ISPRS Int. J. Geo-Inf., Volume 7, Issue 5 (May 2018) – 38 articles

Suitability evaluation of urban construction land is critical for both urban master planning and the proper utilization of land resources. Using the Beihu New District of Jining City, China, as a case study, this paper introduces a novel research approach for comprehensive suitability evaluation based on vertical-horizontal processes. First, by considering both the land development potential and ecological constraint resistance, the potential-resistance (PR) model was developed and used to analyze the suitability for urban construction of vertical processes. Then, given the results of the vertical suitability analysis, the current urban built-up areas were selected as the sources of urban expansion, and the minimum cumulative resistance (MCR) model was applied to evaluate the suitability for urban development in terms of horizontal processes. The study area was regionalized into four categories—priority, suitable, restricted, and prohibited areas—which were defined based on the development threshold. The results showed that restricted and prohibited areas for urban construction occupied most of the study area. Totally, 648.51 km² was categorized as restricted or prohibited, accounting for 12.89% and 54.75% of the total area, respectively. Priority and suitable areas for urban construction covered a total area of 310.37 km², accounting for 16.55% and 15.81% of the total area, respectively. These areas were mainly distributed around urban centers and urban built-up areas. These findings reflect the substantial potential for future urban development and construction in the study area. The newly developed principles and methods of suitability evaluation for urban construction land presented in this paper provide more appropriate scales and spatial location for urban development and an ecological baseline for future urban growth. Full article

2D maps with contour lines can be difficult for students to visualize in three-dimensions to interpret relief. Despite this challenge, teaching based on 2D contour lines is still used, which could generate frustration/motivation problems among students. Recently, strategies based on 3D technologies such as Augmented Reality (AR) have proven to be motivating for students. Has the time come for a paradigm shift in the teaching of land interpretation/representation? The present paper shows the results of an experiment in which 41 engineering students of the subject Cartography performed an activity with 2D contour lines. The impact on students’ motivation was compared with AR. In addition, data about efficiency, effectiveness and user satisfaction were assessed. Results showed that traditional 2D contour line activities were less motivating for students, compared to AR. However, students perceived that doing 2D exercises made them more competent than with AR, although they reported that the 2D exercises required more effort. In terms of participant’s spatial reasoning acquisition, 2D strategies offered results similar to AR. Overall, these results suggest that 2D teaching methodologies are still effective and can be complemented by the use of innovative 3D visualization technologies. Full article

Population density and distribution of services represents the growth and demographic shift of the cities. For urban planners, population density and check-in behavior in space and time are vital factors for planning and development of sustainable cities. Location-based social network (LBSN) data seems to be a complement to many traditional methods (i.e., survey, census) and is used to study check-in behavior, human mobility, activity analysis, and social issues within a city. This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on gender difference and frequency of using LBSN. Therefore, in this study, we investigate the check-in behavior of Chinese microblog Sina Weibo (referred as “Weibo”) in 10 districts of Shanghai, China, for which we observe the gender difference and their frequency of use over a period. The mentioned districts were spatially analyzed for check-in spots by kernel density estimation (KDE) using ArcGIS. Furthermore, our results reveal that female users have a high rate of social media use, and significant difference is observed in check-in behavior during weekdays and weekends in the studied districts of Shanghai. Increase in check-ins is observed during the night as compared to the morning. From the results, it can be assumed that LBSN data can be helpful to observe gender difference. Full article

Nonnegative matrix factorization (NMF) is a blind source separation (BSS) method often used in hyperspectral unmixing. However, it tends to converge to a local optimum. To overcome this limitation, we present a simple, but effective endmember initialization scheme for NMF, which is realized by improving initial values through the application of the automatic target generation process (ATGP) algorithm. The initial spectra and abundances of target endmembers are first obtained using the ATGP algorithm and nonnegative least squares (NNLS) method, respectively. The preliminary results are then optimized through iterative application of NMF. To validate the applicability and effectiveness of the proposed method, we analyzed the improvement of NMF by the ATGP algorithm, using the synthetic hyperspectral data and real hyperspectral images. The results from the proposed method are compared with those of the vertex component analysis (VCA)-NMF algorithm, which uses the VCA algorithm to perform initialization for NMF, the minimum volume constrained NMF (MVC-NMF) algorithm, the traditional two-step VCA-fully-constrained least squares (FCLS) algorithm, which uses the VCA to extract the endmember matrix, and the FCLS algorithm to estimate the abundance matrix. The comparison results prove that proper endmember initialization can help the NMF algorithm yield better estimation results. Through the optimization of target endmembers’ initial values, the proposed ATGP-NMF algorithm can consistently produce good results at a lower computational cost, especially in the case of a real hyperspectral image for which pure pixels do not exist and there is little prior knowledge. With its high applicability and effectiveness, the ATGP-NMF algorithm has a great potential to solve hyperspectral unmixing problems. Full article

Virtual 3D city models act as valuable central information hubs supporting many aspects of cities, from management to planning and simulation. However, we noted that 3D city models are still underexploited and believe that this is partly due to inefficient visual communication channels across 3D model producers and the end-user. With the development of a formalized 3D geovisualization approach, this paper aims to support and make the visual identification and recognition of specific objects in the 3D models more efficient and useful. The foundation of the proposed solution is a knowledge network of the visualization of 3D geospatial data that gathers and links mapping and rendering techniques. To formalize this knowledge base and make it usable as a decision-making system for the selection of styles, second-order logic is used. It provides a first set of efficient graphic design guidelines, avoiding the creation of graphical conflicts and thus improving visual communication. An interactive tool is implemented and lays the foundation for a suitable solution for assisting the visualization process of 3D geospatial models within CAD and GIS-oriented software. Ultimately, we propose an extension to OGC Symbology Encoding in order to provide suitable graphic design guidelines to web mapping services. Full article

In the study of spatiotemporal geographical phenomena, the space–time interpolation method is widely applied, and the demands for computing speed and accuracy are increasing. For nonprofessional modelers, utilizing the space–time interpolation method quickly is a challenge. To solve this problem, the classical ordinary kriging algorithm was selected and expanded to a spatiotemporal kriging algorithm. Using the OpenCL framework to integrate central processing unit (CPU) and graphic processing unit (GPU) computing resources, a parallel spatiotemporal kriging algorithm was implemented, and three experiments were conducted in this work to verify the results. The results indicated the following: (1) when the size of the prediction point dataset is consistent, the performance of the method is robust with the increasing size of the observation point dataset; (2) the acceleration effect of the parallel method increases with an increased number of predicted points. Compared with the original sequential program, the implementation of the improved parallel framework showed a 3.23 speedup, which obviously shortens the interpolation time; (3) when cross-validating the temperature data in the Beijing Tianjin Hebei region, the space–time acceleration model provides a better fit than traditional pure space interpolation. Full article

This paper describes a study of the evaluation of cartographic quality of urban plans in the Czech Republic using eye-tracking. Although map visualization is a crucial part of the urban planning process, only a few studies have focused on the evaluation of these maps. The plans of four Czech cities with different styles of visualization and legends were used in this eye-tracking experiment. Respondents were required to solve spatial tasks consisting of finding and marking a certain symbol on a map. Statistical analyses of various eye-tracking metrics were used, and the differences between experts and students and between the map and legend sections of the stimuli were explored. The study results showed that the quality of map symbols and the map legend significantly influence the legibility and understandability of urban plans. For correct decision-making, it is essential to produce maps according to certain standards, to make them as clear as possible, and to perform usability testing on them. Full article

The integration of facility management and building information modelling (BIM) is an innovative and critical undertaking process to support facility maintenance and management. Even though recent research has proposed various methods and performed an increasing number of case studies, there are still issues of communication processes to be addressed. This paper presents a theoretical framework for digital systems integration of virtual models and smart technologies. Based on the comprehensive analysis of existing technologies for indoor localization, a new workflow is defined and designed, and it is utilized in a practical case study to test the model performance. In the new workflow, a facility management supporting platform is proposed and characterized, featuring indoor positioning systems to allow end users to send geo-referenced reports to central virtual models. In addition, system requirements, information technology (IT) architecture and application procedures are presented. Results show that the integration of end users in the maintenance processes through smart and easy tools can overcome the existing limits of barcode systems and building management systems for failure localization. The proposed framework offers several advantages. First, it allows the identification of every element of an asset including wide physical building elements (walls, floors, etc.) without requiring a prior mapping. Second, the entire cycle of maintenance activities is managed through a unique integrated system including the territorial dimension. Third, data are collected in a standard structure for future uses. Furthermore, the integration of the process in a centralized BIM-GIS (geographical information system) information management system admit a scalable representation of the information supporting facility management processes in terms of assets and supply chain management and monitoring from a spatial perspective. Full article

Information and Communication Technologies (ICTs) and mobile devices are deeply influencing all facets of life, directly affecting the way people experience space and time. ICTs are also tools for supporting urban development, and they have also been adopted as equipment for furnishing public spaces. Hence, ICTs have created a new paradigm of hybrid space that can be defined as Senseable Spaces. Even if there are relevant cases where the adoption of ICT has made the use of public open spaces more “smart”, the interrelation and the recognition of added value need to be further developed. This is one of the motivations for the research presented in this paper. The main goal of the work reported here is the deployment of a system composed of three different connected elements (a real-world infrastructure, a data gathering system, and a data processing and analysis platform) for analysis of human behavior in the open space of Cardeto Park, in Ancona, Italy. For this purpose, and because of the complexity of this task, several actions have been carried out: the deployment of a complete real-world infrastructure in Cardeto Park, the implementation of an ad-hoc smartphone application for the gathering of participants’ data, and the development of a data pre-processing and analysis system for dealing with all the gathered data. A detailed description of these three aspects and the way in which they are connected to create a unique system is the main focus of this paper. Full article

Mobile social networking services provide efficient and easy communication, enabling users to create, retrieve and disseminate messages on the go while making their messages widely available. Despite growing evidence suggesting that geographic location and distance restrict online communication and interaction patterns, the role of geographic factors on the information dissemination in mobile social networks is often overlooked. We conducted a large-scale analysis on how the geographic factors influence the information dissemination in mobile social networks, by using two different datasets which recorded billions of users’ viewing and forwarding activities corresponding as well as the temporal and geographic information. The effects of two geographic factors, namely location and distance, on the probability and velocity of information dissemination were explored by measuring the geographic distribution of the four key indicators, namely viewing probability, forwarding probability, response time, and decision-making time. The results verify the distance decay effect of the information dissemination probability, and demonstrate that the velocity of information dissemination is not dependent on geographic distance. Furthermore, both the probability and velocity of information dissemination show heterogeneity and diversity of geographic location. Our research makes up for the gap in the relationship between geographic factors and information dissemination in mobile social networks. Our findings can provide suggestions for mobile social services, public opinion regulation and precision marketing. Full article

This paper describes the comprehensive analysis of system calibration between an optical camera and a range finder. The results suggest guidelines for accurate and efficient system calibration enabling high-quality data fusion. First, self-calibration procedures were carried out using a testbed designed for both the optical camera and range finder. The interior orientation parameters of the utilized sensors were precisely computed. Afterwards, 92 system calibration experiments were carried out according to different approaches and data configurations. For comparison of the various experimental results, two measures, namely the matching rate of fusion data and the standard deviation of relative orientation parameters derived after system calibration procedures, were considered. Among the 92 experimental cases, the best result (the matching rate of 99.08%) was shown for the use of the one-step system calibration method and six datasets from multiple columns. Also, the root mean square values of the residuals after the self- and system calibrations were less than 0.8 and 0.6 pixels, respectively. In an overall evaluation, it was confirmed that the one-step system calibration method using four or more datasets provided more stable and accurate relative orientation parameters and data fusion results than the other cases. Full article

Although air pollution is one of the most significant environmental factors posing a threat to human health worldwide, air quality data are scarce or not easily accessible in most European countries. The current work aims to develop a centralized air quality data hub that enables citizens to contribute to air quality monitoring. In this work, data from official air quality monitoring stations are combined with air pollution estimates from sky-depicting photos and from low-cost sensing devices that citizens build on their own so that citizens receive improved information about the quality of the air they breathe. Additionally, a data fusion algorithm merges air quality information from various sources to provide information in areas where no air quality measurements exist. Full article

The Quasi-Zenith Satellite System (QZSS) service area covers the Asia-Pacific region and there are four quasi-zenith satellites (QZS) in orbit with three QZS in operation until March 2018. The QZSS is not required to work in a stand-alone mode, but the system can be used to enhance the Global Positioning System (GPS) or Beidou Satellite Navigation System (BDS). The availability, position dilution of precision (PDOP), ambiguity dilution of precision (ADOP), and success rate of GPS/QZSS and BDS/QZSS under different cut-off elevation angles were compared based on a simulation. Two sets of actual QZSS data were processed and analyzed for single-frequency single-epoch (SFSE) positioning together with GPS/BDS data in this paper. Different combination forms were executed to evaluate the positioning performance of GPS/QZSS and BDS/QZSS for two baseline cases. The results indicate that QZSS is able to increase the SFSE PDOP, ADOP, and success rate of the baseline resolution and decrease the position error for GPS or BDS, especially for longer GPS baseline data. The more QZS are used, the better the enhancement effect. Full article

Allama Iqbal Open University (AIOU) is Pakistan’s largest distance learning institute, providing education to 1.4 million students. This is a fairly large setup across a country where students are highly geographically distributed. Currently, the system works using a manual approach, which is not efficient. Allocation of tutors and study centers to students plays a key role in creating a better learning environment for distance learning. Assigning tutors and study centers to distance learning students is a challenging task when there is a huge geographic spread. Using geospatial technologies in open and distance learning can fix allocation problems. This research analyzes real data from the twin cities Islamabad and Rawalpindi. The results show that geospatial technologies can be used for efficient and proper resource utilization and allocation, which in turn can save time and money. The overall idea fits into an improved distance learning framework and related analytics. Full article

The way in which risk maps are used in decision support processes for spatial planning at local scale is critical to helping decision makers in the definition of a prevention strategy to minimize risks. This paper presents a spatial decision support system that is developed to assist spatial planning by integrating the risk management component at municipal level. For the development of the RiskOTe tool, a semi-quantitative risk assessment model was used that assumes risk management to be comprehensive with respect to the type of hazard, vulnerability and risk minimization measures. In this paper, the components for the development of the spatial decision support system are identified, described and implemented using the municipality of Oeiras, in Portugal, as case study. The use of the system allowed the generation of multiple scenarios and outcomes. The results demonstrate that decision-making on the transformation of land uses by integrating risk analysis can be supported on a solid basis of information obtained from a spatial decision support system. Full article

Previous research on the geographies of food put a considerable focus on analyzing how different types of food or ingredients are consumed across different places. Little is known, however, about how food culture is manifested through various cooking traditions as well as people’s perceptions over different culinary styles. Using a data set captured from one of the largest online review sites in China (www.dianping.com), this study demonstrates how geo-referenced social review data can be leveraged to better understand the geographic prevalence and mix of regional cuisines in Chinese cities. Based on information of millions of restaurants obtained in selected cities (i.e., provincial capitals and municipalities under direct supervision of the Chinese central government), we first measure by each city the diversity of restaurants that serve regional Chinese cuisines using the Shannon entropy, and analyze how cities with different characteristics are geographically distributed. A hierarchical clustering algorithm is then used to further explore the similarities of consumers’ dining options among these cities. By associating each regional Chinese cuisine to its origin, we then develop a weighted distance measure to quantify the geographic prevalence of each cuisine type. Finally, a popularity index (POPU) is introduced to quantify consumers’ preferences for different regional cuisines. We find that: (1) diversity of restaurants among the cities shows an “east–west” contrast that is in general agreement with the socioeconomic divide in China; (2) most of the cities have their own unique characteristics, which are mainly driven by a large market share of the corresponding local cuisine; (3) there exists great heterogeneity of the geographic prevalence of different Chinese cuisines. In particular, Chuan and Xiang, which are famous for their spicy taste, are widely distributed across the mainland China and (4) among the top-tier restaurants ranked by the consumers in a city, the local cuisine is not usually favored, while other cuisines are favored by consumers in many different cities. This study demonstrates the use of social review data as a cost-effective approach of studying urban gastronomy and its relationship with human activities. Full article

Currently, huge quantities of remote sensing images (RSIs) are becoming available. Nevertheless, the scarcity of labeled samples hinders the semantic understanding of RSIs. Fortunately, many ground-level image datasets with detailed semantic annotations have been collected in the vision community. In this paper, we attempt to exploit the abundant labeled ground-level images to build discriminative models for overhead-view RSI classification. However, images from the ground-level and overhead view are represented by heterogeneous features with different distributions; how to effectively combine multiple features and reduce the mismatch of distributions are two key problems in this scene-model transfer task. Specifically, a semi-supervised manifold-regularized multiple-kernel-learning (SMRMKL) algorithm is proposed for solving these problems. We employ multiple kernels over several features to learn an optimal combined model automatically. Multi-kernel Maximum Mean Discrepancy (MK-MMD) is utilized to measure the data mismatch. To make use of unlabeled target samples, a manifold regularized semi-supervised learning process is incorporated into our framework. Extensive experimental results on both cross-view and aerial-to-satellite scene datasets demonstrate that: (1) SMRMKL has an appealing extension ability to effectively fuse different types of visual features; and (2) manifold regularization can improve the adaptation performance by utilizing unlabeled target samples. Full article

In high-resolution image data, multilevel cloud detection is a key task for remote sensing data processing. Generally, it is difficult to obtain high accuracy for multilevel cloud detection when using satellite imagery which only contains visible and near-infrared spectral bands. So, multilevel cloud detection for high-resolution remote sensing imagery is challenging. In this paper, a new multilevel cloud detection technique is proposed based on the multiple convolutional neural networks for high-resolution remote sensing imagery. In order to avoid input the entire image into the network for cloud detection, the adaptive simple linear iterative clustering (A-SCLI) algorithm was applied to the segmentation of the satellite image to obtain good-quality superpixels. After that, a new multiple convolutional neural networks (MCNNs) architecture is designed to extract multiscale features from each superpixel, and the superpixels are marked as thin cloud, thick cloud, cloud shadow, and non-cloud. The results suggest that the proposed method can detect multilevel clouds and obtain a high accuracy for high-resolution remote sensing imagery. Full article

The emergence and development of the Internet of Things (IoT) has attracted growing attention to low-cost location systems when facing the dramatically increased number of public infrastructure assets in smart cities. Various radio frequency identification (RFID)-based locating systems have been developed. However, most of them are impractical for infrastructure asset inspection and management on a large scale due to their high cost, inefficient deployment, and complex environments such as emergencies or high-rise buildings. In this paper, we proposed a novel locating system by combing the Global Navigation Satellite System (GNSS) with RFID, in which a target tag was located with one RFID reader and one GNSS receiver with sufficient accuracy for infrastructure asset management. To overcome the cost challenge, one mobile RFID reader-mounted GNSS receiver is used to simulate multiple location known reference tags. A vast number of reference tags are necessary for current RFID-based locating systems, which means higher cost. To achieve fine-grained location accuracy, we utilize a distance-based power law weight algorithm to estimate the exact coordinates. Our experiment demonstrates the effectiveness and advantages of the proposed scheme with sufficient accuracy, low cost and easy deployment on a large scale. The proposed scheme has potential applications for location-based services in smart cities. Full article

Among spatial information applications, SpatialHadoop is one of the most important systems for researchers. Broad analyses prove that SpatialHadoop outperforms the traditional Hadoop in managing distinctive spatial information operations. This paper presents a Two Dimensional Priority R-Tree (2DPR-Tree) as a new partitioning technique in SpatialHadoop. The 2DPR-Tree employs a top-down approach that effectively reduces the number of partitions accessed to answer the query, which in turn improves the query performance. The results were evaluated in different scenarios using synthetic and real datasets. This paper aims to study the quality of the generated index and the spatial query performance. Compared to other state-of-the-art methods, the proposed 2DPR-Tree improves the quality of the generated index and the query execution time. Full article

Points of interest (POIs) such as stores, gas stations, and parking lots are particularly important for maps. Using gas station as a case study, this paper proposed a novel approach to enhance POI information using low-frequency vehicle trajectory data and social media data. First, the proposed method extracted spatial information of the gas station from sparse vehicle trace data in two steps. The first step proposed the velocity sequence linear clustering algorithm to extract refueling stop tracks from the individual trace line after modeling the vehicle refueling stop behavior using movement features. The second step used the Delaunay triangulation to extract the spatial information of gas stations from the collective refueling stop tracks. Second, attribute information and dimension sentiment semantic information of the gas station were extracted from social media data using the text mining method and tripartite graph model. Third, the gas station information was enhanced by fusing the extracted spatial data and semantic data using a matching method. Experiments were conducted using the 15-day vehicle trajectories of 12,000 taxis and social media data from the Dazhongdianping in Beijing, China, and the results showed that the proposed method could extract the spatial information, attribute information, and review information of gas stations simultaneously. Compared with ground truth data, the automatically enhanced gas station was proved to be of higher quality in terms of the correctness, completeness, and real-time. Full article

Taxi behavior is a spatial–temporal dynamic process involving discrete time dependent events, such as customer pick-up, customer drop-off, cruising, and parking. Simulation models, which are a simplification of a real-world system, can help understand the effects of change of such dynamic behavior. In this paper, agent-based modeling and simulation is proposed, that describes the dynamic action of an agent, i.e., taxi, governed by behavior rules and properties, which emulate the taxi behavior. Taxi behavior simulations are fundamentally done for optimizing the service level for both taxi drivers as well as passengers. Moreover, simulation techniques, as such, could be applied to another field of application as well, where obtaining real raw data are somewhat difficult due to privacy issues, such as human mobility data or call detail record data. This paper describes the development of an agent-based simulation model which is based on multiple input parameters (taxi stay point cluster; trip information (origin and destination); taxi demand information; free taxi movement; and network travel time) that were derived from taxi probe GPS data. As such, agent’s parameters were mapped into grid network, and the road network, for which the grid network was used as a base for query/search/retrieval of taxi agent’s parameters, while the actual movement of taxi agents was on the road network with routing and interpolation. The results obtained from the simulated taxi agent data and real taxi data showed a significant level of similarity of different taxi behavior, such as trip generation; trip time; trip distance as well as trip occupancy, based on its distribution. As for efficient data handling, a distributed computing platform for large-scale data was used for extracting taxi agent parameter from the probe data by utilizing both spatial and non-spatial indexing technique. Full article

Urban agglomeration has become the predominant form of urbanization in China. In this process, spatial interaction evidently played a significant role in promoting the collaborative development of these correlated cities. The traditional urban model’s focus on individual cities should be transformed to an urban system model. In this study, a multi-scale simulation model has been proposed to simulate the agglomeration development process of the Wuhan urban agglomeration area by embedding the multi-scale spatial interaction into the transition rule system of cellular automata (CA). A system dynamic model was used to predict the demand for new urban land at an aggregated urban agglomeration area scale. A data field approach was adopted to measuring the interaction of intercity at city scale. Neighborhood interaction was interpreted with a logistic regression method at the land parcel scale. Land use data from 1995, 2005, and 2015 were used to calibrate and evaluate the model. The simulation results show that there has been continuing urban growth in the Wuhan urban agglomeration area from 1995 to 2020. Although extension-sprawl was the predominant pattern of urban spatial expansion, the trend of extensive growth to intensive growth is clear during the entire period. The spatial interaction among these cities has been reinforced, which guided the collaborative development and formed the regional urban system network. Full article

Texture features based on the gray-level co-occurrence matrix (GLCM) can effectively improve classification accuracy in geographical analyses of optical remote sensing (RS) images, with the parameters of scale of the GLCM texture window greatly affecting the validity. By analyzing human visual attention characteristics for geo-texture cognition, it was found that there is a strong correlation between the texture scale parameters and the domain shape knowledge in a specified geo-scene. Therefore, a new approach for quickly determining the multi-scale parameters of the texture with the assistance of a geographic information system (GIS) and domain knowledge is proposed in this paper. First, the validity of domain knowledge from an existing GIS database is measured by spatial data mining algorithms, including spatial partitioning, image segmentation, and space-time system evaluation. Second, the general domain shape knowledge of each category is described by the GIS minimum enclosing rectangle indices and rectangular-degree indices. Then, the corresponding multi-scale texture windows can be quickly determined for each category by a correlation analysis with the shape indices. Finally, the Fisher function is used to evaluate the validity of the scale parameters. The experimental results show that the multi-scale value keeps a one-to-one relationship with the classified objects, and their value ranges are from a few to tens, instead of the smaller values of a traditional analysis; thus, effective texture features at such a scale can be built to identify categories in a geo-scene. In this way, the proposed method can increase the total number of categories for a certain geo-scene and reduce the classification uncertainty, as well as better meet the requirements of large-scale image geo-analysis. It also has as high a calculation efficiency and as good a performance as the traditional enumeration method. Full article

In this paper, we develop a synthetic population as the first step in implementing an integrated land use/transport model. The model is agent-based, where every household, person, dwelling, and job is treated as an individual object. Therefore, detailed socioeconomic and demographic attributes are required to support the model. The Iterative Proportional Updating (IPU) procedure is selected for the optimization phase. The original IPU algorithm has been improved to handle three geographical resolutions simultaneously with very little computational time. For the allocation phase, we use Monte Carlo sampling. We applied our approach to the greater Munich metropolitan area. Based on the available data in the control totals and microdata, we selected 47 attributes at the municipality level, 13 attributes at the county level, and 14 additional attributes at the borough level for the city of Munich. Attributes are aggregated at the household, dwelling, and person level. The algorithm is able to synthesize 4.5 million persons in 2.1 million households in less than 1.5 h. Directions regarding how to handle multiple geographical resolutions and how to balance the amount and order of attributes to avoid overfitting are presented. Full article

Urban building-stocks use a significant amount of resources and energy. At the same time, they have a large potential for energy efficiency measures (EEM). To support decision-making and planning, spatial building-stock models are used to examine the current state and future development of urban building-stocks. While these models normally focus on specific cities, generic and broad stakeholder groups such as planners and policy makers are often targeted. Consequently, the visualization and communication of results are not tailored to these stakeholders. The aim of this paper is to explore the possibilities of mapping and representing energy use of urban building-stocks at different levels of aggregation and spatial distributions, to communicate with specific stakeholders involved in the urban development process. This paper uses a differentiated building-stock description based on building-specific data and measured energy use from energy performance certificates for multi-family buildings (MFB) in the city of Gothenburg. The building-stock description treats every building as unique, allowing results to be provided at any level of aggregation to suit the needs of the specific stakeholders involved. Calculated energy use of the existing stock is within 10% of the measured energy use. The potential for EEM in the existing stock is negated by the increased energy use due to new construction until 2035, using a development scenario based on current renovation rates and planned developments. Visualizations of the current energy use of the stock as well as the impact of renovation and new construction are provided, targeting specific local stakeholders. Full article

The process of searching for a dynamic constrained optimal path has received increasing attention in traffic planning, evacuation, and personalized or collaborative traffic service. As most existing multiple constrained optimal path (MCOP) methods cannot search for a path given various types of constraints that dynamically change during the search, few approaches for dynamic multiple constrained optimal path (DMCOP) with type II dynamics are available for practical use. In this study, we develop a method to solve the DMCOP problem with type II dynamics based on the unification of various types of constraints under a geometric algebra (GA) framework. In our method, the network topology and three different types of constraints are represented by using algebraic base coding. With a parameterized optimization of the MCOP algorithm based on a greedy search strategy under the generation-refinement paradigm, this algorithm is found to accurately support the discovery of optimal paths as the constraints of numerical values, nodes, and route structure types are dynamically added to the network. The algorithm was tested with simulated cases of optimal tourism route searches in China’s road networks with various combinations of constraints. The case study indicates that our algorithm can not only solve the DMCOP with different types of constraints but also use constraints to speed up the route filtering. Full article

The brown marmorated stink bug, Halyomorpha halys Stål (Hemiptera: Pentatomidae), is an invasive pest that has expanded its range outside of its original confinements in Eastern Asia, spreading through the United States, Canada and most of the European and Eurasian countries. The invasiveness of this agricultural and public nuisance pest is facilitated by the availability of an array of suitable hosts, an r-selected life history and the release from natural enemies in the invaded zones. Traditional monitoring methods are usually impeded by the lack of time and resources to sufficiently cover large geographical ranges. Therefore, the citizen science initiative “BugMap” was conceived to complement and assist researchers in breaking down the behavior of this invasive pest via a user-friendly, freely available mobile application. The collected data were employed to forecast its predicted distribution and to identify the areas at risk in Trentino, Northern Italy. Moreover, they permitted the uncovering of the seasonal invasion dynamics of this insect, besides providing insight into its phenological patterns, life cycle and potential management methods. Hence, the outcomes of this work emphasize the need to further integrate citizens in scientific endeavors to resolve ecological complications and reduce the gap between the public and science. Full article

Land-cover changes are driven by different combinations of biophysical, economic, and cultural drivers that are acting at different scales. We aimed to (1) analyze trends in land use and land cover changes (conversion, abandonment, forest persistence) in the dry Chaco in central Argentina (1979 to 2010), and (2) examine how physical and socio-economic drivers have influenced those changes. Based on Landsat data, we obtained the proportion of 16 classes of land cover changes for 81 individual circular samples. We performed a Principal Component Analysis (PCA) to identify the main trends of change across the whole region. To explore the relationships between the changes in land cover and drivers, we developed a GIS comprising thematic maps representing the different drivers. The drivers were first correlated with the two first PCA axes, and in a second approximation were subjected to multiple regression analyses. We obtained in this way the best model to explain each PCA axis. The highest conversion, as indicated by PCA axis 1, was experienced by flat areas close to roads and with the highest annual rainfall. Besides agricultural expansion that was triggered by precipitation increase as a major driver of forest conversion, changes that were observed during the period 1979–2010, may have also been influenced by several other driving forces acting at different spatial scales and contexts. Full article

This study investigates the use of a mobile application, Whale mAPP, as a citizen science tool for collecting marine mammal sighting data. In just over three months, 1261 marine mammal sightings were observed and recorded by 39 citizen scientists in Southeast Alaska. The resulting data, along with a preliminary and post-Whale mAPP questionnaires, were used to evaluate the tool’s scientific, educational, and engagement feasibility. A comparison of Whale mAPP Steller sea lion distribution data to a scientific dataset were comparable (91% overlap) given a high enough sample size (n = 73) and dense spatial coverage. In addition, after using Whale mAPP for two weeks, citizen scientists improved their marine mammal identification skills and self-initiated further learning, representing preliminary steps in developing an engaging citizen science project. While the app experienced high initial enthusiasm, maintaining prolonged commitment represents one of the fundamental challenges for this project. Increasing participation with targeted recruitment and sustained communication will help combat the limitations of sample size and spatial coverage. Overall, this study emphasizes the importance of early evaluation of the educational and scientific outcomes of a citizen science project, so that limitations are recognized and reduced. Full article