Our methodological approach classifies urban nodes (by settlement density and size), spans an unbounded soft space by the classification of spatial connectivity between nodes (by continuous settlement) and maps territorial entities (by density around nodes); the approach is following a space of place logic. From a geographic perspective we identify uneven development across Europe. The corridor streching from England via the Benelux areas via Germany, Switzerland, France to Northern Italy is mapped as the European backbone; however, new focal areas such as, e.g., towards eastern Europe are also detected. Applying a plausibility check reveals that the proxy settlement pattern corresponds well with regional conceptions presented in other studies. Full article

The Worldwide Sensor Web has been applied for monitoring the physical world with spatial and temporal scales that were impossible in the past. With the development of sensor technologies and interoperable open standards, sensor webs generate tremendous volumes of priceless data, enabling scientists to observe previously unobservable phenomena. With its powerful monitoring capability, the sensor web is able to capture time-critical events and provide up-to-date information to support decision-making. In order to harvest the full potential of the sensor web, efficiently processing sensor web data and providing timely notifications are necessary. Therefore, we aim to design a software component applying the publish/subscribe communication model for the sensor web. However, as sensor web data are geospatial in nature, existing topological operators are inefficient when processing a large number of geometries. This paper presents the Aggregated Hierarchical Spatial Model (AHS model) to efficiently determine topological relationships between sensor data and predefined query objects. By using a predefined hierarchical spatial framework to index geometries, the AHS model can match new sensor data with all subscriptions in a single process to improve the query performance. Based on our evaluation results, the query latency of the AHS model increases 2.5 times more slowly than that of PostGIS. As a result, we believe that the AHS model is able to more efficiently process topological operators in a sensor web publish/subscribe system. Full article

The integration of Building Information Modeling (BIM) and Geographic Information System (GIS) has been identified as a promising but challenging topic to transform information towards the generation of knowledge and intelligence. Achievement of integrating these two concepts and enabling technologies will have a significant impact on solving problems in the civil, building and infrastructure sectors. However, since GIS and BIM were originally developed for different purposes, numerous challenges are being encountered for the integration. To better understand these two different domains, this paper reviews the development and dissimilarities of GIS and BIM, the existing integration methods, and investigates their potential in various applications. This study shows that the integration methods are developed for various reasons and aim to solve different problems. The parameters influencing the choice can be summarized and named as “EEEF” criteria: effectiveness, extensibility, effort, and flexibility. Compared with other methods, semantic web technologies provide a promising and generalized integration solution. However, the biggest challenges of this method are the large efforts required at early stage and the isolated development of ontologies within one particular domain. The isolation problem also applies to other methods. Therefore, openness is the key of the success of BIM and GIS integration. Full article

With the continuous progress of human production and life, air quality has become the focus of attention. In this paper, Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces were taken as the study area, where there are 58 air quality monitoring stations from which daily and monthly data are obtained. Firstly, the temporal characteristics of the air quality index (AQI) are explored. Then, the spatial distribution of the AQI is mapped by the inverse distance weighted (IDW) method, the ordinary kriging (OK) method and the Bayesian maximum entropy (BME) method. Additionally, cross-validation is utilized to evaluate the mapping results of these methods with two indexes: mean absolute error and root mean square interpolation error. Furthermore, the correlation analysis of meteorological factors, including precipitation anomaly percentage, precipitation, mean wind speed, average temperature, average water vapor pressure and average relative humidity, potentially affecting the AQI was carried out on both daily and monthly scales. In the study area and period, AQI shows a clear periodicity, although overall, it has a downward trend. The peak of AQI appeared in November, December and January. BME interpolation has a higher accuracy than OK. IDW has the maximum error. Overall, the AQI of winter (November), spring (February) is much worse than summer (May) and autumn (August). Additionally, the air quality has improved during the study period. The most polluted areas of air quality are concentrated in Beijing, the southern part of Tianjin, the central-southern part of Hebei, the central-northern part of Henan and the western part of Shandong. The average wind speed and average relative humidity have real correlation with AQI. The effect of meteorological factors such as wind, precipitation and humidity on AQI is putative to have temporal lag to different extents. AQI of cities with poor air quality will fluctuate greater than that of others when weather changes and has higher correlation with meteorological factors. Full article

The increased feature space available in object-based classification environments (e.g., extended spectral feature sets per object, shape properties, or textural features) has a high potential of improving classifications. However, the availability of a large number of derived features per segmented object can also lead to a time-consuming and subjective process of optimizing the feature subset. The objectives of this study are to evaluate the effect of the advanced feature selection methods of popular supervised classifiers (Support Vector Machines (SVM) and Random Forest (RF)) for the example of object-based mapping of an agricultural area using Unmanned Aerial Vehicle (UAV) imagery, in order to optimize their usage for object-based agriculture pattern recognition tasks. In this study, several advanced feature selection methods were divided into both types of classifiers (SVM and RF) to conduct further evaluations using five feature-importance-evaluation methods and three feature-subset-evaluation methods. A visualization method was used to measure the change pattern of mean classification accuracy with the increase of features used, and a two-tailed t-test was used to determine the difference between two population means for both repeated ten classification accuracies. This study mainly contribute to the uncertainty analysis of feature selection for object-based classification instead of the per-pixel method. The results highlight that the RF classifier is relatively insensitive to the number of input features, even for a small training set size, whereby a negative impact of feature set size on the classification accuracy of the SVM classifier was observed. Overall, the SVM Recursive Feature Elimination (SVM-RFE) seems to be an appropriate method for both groups of classifiers, while the Correlation-based Feature Selection (CFS) is the best feature-subset-evaluation method. Most importantly, this study verified that feature selection for both classifiers is crucial for the evolving field of Object-based Image Analysis (OBIA): It is highly advisable for feature selection to be performed before object-based classification, even though an adverse impact could sometimes be observed from the wrapper methods. Full article

Due to the incomprehensive and inconsistent description of spatial and temporal information for city data observed by sensors in various fields, it is a great challenge to share the massive, multi-source and heterogeneous interdisciplinary instant point observation data resources. In this paper, a spatio-temporal enhanced metadata model for point observation data sharing was proposed. The proposed Data Meta-Model (DMM) focused on the spatio-temporal characteristics and formulated a ten-tuple information description structure to provide a unified and spatio-temporal enhanced description of the point observation data. To verify the feasibility of the point observation data sharing based on DMM, a prototype system was established, and the performance improvement of Sensor Observation Service (SOS) for the instant access and insertion of point observation data was realized through the proposed MongoSOS, which is a Not Only SQL (NoSQL) SOS based on the MongoDB database and has the capability of distributed storage. For example, the response time of the access and insertion for navigation and positioning data can be realized at the millisecond level. Case studies were conducted, including the gas concentrations monitoring for the gas leak emergency response and the smart city public vehicle monitoring based on BeiDou Navigation Satellite System (BDS) used for recording the dynamic observation information. The results demonstrated the versatility and extensibility of the DMM, and the spatio-temporal enhanced sharing for interdisciplinary instant point observations in smart cities. Full article

Association rule mining on oil and gas data has recently been successfully used to help understand reservoirs; therefore, the visualization and understanding of the discovered association rules based on well locations and subsequent predictions based on the applicable areas of the rules are important. In this paper, two visualization methods—point- and surface-based geovisualization—are proposed for association rules from oil and gas well data. The point-based method represents association rules based on well locations, and the surface-based method represents potentially applicable areas through spatial interpolation and visualization. A case study has been carried out on a real cold production oil well dataset in western Alberta, Canada, and, the results illustrate the feasibility of the proposed geovisualization methods. Full article

Fast and reliable ambiguity resolution (AR) has been a continuing challenge for real-time precise positioning based on dual-frequency Global Navigation Satellite Systems (GNSS) carrier phase observation. New GNSS systems (i.e., GPS modernization, BDS (BeiDou Navigation Satellite System), GLONASS (Global Navigation Satellite System), and Galileo) will provide multiple-frequency signals. The GNSS multiple-constellation and multiple-frequency signals are expected to bring great benefits to AR. A new GNSS single-epoch AR method for a short-range baseline based on triple-frequency signals is developed in this study. Different from most GNSS multiple-constellation AR methods, this technique takes advantage of the triple-frequency signals and robust estimation as much as possible. In this technique, the double difference (DD) AR of the triple-frequency observations is achieved in the first step. Second, the triple-frequency carrier phase observations with fixed ambiguities are used with the dual-frequency carrier phase observations to estimate their ambiguity. Finally, to realize reliable GNSS single-epoch AR, robust estimation is involved. The performance of the new technique is examined using 24 hours of GPS/GLONASS/BDS observation collected from a short-range baseline. The results show that single-epoch AR of the GNSS signals can be realized using this new technique. Moreover, the AR of BDS Geostationary Earth Orbit (GEO) satellites’ observations is easier than are those of the Medium Earth Orbit (MEO) and Inclined Geosynchronous Satellite Orbit (IGSO) satellites’ observations. Full article

General declines in ecosystem service values (ESV) are acknowledged worldwide; however, rather few studies have quantitatively analyzed the interrelationship between changing rural settlements and values of ecosystem services. This study used the county of Tongyu in West Jilin Province, China, as a case study to analyze how changing rural settlements impact the values of ecosystem services on surrounding land in the eco-fragile areas during 1997–2010. Quantitative analytical techniques mainly include the buffer analysis and an ecosystem services valuation. The results show that as the area of rural settlements increased in 1997–2010, the structure of land ecosystems had changed significantly during this time period, causing a change in ESV that was observed with a decline by 1.87 billion yuan and above 20%. The degradation of grasslands, wetlands, and water areas, as well as the farmland reclamation, were the main drivers of the decreases in ESV. The effects of the increased rural settlements on the distribution and variation of ESV were larger than the decreased rural settlements, especially the new rural settlements whose effect was largest, and the effect of changing rural settlements on the values of ecosystem services on the surrounding land was significant in proximity to these settlements. In conclusion, the effects of rural settlement evolution on the natural environment were obvious in the eco-fragile areas. Thus the encroachment of rural settlements still requires enhanced supervision in land management practices, and the scale and spatial distribution of rural settlements should be befittingly allocated in the eco-fragile areas to reduce the disturbance to the ecosystem. Full article

Integrated Environment Modeling (IEM) has become more and more important for environmental studies and applications. IEM systems have also been extended from scientific studies to much wider practical application situations. The quality and improved efficiency of IEM systems have therefore become increasingly critical. Although many advanced and creative technologies have been adopted to improve the quality of IEM systems, there is scarcely any formal method for evaluating and improving them. This paper is devoted to proposing a formal method to improve the quality and the developing efficiency of IEM systems. Two primary contributions are made. Firstly, a formal framework for IEM is proposed. The framework not only reflects the static and dynamic features of IEM but also covers different views from variant roles throughout the IEM lifecycle. Secondly, the formal operational semantics corresponding to the former model of the IEM is derived in detail; it can be used as the basis for aiding automated integrated modeling and verifying the integrated model. Full article

With the rapid development of urban transportation, people urgently need high-precision and up-to-date road maps. At the same time, people themselves are an important source of road information for detailed map construction, as they can detect real-world road surfaces with GPS devices in the course of their everyday life. Big trace data makes it possible and provides a great opportunity to extract and refine road maps at relatively low cost. In this paper, a new refinement method is proposed for incremental road map construction using big trace data, employing Delaunay triangulation for higher accuracy during the GPS trace stream fusion process. An experiment and evaluation were carried out on the GPS traces collected by taxis in Wuhan, China. The results show that the proposed method is practical and improves upon existing incremental methods in terms of accuracy. Full article

The spatial variation of geographical phenomena is a classical problem in spatial data analysis and can provide insight into underlying processes. Traditional exploratory methods mostly depend on the planar distance assumption, but many spatial phenomena are constrained to a subset of Euclidean space. In this study, we apply a method based on a hierarchical Bayesian model to analyse the spatial variation of network-constrained phenomena represented by a link attribute in conjunction with two experiments based on a simplified hypothetical network and a complex road network in Shenzhen that includes 4212 urban facility points of interest (POIs) for leisure activities. Then, the methods named local indicators of network-constrained clusters (LINCS) are applied to explore local spatial patterns in the given network space. The proposed method is designed for phenomena that are represented by attribute values of network links and is capable of removing part of random variability resulting from small-sample estimation. The effects of spatial dependence and the base distribution are also considered in the proposed method, which could be applied in the fields of urban planning and safety research. Full article

Smartphones have become indispensable in our daily lives. Their various embedded sensors have inspired innovations in mobile applications—especially for indoor navigation. However, the accuracy, reliability and generalizability of navigation all continue to struggle in environments lacking a Global Navigation Satellite System (GNSS). Pedestrian Dead Reckoning (PDR) is a popular method for indoor pedestrian navigation. Unfortunately, due to its fundamental principles, even a small navigation error will amplify itself, step by step, generally leading to the need for supplementary resources to maintain navigation accuracy. Virtually all mobile devices and most robots contain a basic camera sensor, which has led to the popularity of image-based localization, and vice versa. However, all of the image-based localization requires continuous images for uninterrupted positioning. Furthermore, the solutions provided by either image-based localization or a PDR are usually in a relative coordinate system. Therefore, this research proposes a system, which uses space resection-aided PDR with geo-referenced images of a previously mapped environment to enable seamless navigation and solve the shortcomings of PDR and image-based localization, and evaluates the performance of space resection with different assumptions using a smartphone. The indoor mobile mapping system (IMMS) is used for the effective production of geo-referenced images. The preliminary results indicate that the proposed algorithm is suitable for universal pedestrian indoor navigation, achieving the accuracy required for commercial applications. Full article

Geo-tagged tweets provide useful implications for studies in human geography, urban science, location-based services, targeted advertising, and social network. This research aims to discover the patterns and mobility of Twitter users by analyzing the spatial and temporal dynamics in their tweets. Geo-tagged tweets are collected over a period of six months for four US Midwestern college cities: (1) West Lafayette, IN; (2) Bloomington, IN; (3) Ann Arbor, MI; (4) Columbus, OH. Various analytical and statistical methods are used to reveal the spatial and temporal patterns of tweets, and the tweeting behaviors of Twitter users. It is discovered that Twitter users are most active between 9:00 pm and 11:00 pm. In smaller cities, tweets aggregate at campuses and apartment complexes, while tweets in residential areas of bigger cities make up the majority of tweets. We also found that most Twitter users have two to four places of frequent visits. The mean mobility range of frequent Twitter users is linearly correlated to the size of the city, specifically, about 40% of the city radius. The research therefore confirms the feasibility and promising future for using geo-tagged microblogging services such as Twitter to understand human behavior patterns and carry out other geo-social related studies. Full article

The automated development of spatial analysis workflows is one of the envisioned benefits of Web services that provide geoprocessing functionality. Automated workflow development requires the means to translate a user objective into a series of geographic information system (GIS) operations and to evaluate the match between data and operations. Even though full automation is yet out of reach, users benefit from formalized knowledge about operations that is available during workflow development. This article presents user support during workflow development based on a recent approach to extended operation descriptions. User support thereby focuses on the discovery of operations across GIS tools and the validation of chains of spatial analysis operations. The required knowledge about operations is stored in a knowledge base, which builds on an approach called geooperators and extends the geooperator approach with a data-type ontology for describing the interfaces of geooperators and for expressing constraints of geooperator inputs. The advantages of the knowledge base are demonstrated for the construction of a multi-criteria decision making workflow. This workflow contains a set of pre-processing tasks for the input datasets and eventually the calculation of a cost distance raster. A critical discussion of the complexity of the knowledge base and a comparison with existing approaches complement this contribution. Full article

The prevention and correct management of natural disaster event sequences play a key role in saving human lives. The availability of embedded and mobile smart computing systems opens new roads for the management of land and infrastructures by civil protection operators. To date, research has explored the use of social networks for the management of disasters connected to meteorological/hydrogeological events or earthquakes, but without emphasis on the importance of an integrated system. The main feature of the Whistland system proposed in this paper is to make synergistic use of augmented reality (AR), crowd-mapping (CM), social networks, the Internet of Things (IoT) and wireless sensor networks (WSN) by exploiting technologies and frameworks of Web 2.0 and GIS 2.0 to make informed decisions about the chain of events. The Whistland system is composed of a geo-server, a mobile application with AR and an analytics dashboard. The geo-server acts as the hub of the sensor and social networks. The abstracted concept in this sense is the transformation of the user domain into “intelligent sensors” for the whole scope of crisis management. The social network integration is made through an efficient pointer-like mechanism that keeps the storage requirement low through a mobile application based on an augmented reality engine and provides qualitative information that sensors are unable to capture. Real-time analyses, geo-searches and the capability to examine event histories with an augmented reality engine all help the stakeholders to understand better the state of the resources under observation/monitoring. The system has been extensively tested in the programmed maintenance of river basins, where it is necessary to log maintenance activities in order to keep the riverbank clean: a significant use-case in many countries affected by hydro-geological instability. Full article

Automated external defibrillators (AEDs) are portable devices that defibrillate and diagnose sudden-cardiac-arrest patients. Therefore, AEDs are widely installed in public places such as airports, schools, sport complexes, etc., and the installation of AEDs is required by law in these places. However, despite their usefulness, AEDs are mostly installed indoors with limited coverage outdoors. Hence, this study conducts research in the placement of AEDs in outdoor locations. This study considers a complicated urban environment using a pedestrian network dataset and network barriers. We draw on the Teitz and Bart’s (1968) heuristic method that was built in the location-allocation solver in ArcMap. The results of this study found that a total of 455 AEDs, including 227 pre-installed AEDs, could be placed in the study area, thus providing an additional 228 devices. Compared with 10 different installation methods that were set as experimental groups, our test results found that additional installations were able to cover 10% to 30% more actual out-of-hospital cardiac-arrest cases. The main contribution of this study is the proposal of a new method in locating AEDs in optimal areas while considering complicated urban environments. We predict that the cardiac-arrest-related mortality rate would be reduced through implementing the findings of this study. Full article

Services provided by public facilities are essential to people’s lives and are closely associated with human mobility. Traditionally, public facility access characteristics, such as accessibility, equity issues and service areas, are investigated mainly based on static data (census data, travel surveys and particular records, such as medical records). Currently, the advent of big data offers an unprecedented opportunity to obtain large-scale human mobility data, which can be used to study the characteristics of public facilities from the spatial interaction perspective. Intuitively, spatial interaction characteristics and service areas of different types and sizes of public facilities are different, but how different remains an open question, so we, in turn, examine this question. Based on spatial interaction, we classify public facilities and explore the differences in facilities. In the research, based on spatial interaction extracted from taxi data, we introduce an unsupervised classification method to classify 78 hospitals in 6 districts of Beijing, and the results better reflect the type of hospital. The findings are of great significance for optimizing the spatial configuration of medical facilities or other types of public facilities, allocating public resources reasonably and relieving traffic pressure. Full article

Determining the spatial relations between objects is a primary function of a geographic information system (GIS). One important aspect of spatial relations is topological relations, which remain constant under topological transformations. Describing the geometry of a spatial object using the OpenGIS Simple Features Specification requires only simple features: the interior, boundary and exterior of a spatial object are defined. This paper proposes a comprehensive model, the 27-intersection model (27IM), which considers both the dimensions and the number of intersections. Some propositions are presented to exclude relations that the 27IM cannot implement. The 27IM describes six groups of topological relations: point/point, point/line, point/region, line/line, line/region and region/region. The formalism of the 27IM and the corresponding geometric interpretations between spatial objects are illustrated and then compared to the topological relations represented by the existing models, the nine-intersection model (9IM), the dimensionally-extended nine-intersection matrix (DE-9IM) and the separation number extended nine-intersection matrix (SNE-9IM). The results show that (1) the 27IM can represent the topological relations between two-dimensional spatial objects, (2) the 27IM can distinguish more topological relations than can the 9IM, DE-9IM or the SNE-9IM and that (3) the interoperability of the 27IM with the 9IM, DE-9IM and SNE-9IM is good. Full article

The overall objective of this work is to apply GIS-based cost distance modeling (CDM) to site catchment modeling and analysis of prehistoric (Solutrean) sites in Andalusia. The implementation of a GIS-method for slope-based CDM was explained in detail, so that it can be replicated easily in future studies. Additional cost components, vegetation and stream networks, were included in the method. The presented CDM approach uses slope rasters as input data, which were derived from digital elevation models (DEMs). Various DEMs that differ in cell size, accuracy and other characteristics can be applied to this method. Thus, a major goal of this work is to investigate the influence different publicly available DEMs (SRTM, ASTER GDEM, EU-DEM, official 5-m/10-m cell size DEMs) have on the results of GIS-based CDM. While the investigation was conducted on sites from different chronocultural periods, a case study was performed on Solutrean sites in order to test the CDM approach by producing actual results and then comparing and interpreting them from an archaeological perspective. The results of the DEM evaluation with resampled horizontal resolutions show a clear influence of the DEM cell size on the modeled catchment area sizes. The investigation also indicates that this influence can be superimposed by other factors, such as noise and residuals of filtered anthropogenic features, when using DEMs of different origins. Full article

Influential nodes are rare in social networks, but their influence can quickly spread to most nodes in the network. Identifying influential nodes allows us to better control epidemic outbreaks, accelerate information propagation, conduct successful e-commerce advertisements, and so on. Classic methods for ranking influential nodes have limitations because they ignore the impact of the topology of neighbor nodes on a node. To solve this problem, we propose a novel measure based on local centrality with a coefficient. The proposed algorithm considers both the topological connections among neighbors and the number of neighbor nodes. First, we compute the number of neighbor nodes to identify nodes in cluster centers and those that exhibit the “bridge” property. Then, we construct a decreasing function for the local clustering coefficient of nodes, called the coefficient of local centrality, which ranks nodes that have the same number of four-layer neighbors. We perform experiments to measure node influence on both real and computer-generated networks using six measures: Degree Centrality, Betweenness Centrality, Closeness Centrality, K-Shell, Semi-local Centrality and our measure. The results show that the rankings obtained by the proposed measure are most similar to those of the benchmark Susceptible-Infected-Recovered model, thus verifying that our measure more accurately reflects the influence of nodes than do the other measures. Further, among the six tested measures, our method distinguishes node influence most effectively. Full article

The relevance of local knowledge in cultural heritage is by now acknowledged. It helps to determine many community-based projects by identifying the material to be digitally maintained in multimedia collections provided by communities of volunteers, rather than for-profit businesses or government entities. Considering that the search and browsing of texts, images, video, and 3D models related to places is more essential than using a simple text-based search, an interactive multimedia map was implemented in this study. The map, which is loaded on a single HyperText Markup Language (HTML) page using AJAX (Asynchronous JavaScript and XML), with a client-side control mechanism utilising jQuery components that are both freely available and ad-hoc developed, is updated according to user interaction. To simplify the publication of geo-referenced information, the application stores all the data in a Geographic JavaScript Object Notation (GeoJSON) file rather than in a database. The multimedia contents—associated with the selected Points of Interest (PoIs)—can be selected through text search and list browsing as well as by viewing their previews one by one in a sequence all together in a scrolling window (respectively: “Table”, “Folder”, and “Tile” functions). PoIs—visualised on the map with multi-shape markers using a set of unambiguous colours—can be filtered through their categories and types, accessibility status and timeline, thus improving the system usability. The map functions are illustrated using data collected in a Comenius project. Notes on the application software and architecture are also presented in this paper. Full article

This article discusses the convenience of adopting an approach of Collective Spatial Analysis in the P/PGIS processes, with the aim of improving the collection and integration of knowledge and local expertise in decision-making, mainly in the fields of planning and adopting territorial policies. Based on empirical evidence, as a result of the review of scientific articles from the Web of Science database, in which it is displayed how the knowledge and experience of people involved in decision-making supported by P/PGIS are collected and used, a prototype of a WEB-GSDSS application has been developed. This prototype allows a group of people to participate anonymously, in an asynchronous and distributed way, in a decision-making process to locate goods, services, or events through the convergence of their views. Via this application, two case studies for planning services in districts of Ecuador and Italy were carried out. Early results suggest that in P/PGIS local and external actors contribute their knowledge and experience to generate information that afterwards is integrated and analysed in the decision-making process. On the other hand, in a Collective Spatial Analysis, these actors analyse and generate information in conjunction with their knowledge and experience during the process of decision-making. We conclude that, although the Collective Spatial Analysis approach presented is in a subjective and initial stage, it does drive improvements in the collection and integration of knowledge and local experience, foremost among them is an interdisciplinary geo-consensus. Full article