ISPRS Int. J. Geo-Inf., Volume 6, Issue 9 (September 2017) – 32 articles

Citizens are increasingly sharing their location and movements through “check-ins” on location based social networks (LBSNs). These services are collecting unprecedented amounts of big data that can be used to study how we travel and interact with our environment. This paper presents the development of a long distance destination choice model for Ontario, Canada, using data from Foursquare to model destination attractiveness. A methodology to collect and process historical check-in counts has been developed, allowing the utility of each destination to be calculated based on the intensity of different activities performed at the destination. Destinations such as national parks and ski areas are very strong attractors of leisure trips, yet do not employ many people and have few residents. Trip counts to such destinations are therefore poorly predicted by models based on population and employment. Traditionally, this has been remedied by extensive manual data collection. The integration of Foursquare data offers an alternative approach to this problem. The Foursquare based destination choice model was evaluated against a traditional model estimated only with population and employment. The results demonstrate that data from LBSNs can be used to improve destination choice models, particularly for leisure travel. Full article

This study aims to develop a method to estimate chlorophyll-a concentration (Chla) in tropical freshwater lake waters using in situ data of Chla, water reflectance, and concurrent Sentinel 2A MSI imagery (S2A) over Lake Ba Be, a Ramsar site and the largest natural freshwater lake in Vietnam. Data from 30 surveyed sampling sites over the lake water in June 2016 and May 2017 demonstrated the appropriateness of S2A green-red band ratio (band 3 versus band 4) for estimating Chla. This was shown through a strong correlation of corresponded field measured reflectance ratio with Chla by an exponential curve (r² = 0.68; the mean standard error of the estimates corresponding to 5% of the mean value of in situ Chla). The small error between in situ Chla, and estimated Chla from S2A acquired concurrently, confirmed the S2A green-red band ratio as the most suitable option for monitoring Chla in Lake Ba Be water. Resultant Chla distribution maps over time described a partially-seasonal pattern and also displayed the spatial dynamic of Chla in the lake. This allows a better understanding of the lake’s limnological processes to be developed and provides an insight into the factors that affect lake water quality. The results also confirmed the potential of S2A to be used as a free tool for lake monitoring and research due to high spatial resolution data (10 m pixel size). Full article

Spatial–temporal analysis of land-use/land-cover (LULC) change as well as the monitoring and modeling of urban expansion are essential for the planning and management of urban environments. Such environments reflect the economic conditions and quality of life of the individual country. Urbanization is generally influenced by national laws, plans and policies and by power, politics and poor governance in many less-developed countries. Remote sensing tools play a vital role in monitoring LULC change and measuring the rate of urbanization at both the local and global levels. The current study evaluated the LULC changes and urban expansion of Jhapa district of Nepal. The spatial–temporal dynamics of LULC were identified using six time-series atmospherically-corrected surface reflectance Landsat images from 1989 to 2016. A hybrid cellular automata Markov chain (CA–Markov) model was used to simulate future urbanization by 2026 and 2036. The analysis shows that the urban area has increased markedly and is expected to continue to grow rapidly in the future, whereas the area for agriculture has decreased. Meanwhile, forest and shrub areas have remained almost constant. Seasonal rainfall and flooding routinely cause predictable transformation of sand, water bodies and cultivated land from one type to another. The results suggest that the use of Landsat time-series archive images and the CA–Markov model are the best options for long-term spatiotemporal analysis and achieving an acceptable level of prediction accuracy. Furthermore, understanding the relationship between the spatiotemporal dynamics of urbanization and LULC change and simulating future landscape change is essential, as they are closely interlinked. These scientific findings of past, present and future land-cover scenarios of the study area will assist planners/decision-makers to formulate sustainable urban development and environmental protection plans and will remain a scientific asset for future generations. Full article

The aim of the topographic normalization of remotely sensed imagery is to reduce reflectance variability caused by steep terrain and thus improve further processing of images. A process of topographic correction was applied to Landsat imagery in a mountainous forest area in the south of Mexico. The method used was the Sun Canopy Sensor + C correction (SCS + C) where the C parameter was differently determined according to a classification of the topographic slopes of the studied area in nine classes for each band, instead of using a single C parameter for each band. A comparative, visual, and numerical analysis of the normalized reflectance was performed based on the corrected images. The results showed that the correction by slope classification improves the elimination of the effect of shadows and relief, especially in steep slope areas, modifying the normalized reflectance values according to the combination of slope, aspect, and solar geometry, obtaining reflectance values more suitable than the correction by non-slope classification. The application of the proposed method can be generalized, improving its performance in forest mountainous areas. Full article

The current methods that use hyperspectral remote sensing imagery to extract and monitor marine oil spills are quite popular. However, the automatic extraction of endmembers from hyperspectral imagery remains a challenge. This paper proposes a data field-spectral preprocessing (DSPP) algorithm for endmember extraction. The method first derives a set of extreme points from the data field of an image. At the same time, it identifies a set of spectrally pure points in the spectral space. Finally, the preprocessing algorithm fuses the data field with the spectral calculation to generate a new subset of endmember candidates for the following endmember extraction. The processing time is greatly shortened by directly using endmember extraction algorithms. The proposed algorithm provides accurate endmember detection, including the detection of anomalous endmembers. Therefore, it has a greater accuracy, stronger noise resistance, and is less time-consuming. Using both synthetic hyperspectral images and real airborne hyperspectral images, we utilized the proposed preprocessing algorithm in combination with several endmember extraction algorithms to compare the proposed algorithm with the existing endmember extraction preprocessing algorithms. The experimental results show that the proposed method can effectively extract marine oil spill data. Full article

In the era of big data, Internet-based geospatial information services such as various LBS apps are deployed everywhere, followed by an increasing number of queries against the massive spatial data. As a result, the traditional relational spatial database (e.g., PostgreSQL with PostGIS and Oracle Spatial) cannot adapt well to the needs of large-scale spatial query processing. Spark is an emerging outstanding distributed computing framework in the Hadoop ecosystem. This paper aims to address the increasingly large-scale spatial query-processing requirement in the era of big data, and proposes an effective framework GeoSpark SQL, which enables spatial queries on Spark. On the one hand, GeoSpark SQL provides a convenient SQL interface; on the other hand, GeoSpark SQL achieves both efficient storage management and high-performance parallel computing through integrating Hive and Spark. In this study, the following key issues are discussed and addressed: (1) storage management methods under the GeoSpark SQL framework, (2) the spatial operator implementation approach in the Spark environment, and (3) spatial query optimization methods under Spark. Experimental evaluation is also performed and the results show that GeoSpark SQL is able to achieve real-time query processing. It should be noted that Spark is not a panacea. It is observed that the traditional spatial database PostGIS/PostgreSQL performs better than GeoSpark SQL in some query scenarios, especially for the spatial queries with high selectivity, such as the point query and the window query. In general, GeoSpark SQL performs better when dealing with compute-intensive spatial queries such as the kNN query and the spatial join query. Full article

With the increasing use of geographical information and technology in a variety of knowledge domains and disciplines, the need to discover and access suitable geospatial data is imperative. Most spatial data infrastructures (SDI) provide geoportals as entry points to the SDI through which geospatial data are disseminated and shared. Geoportals are often known in geoinformation communities only, and they present technological challenges for indexing by web search engines. To overcome these challenges, we identified and categorized search terms typically employed by users when looking for geospatial resources on the Web. Guided by these terms, we published metadata about geospatial sources “directly” on the Web and performed empirical tests with search engine optimization (SEO) techniques. Two sets of HTML pages were prepared and registered with Google and Bing respectively. The metadata in one set was marked up with Dublin Core, the other with Schema.org. Analysis of the results shows that Google was more effective than Bing in retrieving the pages. Pages marked up with Schema.org were more effectively retrieved than those marked up with Dublin Core. The statistical results were significant in most of the tests performed. This research confirms that pages marked up with Schema.org and Dublin Core are a novel alternative for improving the visibility and facilitating the discovery of geospatial resources on the Web. Full article

In this study, different interpolation techniques are presented, assessed, and compared for the estimation of soil iron (Fe) contents in locations where observations were not available. Initially, 400 soil samples from the Kozani area, which is near Polifitou Lake in northern Greece, were randomly collected from 2013 to 2015 and were analysed in the laboratory to determine the soil Fe concentrations and pH. The soil Fe concentrations were examined for spatial autocorrelation, and semivariograms were used to determine whether pH and Fe exhibited spatial cross correlation. Three interpolation methods, including Ordinary Kriging, Universal Kriging, and Co-Kriging, were applied, and their results were compared with the use of two different cross-validation methods. In the current study, there was evidence of spatial cross correlation of soil Fe and pH for each year, which was subsequently used to improve the interpolation results in locations where there were no measurements. In nearly all cases, Co-Kriging, which takes advantage of the covariance between the two regionalized variables (Fe and pH), outperformed the other interpolation techniques each year. Full article

Geospatial data and geospatial e-services require governance and coordination between different governmental organisations. This article aims to understand what governance, and specifically what coordination, is used in Belgium for geospatial e-services and data. The Belgian case, with a focus on the regions and federal administration, is researched by making use of a document analysis, interviews with key stakeholders and an online survey. In contrast to the federal and Walloon administration, the Flemish administration and the Brussels Capital Region administration have a clearly developed governance model. Flanders combines hierarchy with network governance, whereas the Brussels administration is known for its hierarchical way of working. The transposition of the INSPIRE Directive had a strong influence: The Brussels Capital Region became more network-oriented, and the Walloon Region developed a form of network governance. The federal level, however, struggles to make the connection between geospatial data and e-services. From an inter-organisational perspective, the coordination can be labelled as a weak form of network governance: Cooperation exists, but only in a few areas. Nevertheless, geospatial data are exchanged within and between regions and the federal level. Geospatial e-services are also developed but there is a clear influence of the degree of organisational coordination on the development of geospatial e-services. Full article

The concepts of “Smart Cities” and “Smart Manufacturing” are different data-driven domains, although both rely on intelligent information technology and data analysis. With the application of linked data and affordance-based approaches, both domains converge, paving the way for new and innovative viewpoints regarding the comparison of urban tasks with indoor manufacturing tasks. The present study builds on the work, who state that cities are scaled versions of each other, by extending this thesis towards indoor manufacturing environments. Based on their structure and complexity, these environments are considered to form ecosystems of their own, comparable to “small cities”. This conceptual idea is demonstrated by examining the process of human problem-solving in transportation situations from both perspectives (i.e., city-level and manufacturing-level). In particular, the authors model tasks of human operators that are used to support transportation processes in indoor manufacturing environments based on affordances and spatial-temporal data. This paper introduces the fundamentals of the transformation process of outdoor tasks and process planning activities to indoor environments, particularly to semiconductor manufacturing environments. The idea is to examine the mapping of outdoor tasks and applications to indoor environments, and vice-versa, based on an example focusing on the autonomous transportation of production assets in a manufacturing environment. The approach is based on a spatial graph database, populated with an indoor navigation ontology and instances of indoor and outdoor objects. The results indicate that human problem-solving strategies can be applied to indoor manufacturing environments to support decision-making in autonomous transportation tasks. Full article

In manufacturing companies, productivity and efficiency are the main priorities, besides an emphasis on quality issues. The outcome of this research contributes to increasing production quality and efficiency in manufacturing. The article deals with indoor manufacturing environments and the transportation processes of production assets—referred to as smart transportation. The authors modelled the objects present in the indoor manufacturing environment with ontologies including their affordances and spatial suitability. To support flexible production and dynamic transportation processes have to be tailored towards the ‘needs’ of the production asset. Hence, the authors propose an approach utilizing an ad-hoc suitability network to support the “optimal” path computation for transportation processes. The objective is to generate a graph for routing purposes for each individual production asset, with respect to the affordances of the indoor space for each production asset, and measurements of a sensor network. The generation of the graph follows an ad-hoc strategy, in two ways. First, the indoor navigation graph is created exactly when a path needs to be found—when a production asset shall be transported to the next manufacturing step. Secondly, the transportation necessities of each production asset, as well as any disturbances present in the environment, are taken into account at the time of the path calculation. The novelty of this approach is that the development of the navigation graph—including the weights—is done with affordances, which are based on an ontology. To realize the approach, the authors developed a linked data approach based on manufacturing data and on an application ontology, linking the indoor manufacturing environment and a graph-based network. The linked data approach is finally implemented as a spatial graph database containing walkable corridors, production equipment, assets and a sensor network. The results show the optimal path for transportation processes with respect to affordances of the indoor manufacturing environments. An evaluation of the computational complexity shows that the affordance-based ad-hoc graphs are thinner and thus reduce the computational complexity of shortest path calculations. Hence, we conclude that an affordance-based approach can help to decrease computational efforts for calculating “optimal” paths for transportation purposes. Full article

There are several competences and spatial skills to be acquired by the student related to the treatment of geo-information in Science, Technology, Engineering, and Mathematics (STEM) disciplines. Spatial orientation is the spatial skill related to the use of georeferenced information, and geospatial applications (on-line map interfaces) such as the spatial data infrastructure offer a great opportunity for development of this skill. In this report we present several experiments, carried out over five academic years with 559 university students, to improve the spatial orientation skill of the students. Survey learning and wayfinding activities were conducted. First- and second-year university students performed the experiments on a PC and also used digital tablet support. The statistical analysis showed that the students improved their spatial orientation skill with a range from 12.90 (minimum) to 19.21 (maximum) measured with the Perspective Taking Spatial Orientation Test, regardless of the academic year, the hardware (PC or Tablet-PC), or the orientation strategy (survey learning or wayfinding). The second year students improved more than those in their first year. The methodologies employed could be developed by teachers or researchers, and the results presented could be taken as a reference for comparisons in future research in the field of strategy planning with geospatial applications and location-based tools for spatial orientation skill improvement in education. Full article

The studies of forest ecosystems from remotely-sensed data are of great interest to researchers because of ecosystem services provided by this ecosystem, including protection of soils and vegetation, climate stabilization, and regulation of the hydrological cycle. In this context, our study focused on the use of a spectral angle mapper (SAM) classification method for mapping species in the Azrou Forest, Central Middle Atlas, Morocco. A Sentinel-2A image combined with ground reference data were used in this research. Four classes (holm oak, cedar forest, bare soil, and others-unclassified) were selected; they represent, respectively, 27, 11, 24, and 38% of the study area. The overall accuracy of classification was estimated to be around 99.72%. This work explored the potential of the SAM classification combined with Sentinel-2A data for mapping land cover in the Azrou Forest ecosystem. Full article

Although data is increasingly shared online and accessible for re-use, we still witness heterogeneous coverage of thematic areas and geographic regions. This especially becomes an issue when data is needed for large territories and including different nations, as, for example, required to support macro-regional development policies. Once identified, data gaps might be closed using different approaches. Existing—but so far non accessible—data might be made available; new public sector information could be gathered; or data might be acquired from the private sector. Our work explores a fourth option: closing data gaps with direct contributions from citizen (Citizen Science). This work summarizes a particular case study that was conducted in 2016 in the Danube Region. We provide a gap analysis over an existing macro-regional data infrastructure, and examine potential Citizen Science approaches that might help to close these gaps. We highlight already existing Citizen Science projects that could address a large part of the identified gaps, and suggest one particular new application in order to indicate how a—so far uncovered—gap might be approached. This new application addresses bioenergy as a particular field of the circular economy. On this basis we discuss the emerging opportunities and challenges for this particular way of public participation in regional development policy. We close by highlighting areas for future research. Full article

The commonly used methods in digital cartography are based on the minimum dimensions of black and white objects. This article presents a solution in which both the colour of the symbols and the background on which they are presented are relevant in the context of setting the minimum dimensions of the objects on a map. To achieve this, the authors have developed a perception coefficient that is an extension of the formal definitions of minimum object dimensions. In support of the presented solutions, the authors offer several cartographic examples. The article also contains experimental research that examines the impact of colour on the recognition of objects by means of specially prepared surveys. These results are compared against the theoretical values of the perception coefficient. The research objective was achieved by developing new solutions that could be used in the cartographic production processes of any national map agency. Full article

An understanding of guidance, which means guiding attention, and constancy, meaning that an area can be perceived for what it is despite environmental changes, of the visual variables related to three-dimensional (3D) symbols is essential to ensure rapid and consistent human perception in 3D visualization. Previous studies have focused on the guidance and constancy of visual variables related to two-dimensional (2D) symbols, but these aspects are not well documented for 3D symbols. In this study, we used eye tracking to analyze the visual guidance from shapes, hues and sizes, and the visual constancy that is related to the shape, color saturation and size of 3D symbols in different locations. Thirty-six subjects (24 females and 12 males) participated in the study. The results indicate that hue and shape provide a high level of visual guidance, whereas guidance from size, a variable that predominantly guides attention in 2D visualization, is much more limited in 3D visualization. Additionally, constancy of shape and saturation are perceived with relatively high accuracy, whereas constancy of size is perceived with only low accuracy. These first empirical studies are intended to pave the way for a more comprehensive user understanding of 3D visualization design. Full article

Urban land-use allocation is a complicated problem due to the variety of land-uses, a large number of parcels, and different stakeholders with diverse and conflicting interests. Various approaches and techniques have been proposed for the optimization of urban land-use allocation. The outputs of these approaches are almost optimum plans that suggest a unique, appropriate land-use for every land unit. However, because of some restrictions, such stakeholder opposition to a specific land-use or the high cost of land-use change, it is not possible for planners to propose a desirable land-use for each parcel. As a result, planners have to identify other priorities of the land-uses. Thus, ranking land-uses for parcels along with optimal land-use allocation could be advantageous in urban land-use planning. In this paper, a parcel-level model is presented for ranking and allocating urban land-uses. The proposed model benefits from the capabilities of geographic information systems (GIS), fuzzy calculations, and Multi-Criteria Decision-Making (MCDM) methods (fuzzy TOPSIS), intends to improve the capabilities of existing urban land-use planning support systems. In this model, as a first step, using fuzzy calculations and spatial analysis capabilities of GIS, quantitative and qualitative evaluation criteria are estimated based on physical characteristics of the parcels and their neighborhoods. In the second step, through the fuzzy TOPSIS method, urban land-uses are ranked for each of the urban land units. In the third step, using the proposed land-use allocation process and genetic algorithm, the efficiency of the model is evaluated in urban land-use optimal allocation. The proposed model is tested on spatial data of region 7, district 1 of Tehran. The implementation results demonstrate that, in the study area, the land-use of 77.2% of the parcels have first priority. As such, the land-use of 22.8% of the parcels do not have first priority, and are prone to change. Full article

Statistically clustering air pollution can provide evidence of underlying spatial processes responsible for intensifying the concentration of contaminants. It may also lead to the identification of hotspots. The patterns can then be targeted to manage the concentration level of pollutants. In this regard, employing spatial autocorrelation indices as important tools is inevitable. In this study, general and local indices of Moran’s I and Getis-Ord statistics were assessed in their representation of the structural characteristics of carbon monoxide (CO) and fine particulate matter (PM_2.5) polluted areas in Tehran, Iran, which is one of the most polluted cities in the world. For this purpose, a grid (200 m × 200 m) was applied across the city, and the inverse distance weighted (IDW) interpolation method was used to allocate a value to each pixel. To compare the methods of detecting clusters meaningfully and quantitatively, the pollution cleanliness index (PCI) was established. The results ascertained a high clustering level of the pollutants in the study area (with 99% confidence level). PM_2.5 clusters separated the city into northern and southern parts, as most of the cold spots were situated in the north half and the hotspots were in the south. However, the CO hotspots also covered an area from the northeast to southwest of the city and the cold spots were spread over the rest of the city. The Getis-Ord’s PCI suggested a more polluted air quality than the Moran’s I PCI. The study provides a feasible methodology for urban planners and decision makers to effectively investigate and govern contaminated sites with the aim of reducing the harmful effects of air pollution on public health and the environment. Full article

An adaptive spatial clustering (ASC) algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation, or Voronoi diagram). It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need for prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets. Full article

In map generalization, scale reduction and feature symbolization inevitably generate problems of overlapping objects or map congestion. To solve the legibility problem with respect to the generalization of dispersed rural buildings, selection of buildings is necessary and can be transformed into an optimization problem. In this paper, an improved genetic algorithm for building selection is designed to be able to incorporate cartographic constraints related to the building selection problem. Part of the local constraints for building selection is used to constrain the encoding and genetic operation. To satisfy other local constraints, a preparation phase is necessary before building selection, which includes building enlargement, local displacement, conflict detection, and attribute enrichment. The contextual constraints are used to ascertain a fitness function. The experimental results indicate that the algorithm proposed in this article can obtain good results for building selection whilst preserving the spatial distribution characteristics of buildings. Full article

A 3D building model retrieval method using airborne LiDAR point clouds as input queries is introduced. Based on the concept of data reuse, available building models in the Internet that have geometric shapes similar to a user-specified point cloud query are retrieved and reused for the purpose of data extraction and building modeling. To retrieve models efficiently, point cloud queries and building models are consistently and compactly encoded by the proposed method. The encoding focuses on the geometries of building roofs, which are the most informative part of a building in airborne LiDAR acquisitions. Spatial histograms of geometric features that describe shapes of building roofs are utilized as shape descriptor, which introduces the properties of shape distinguishability, encoding compactness, rotation invariance, and noise insensitivity. These properties facilitate the feasibility of the proposed approaches for efficient and accurate model retrieval. Analyses on LiDAR data and building model databases and the implementation of web-based retrieval system, which is available at http://pcretrieval.dgl.xyz, demonstrate the feasibility of the proposed method to retrieve polygon models using point clouds. Full article

For the simulation of public transport, next to a schedule, knowledge of the public transport routes is required. While the schedules are becoming available, the precise network routes often remain unknown and must be reconstructed. For large-scale networks, however, a manual reconstruction becomes unfeasible. This paper presents a route reconstruction algorithm, which requires only the sequence and positions of the public transport stops and the street network. It uses an abstract graph to calculate the least-cost path from a route’s first to its last stop, with the constraint that the path must contain a so-called link candidate for every stop of the route’s stop sequence. The proposed algorithm is implemented explicitly for large-scale, real life networks. The algorithm is able to handle multiple lines and modes, to combine them at the same stop location (e.g., train and bus lines coming together at a train station), to automatically reconstruct missing links in the network, and to provide intelligent and efficient feedback if apparent errors occur. GPS or OSM tracks of the lines can be used to improve results, if available. The open-source algorithm has been tested for Zurich for mapping accuracy. In summary, the new algorithm and its MATSim-based implementation is a powerful, tested tool to reconstruct public transport network routes for large-scale systems. Full article

As recommended by the international standards, ISO 21542, ease of wayfinding must be ensured by installing signage at all key decision points on walkways such as forks because signage greatly influences the way in which people unfamiliar with an environment navigate through it. Therefore, we aimed to develop a new system for evaluating the ease of wayfinding, which could detect spots that cause disorientation, i.e., “disorientation spots”, based on simulated three-dimensional (3D) interactions between wayfinding behaviors and signage location, visibility, legibility, noticeability, and continuity. First, an environment model reflecting detailed 3D geometry and textures of the environment, i.e., “as-is environment model”, is generated automatically using 3D laser-scanning and structure-from-motion (SfM). Then, a set of signage entities is created by the user. Thereafter, a 3D wayfinding simulation is performed in the as-is environment model using a digital human model (DHM), and disorientation spots are detected. The proposed system was tested in a virtual maze and a real two-story indoor environment. It was further validated through a comparison of the disorientation spots detected by the simulation with those of six young subjects. The comparison results revealed that the proposed system could detect disorientation spots, where the subjects lost their way, in the test environment. Full article

Public water services are still failing rural Tanzanians. Emboldened by advances in information communication technologies, the Ministry of Water has been developing computing, financial and administrative technologies to update and visualise the status of rural water points. This amalgam of technologies marks the emergence of an information infrastructure for rural water governance. The information infrastructure will enable the ministry to “see” the functionality status of all rural water points and to plan and budget for their repair and maintenance. In this paper, we examine three administrative technologies, which aim to standardise the functionality status of water points, and to prescribe how the information flows within the government hierarchy, and who is a legitimate recipient of this information. We analyze qualitative data, collected over a period of four years, in the framework of an interdisciplinary research program, funded by the Netherlands Organisation for Scientific Research—Science for Global Development (NWO-Wotro). In contrast to other researchers who study how information infrastructure evolves over time, we study what infrastructure evolution reveals about water governance. Our analysis of the practices of participants in rural water governance reveals tensions between formal and informal processes, which affect rural water services negatively. Full article

The visual perception of streets plays an important role in urban planning, and contributes to the quality of residents’ lives. However, evaluation of the visual perception of streetscapes has been restricted by inadequate techniques and the availability of data sources. The emergence of street view services (Google Street View, Tencent Street View, etc.) has provided an enormous number of new images at street level, thus shattering the restrictions imposed by the limited availability of data sources for evaluating streetscapes. This study explored the possibility of analyzing the visual perception of an urban street based on Tencent Street View images, and led to the proposal of four indices for characterizing the visual perception of streets: salient region saturation, visual entropy, a green view index, and a sky-openness index. We selected the Jianye District of Nanjing City, China, as the study area, where Tencent Street View is available. The results of this experiment indicated that the four indices proposed in this work can effectively reflect the visual attributes of streets. Thus, the proposed indices could facilitate the assessment of urban landscapes based on visual perception. In summary, this study suggests a new type of data for landscape study, and provides a technique for automatic information acquisition to determine the visual perception of streets. Full article

A large number of studies have dealt with the driven forces of land expansion, in which the remote sensing data and statistical data are most commonly used. The recent progress based on the statistical data have not been fully tested and discussed by the remote sensing data, and the remote sensing data used in the previous studies are usually interpreted within certain areas which is not convenient for global comparison. In this paper, the 30-m GlobalLand Cover Dataset (GlobeLand30) and socioeconomic data from 2000 to 2010 are adopted to investigate the factors driving impervious surface expansion in China based on a multilevel regression model. The GlobeLand30 provides a world-wide data framework which has a sound basis for regional comparison research. The variables are selected according to the existing research. Most, but not all, results are consistent with the previous studies when using impervious surface data of GlobeLand30. The main findings are: (1) the market demand caused by economic development, such as the increase in GDP from 2000 to 2010, plays a positive role in the expansion of developed land; (2) the land supply, as reflected by the ratio of the total of land transfer fees to fiscal revenue, also has a positive effect on the increase in impervious surfaces; (3) the percentage of the increase by private workers to the increase in total workers and certain other frequently-used variables are not relevant after controlling for land demand- and supply-related variables; and (4) the growth in impervious surfaces is related to the amount of the cultivated land, which implies the necessity for a more stringent farmland protection policy. Considering the need to compare across regions, we suggest that GlobeLand30 should be used for more studies to better understand the driving forces of land expansion. Full article

Nature conservation is critical for securing an adequate supplying of environmental services to humans. Paradoxically, financial resources for conservation are normally scarce and, forest ecosystem restoration activities are expensive. So, a careful and detailed planning is vital for optimizing economic funds when ecosystems restoration practices are implemented. In this work, we developed a methodology to find physically-degraded sites in order to determine both, urgency and feasibility to carry out ecological forest restoration activities in the Priority Region for Conservation Xilitla in the state of San Luis Potosí (Mexico). Both, Universal Soil Loss Equation (USLE) and Multi-Criteria Decision Analysis (MCDA) were integrated together by using climatic, soil, remotely-sensed, and proximity data at a 30 m spatial resolution. The results indicated that, more than 80% of the bare soil land in the protected area is under several conditions that lead to feasible ecosystem restoration. This methodology can be further applied to know about the spatial location of soil degraded sites when planning forest restoration practices in natural protected areas. Full article

Rapid urbanization in China has increased the demand of land resources for urban areas and caused a series of environmental problems. Ecological security under the pressure of urban sprawl has become one typical indicator for illustrating regional environmental conditions and thus inform urban development. As an important farming area and one of the core economic development regions in northeast China, Changchun City is now confronted with severe contradictions between economic growth, habitat conservation and food security. Therefore, with the aim of developing an approach to optimize a regional ecological security pattern and land use structure, this study built a comprehensive ecological security pattern taking into account regional ecological processes including water regulation, soil and water conservation, species protection and recreation. Three patterns of ecological security were identified responding to different levels of ecological conservation: the basic security pattern, the buffer security pattern and the optimal security pattern. Based on the constraint of the ecological security pattern, the preservation area of prime farmland was added to an urban expansion suitability pattern as an additional constraint to simulate scenarios of urban expansion. The results indicate that the basic security pattern covers an area of 374.23 km², accounting for 19.27% of the total area of Changchun City. This pattern is considered as the ecological baseline that maintains the basic ecological functions, and it is the area where ecological land cannot be occupied for construction purposes. Furthermore, co-constrained by the preservation area of prime farmland, the ecological conservation area, the ecological restriction area and the suitable development area are 190.34 km², 384.75 km² and 152.83 km², respectively, accounting for 9.80%, 19.80% and 7.87% of the total area. It can be concluded that the suitable expansion area for the city is relatively limited when the conservation of farmland and regional ecological environment is considered. Therefore, positive actions such as industrial structure transformation and land use efficiency improvements should be perceived as a preferable pathway for urban development to balance economic growth, and regional ecological and food security. Full article

Motivation is a determining factor in the learning process, and encourages the student to participate in activities that increase their performance. Learning strategies supplemented by computer technology in a scenario-based learning environment can improve students′ motivation for spatial knowledge acquisition. In this sense, a workshop carried out with 43-second year engineering students supported by Google Street View mobile geospatial application for location-based tasks is presented, in which participants work in an immersive wayfinding 3D urban environment on virtual reality. Students use their own smartphones with Google Street View application integrated in virtual reality (VR) 3D glasses with a joystick as locomotion interface. The tool to analyse the motivational factor of this pedagogical approach is the multidimensional measurement device Intrinsic Motivation Inventory with six subscales: interest, perceived competence, perceived choice, effort, tension, and value, measured on a seven point Likert scale. Scores in all subscales considered are above 4 on a scale of 7. A usability study conducted at the end of the experiment provides values above 3 on a scale of 5 in efficacy, efficiency and satisfaction. The results of the experiment carried out indicate that geospatial Google Street View application in Virtual Reality is a motivating educational purpose in the field of spatial training. Full article