Showing papers in "ISPRS international journal of geo-information in 2016"
TL;DR: A snapshot of the role of citizens in crowdsourcing geographic information is provided and a guide to the current status of this rapidly emerging and evolving subject is provided.
Abstract: Citizens are increasingly becoming an important source of geographic information, sometimes entering domains that had until recently been the exclusive realm of authoritative agencies. This activity has a very diverse character as it can, amongst other things, be active or passive, involve spatial or aspatial data and the data provided can be variable in terms of key attributes such as format, description and quality. Unsurprisingly, therefore, there are a variety of terms used to describe data arising from citizens. In this article, the expressions used to describe citizen sensing of geographic information are reviewed and their use over time explored, prior to categorizing them and highlighting key issues in the current state of the subject. The latter involved a review of ~100 Internet sites with particular focus on their thematic topic, the nature of the data and issues such as incentives for contributors. This review suggests that most sites involve active rather than passive contribution, with citizens typically motivated by the desire to aid a worthy cause, often receiving little training. As such, this article provides a snapshot of the role of citizens in crowdsourcing geographic information and a guide to the current status of this rapidly emerging and evolving subject.
304 citations
TL;DR: This position paper points out six citizen-related challenges: the engagement of citizens, the improvement of citizens’ data literacy, the pairing of quantitative and qualitative data, the need for open standards, theDevelopment of personal services, and the development of persuasive interfaces.
Abstract: The holy grail of smart cities is an integrated, sustainable approach to improve the efficiency of the city’s operations and the quality of life of citizens. At the heart of this vision is the citizen, who is the primary beneficiary of smart city initiatives, either directly or indirectly. Despite the recent surge of research and smart cities initiatives in practice, there are still a number of challenges to overcome in realizing this vision. This position paper points out six citizen-related challenges: the engagement of citizens, the improvement of citizens’ data literacy, the pairing of quantitative and qualitative data, the need for open standards, the development of personal services, and the development of persuasive interfaces. The article furthermore advocates the use of methods and techniques from GIScience to tackle these challenges, and presents the concept of an Open City Toolkit as a way of transferring insights and solutions from GIScience to smart cities.
123 citations
TL;DR: This study is the first to use remote sensing data to examine the decadal land cover changes in Saudi Arabia’s eastern coastal city of Al-Khobar between 1990 and 2013 and shows a high rate of urban sprawl and the city dispersing near the outskirts and towards the neighboring cities of Dhahran and Dammam.
Abstract: While several studies examined land use and land cover changes in the central and western parts of Saudi Arabia, this study is the first to use remote sensing data to examine the decadal land cover changes in Saudi Arabia’s eastern coastal city of Al-Khobar between 1990 and 2013. Specifically, it utilized ISODATA classification method to classify Landsat TM, ETM+, and OLI data collected from 1990, 2001, and 2013 and then detected changes in the land cover within the study area. It then measured urban sprawl by calculating the relative Shannon’s entropy index values for the three years. With overall classification accuracies greater than 85%, the results show that urban built-up areas increased by 117% between 1990 and 2001 and 43.51% from 2001 to 2013. Vegetation increased by 110% from 1990 to 2001 and by 52% between 2001 and 2013. The entropy index values of 0.700 (1990), 0.779 (2001), and 0.840 (2013) indicates a high rate of urban sprawl and the city dispersing near the outskirts and towards the neighboring cities of Dhahran and Dammam. Future studies should examine the current challenges faced by the city’s residents due to urban expansion and attempt to find ways to resolve them in the near future.
114 citations
TL;DR: This article presents a methodology to acquire and process high resolution data for coastal zones acquired by a vertical take off and landing (VTOL) unmanned aerial vehicle (UAV) attached to a small commercial camera and shows that the presented methodology is a robust tool for the classification, 3D visualization, and mapping of coastal morphology.
Abstract: Spatial data acquisition is a critical process for the identification of the coastline and coastal zones for scientists involved in the study of coastal morphology. The availability of very high-resolution digital surface models (DSMs) and orthophoto maps is of increasing interest to all scientists, especially those monitoring small variations in the earth’s surface, such as coastline morphology. In this article, we present a methodology to acquire and process high resolution data for coastal zones acquired by a vertical take off and landing (VTOL) unmanned aerial vehicle (UAV) attached to a small commercial camera. The proposed methodology integrated computer vision algorithms for 3D representation with image processing techniques for analysis. The computer vision algorithms used the structure from motion (SfM) approach while the image processing techniques used the geographic object-based image analysis (GEOBIA) with fuzzy classification. The SfM pipeline was used to construct the DSMs and orthophotos with a measurement precision in the order of centimeters. Consequently, GEOBIA was used to create objects by grouping pixels that had the same spectral characteristics together and extracting statistical features from them. The objects produced were classified by fuzzy classification using the statistical features as input. The classification output classes included beach composition (sand, rubble, and rocks) and sub-surface classes (seagrass, sand, algae, and rocks). The methodology was applied to two case studies of coastal areas with different compositions: a sandy beach with a large face and a rubble beach with a small face. Both are threatened by beach erosion and have been degraded by the action of sea storms. Results show that the coastline, which is the low limit of the swash zone, was detected successfully by both the 3D representations and the image classifications. Furthermore, several traces representing previous sea states were successfully recognized in the case of the sandy beach, while the erosion and beach crests were detected in the case of the rubble beach. The achieved level of detail of the 3D representations revealed new beach characteristics, including erosion crests, berm zones, and sand dunes. In conclusion, the UAV SfM workflow provides information in a spatial resolution that permits the study of coastal changes with confidence and provides accurate 3D visualizations of the beach zones, even for areas with complex topography. The overall results show that the presented methodology is a robust tool for the classification, 3D visualization, and mapping of coastal morphology.
95 citations
TL;DR: A novel device-free WiFi-based gesture recognition system (WiGeR) by leveraging the fluctuations in the channel state information (CSI) of WiFi signals caused by hand motions to classify gestures with high accuracy.
Abstract: Recently, researchers around the world have been striving to develop and modernize human–computer interaction systems by exploiting advances in modern communication systems. The priority in this field involves exploiting radio signals so human–computer interaction will require neither special devices nor vision-based technology. In this context, hand gesture recognition is one of the most important issues in human–computer interfaces. In this paper, we present a novel device-free WiFi-based gesture recognition system (WiGeR) by leveraging the fluctuations in the channel state information (CSI) of WiFi signals caused by hand motions. We extract CSI from any common WiFi router and then filter out the noise to obtain the CSI fluctuation trends generated by hand motions. We design a novel and agile segmentation and windowing algorithm based on wavelet analysis and short-time energy to reveal the specific pattern associated with each hand gesture and detect duration of the hand motion. Furthermore, we design a fast dynamic time warping algorithm to classify our system’s proposed hand gestures. We implement and test our system through experiments involving various scenarios. The results show that WiGeR can classify gestures with high accuracy, even in scenarios where the signal passes through multiple walls.
91 citations
TL;DR: The results of this study have key implications for sustainable urban planning and development; to mitigate urban heat island effects it is important to not only increase canopy cover or the size of urban green spaces, but also to optimize their spatial configuration.
Abstract: An essential part of urban natural systems, urban green spaces play a crucial role in mitigating the urban heat island effect (UHI). The UHI effect refers to the phenomenon where the temperature within a city is higher than that of the surrounding rural areas. The effects of the spatial composition and configuration of urban green spaces on urban land surface temperature (LST) have recently been documented. However, few studies have examined the effects of the directionality and distribution of green spaces on LST. In this study, we used a landscape index to describe the change in pattern of heat island intensity for the city of Baotou, China. We then used a semi-variable function and nearest neighbor algorithm to analyze the cooling effects of green spaces. We found that: (1) the cooling distance of an urban green space was not only influenced by its size, vegetation cover, and shape, but also showed anisotropy. In general, the larger the area of the urban green space and the higher the value of Normalized Difference Vegetation Index (NDVI; a measure of plant photosynthetic activity), the larger the cooling distance within a certain threshold. Green spaces with more regular shapes displayed higher LST mitigation; however, the cooling distance was directional, and cooling effects depended on the semi-major axis and semi-minor axis of the green space. (2) The distribution of the urban green space within the landscape played a key role in mitigating the UHI effect. Within a certain area, the cooling effect of green spaces that are evenly distributed was greater than that which was associated with either green spaces that were large in area or where greens spaces were aggregated in the landscape. Therefore, within urban areas, where space is limited, urban planning should account for green spaces that are relatively scattered and evenly distributed to maximize cooling effects. The results of this study have key implications for sustainable urban planning and development; to mitigate urban heat island effects it is important to not only increase canopy cover or the size of urban green spaces, but also to optimize their spatial configuration.
85 citations
TL;DR: The analysis revealed that ULC was more intense or faster during the 2000s than in the 1990s, coinciding with the trends of population and economic growth, and revealed that by 2030 and 2050, the CMA’s built-up land will increase to 42,500 ha and 56,000 ha, respectively.
Abstract: Understanding urban growth spatiotemporally is important for landscape and urban development planning In this study, we examined the spatiotemporal pattern of urban growth of the Colombo Metropolitan Area (CMA)—Sri Lanka’s only metropolitan area—from 1992 to 2014 using remote sensing data and GIS techniques First, we classified three land-use/cover maps of the CMA (ie, for 1992, 2001, and 2014) using Landsat data Second, we examined the temporal pattern of urban land changes (ULCs; ie, land changes from non-built-up to built-up) across two time intervals (1992–2001 and 2001–2014) Third, we examined the spatial pattern of ULCs along the gradients of various driver variables (eg, distance to roads) and by using spatial metrics Finally, we predicted the future urban growth of the CMA (2014–2050) Our results revealed that the CMA’s built-up land has increased by 24,711 ha (221%) over the past 22 years (11,165 ha in 1992 to 35,876 ha in 2014), at a rate of 1123 ha per year The analysis revealed that ULC was more intense or faster during the 2000s (1268 ha per year) than in the 1990s (914 ha per year), coinciding with the trends of population and economic growth The results also revealed that most of the ULCs in both time intervals occurred in close proximity to roads and schools, while also showing some indications of landscape fragmentation and infill urban development patterns The ULC modeling revealed that by 2030 and 2050, the CMA’s built-up land will increase to 42,500 ha and 56,000 ha, respectively Most of these projected gains of built-up land will be along the transport corridors and in proximity to the growth nodes These findings are important in the context of landscape and urban development planning for the CMA Overall, this study provides valuable information on the landscape transformation of the CMA, also highlighting some important challenges facing its future sustainable urban development
77 citations
TL;DR: Bibliometric analysis results from this study will greatly facilitate the understanding of the progress and trends in artificial intelligence, in particular, for those researchers interested in domain-specific AI-driven problem-solving.
Abstract: In this article, we conducted the evaluation of artificial intelligence research from 1990–2014 by using bibliometric analysis. We introduced spatial analysis and social network analysis as geographic information retrieval methods for spatially-explicit bibliometric analysis. This study is based on the analysis of data obtained from database of the Science Citation Index Expanded (SCI-Expanded) and Conference Proceedings Citation Index-Science (CPCI-S). Our results revealed scientific outputs, subject categories and main journals, author productivity and geographic distribution, international productivity and collaboration, and hot issues and research trends. The growth of article outputs in artificial intelligence research has exploded since the 1990s, along with increasing collaboration, reference, and citations. Computer science and engineering were the most frequently-used subject categories in artificial intelligence studies. The top twenty productive authors are distributed in countries with a high investment of research and development. The United States has the highest number of top research institutions in artificial intelligence, producing most single-country and collaborative articles. Although there is more and more collaboration among institutions, cooperation, especially international ones, are not highly prevalent in artificial intelligence research as expected. The keyword analysis revealed interesting research preferences, confirmed that methods, models, and application are in the central position of artificial intelligence. Further, we found interesting related keywords with high co-occurrence frequencies, which have helped identify new models and application areas in recent years. Bibliometric analysis results from our study will greatly facilitate the understanding of the progress and trends in artificial intelligence, in particular, for those researchers interested in domain-specific AI-driven problem-solving. This will be of great assistance for the applications of AI in alternative fields in general and geographic information science, in particular.
76 citations
TL;DR: This paper proposes to detect the intersections under this definition by finding the common sub-tracks of the GPS traces using the dynamic programming approach, and shows that the proposed method outperforms the turning point-based methods in terms of the F-score.
Abstract: Intersections are important components of road networks, which are critical to both route planning and path optimization. Most existing methods define the intersections as locations where the road users change their moving directions and identify the intersections from GPS traces through analyzing the road users’ turning behaviors. However, these methods suffer from finding an appropriate threshold for the moving direction change, leading to true intersections being undetected or spurious intersections being falsely detected. In this paper, the intersections are defined as locations that connect three or more road segments in different directions. We propose to detect the intersections under this definition by finding the common sub-tracks of the GPS traces. We first detect the Longest Common Subsequences (LCSS) between each pair of GPS traces using the dynamic programming approach. Second, we partition the longest nonconsecutive subsequences into consecutive sub-tracks. The starting and ending points of the common sub-tracks are collected as connecting points. At last, intersections are detected from the connecting points through Kernel Density Estimation (KDE). Experimental results show that our proposed method outperforms the turning point-based methods in terms of the F-score.
72 citations
TL;DR: A Wi-Fi and PDR (pedestrian dead reckoning) real-time fusion scheme is proposed in this paper to perform fusing calculation by adaptively determining the dynamic noise of a filtering system according to pedestrian movement, which can effectively restrain the jumping or accumulation phenomena of wireless positioning and the PDR error accumulation problem.
Abstract: Wireless signal strength is susceptible to the phenomena of interference, jumping, and instability, which often appear in the positioning results based on Wi-Fi field strength fingerprint database technology for indoor positioning. Therefore, a Wi-Fi and PDR (pedestrian dead reckoning) real-time fusion scheme is proposed in this paper to perform fusing calculation by adaptively determining the dynamic noise of a filtering system according to pedestrian movement (straight or turning), which can effectively restrain the jumping or accumulation phenomena of wireless positioning and the PDR error accumulation problem. Wi-Fi fingerprint matching typically requires a quite high computational burden: To reduce the computational complexity of this step, the affinity propagation clustering algorithm is adopted to cluster the fingerprint database and integrate the information of the position domain and signal domain of respective points. An experiment performed in a fourth-floor corridor at the School of Environment and Spatial Informatics, China University of Mining and Technology, shows that the traverse points of the clustered positioning system decrease by 65%–80%, which greatly improves the time efficiency. In terms of positioning accuracy, the average error is 4.09 m through the Wi-Fi positioning method. However, the positioning error can be reduced to 2.32 m after integration of the PDR algorithm with the adaptive noise extended Kalman filter (EKF).
70 citations
TL;DR: The support vector machine (SVM), which has been claimed to be relatively insensitive to training data error, was the most sensitive of the set of classifiers investigated, with overall classification accuracy declining by 8% with the use of a training set containing 20% mislabelled cases.
Abstract: The accuracy of a map is dependent on the reference dataset used in its construction. Classification analyses used in thematic mapping can, for example, be sensitive to a range of sampling and data quality concerns. With particular focus on the latter, the effects of reference data quality on land cover classifications from airborne thematic mapper data are explored. Variations in sampling intensity and effort are highlighted in a dataset that is widely used in mapping and modelling studies; these may need accounting for in analyses. The quality of the labelling in the reference dataset was also a key variable influencing mapping accuracy. Accuracy varied with the amount and nature of mislabelled training cases with the nature of the effects varying between classifiers. The largest impacts on accuracy occurred when mislabelling involved confusion between similar classes. Accuracy was also typically negatively related to the magnitude of mislabelled cases and the support vector machine (SVM), which has been claimed to be relatively insensitive to training data error, was the most sensitive of the set of classifiers investigated, with overall classification accuracy declining by 8% (significant at 95% level of confidence) with the use of a training set containing 20% mislabelled cases.
TL;DR: An inventory of the metadata that are collected with geo-tagged photographs is provided and what elements would be essential, desirable, or unnecessary for three use cases related to land cover: Calibration, validation and verification.
Abstract: Geo-tagged photographs are used increasingly as a source of Volunteered Geographic Information (VGI), which could potentially be used for land use and land cover applications. The purpose of this paper is to analyze the feasibility of using this source of spatial information for three use cases related to land cover: Calibration, validation and verification. We first provide an inventory of the metadata that are collected with geo-tagged photographs and then consider what elements would be essential, desirable, or unnecessary for the aforementioned use cases. Geo-tagged photographs were then extracted from Flickr, Panoramio and Geograph for an area of London, UK, and classified based on their usefulness for land cover mapping including an analysis of the accompanying metadata. Finally, we discuss protocols for geo-tagged photographs for use of VGI in relation to land cover applications.
TL;DR: This paper discusses the analytical opportunities that recent remote sensing data offer in regard to an objective and transparent measurement of built density patterns of city regions and clarifies the interrelations between built and activity densities.
Abstract: Urban density must be considered a key concept in the description of a city’s urban spatial structure. Countless studies have provided evidence of a close relationship between built density and activity densities, on the one hand, and urban environmental conditions or social practices, on the other hand. However, despite the concept’s common use in urban research, urban density is a rather fuzzy and highly complex concept that is accompanied by a confusing variety of indicators and measurement approaches. To date, an internationally-accepted standard for the implementation of density indicators that permits a robust comparison of different countries, regions or cities is widely missing. This paper discusses the analytical opportunities that recent remote sensing data offer in regard to an objective and transparent measurement of built density patterns of city regions. It furthermore clarifies the interrelations between built and activity densities. We apply our approach to four German city regions to demonstrate the analytical capacity of spatially-refined density indicators for the purposes of comparative urban research at a regional scale. In so doing, we contribute to a more encompassing and robust understanding of the urban density concept when analyzing regional morphology.
TL;DR: The potential of light UAVs (Unmanned Aerial Vehicles) for monitoring sedimentary hydrodynamics at different spatial scales in a silty estuary is shown and the UAV dataset enables multi-scale approaches from the study of large areas to the Morphodynamics of smaller-scale sedimentary structures and the morphodynamics impact of plant ground cover.
Abstract: Intertidal mudflats play a critical role in estuarine exchange, connecting marine and continental supplies of nutrients and sediments. However, their complex morphodynamics, associated with a wide range of physical and biological processes, are still poorly understood and require further field investigation. In addition, mudflats are challenging areas for Structure-from-Motion (SfM) photogrammetric surveys. Indeed, the mudflats generally hold back residual tidal water, which can make stereo restitution particularly difficult because of poor correlations or sun-glint effects. This study aims to show the potential of light UAVs (Unmanned Aerial Vehicles) for monitoring sedimentary hydrodynamics at different spatial scales in a silty estuary. For each UAV mission an orthophotograph and a Digital Elevation Model (DEM) are computed. From repeated surveys the diachronic evolution of the area can be observed via DEM differencing. Considering the ground texture in such a context, the stereo restitution process is made possible because of the high spatial resolution of the UAV photographs. Providing a synoptic view as well as high spatial resolution (less than 4 cm), the UAV dataset enables multi-scale approaches from the study of large areas to the morphodynamics of smaller-scale sedimentary structures and the morphodynamics impact of plant ground cover.
TL;DR: A systematic literature review aims at identifying current research and directions for future research in terms of Volunteered Geographic Information (VGI) within natural hazard analysis, and reveals auspicious approaches regarding community engagement and data fusion, but also important research gaps.
Abstract: With the rise of new technologies, citizens can contribute to scientific research via Web 2.0 applications for collecting and distributing geospatial data. Integrating local knowledge, personal experience and up-to-date geoinformation indicates a promising approach for the theoretical framework and the methods of natural hazard analysis. Our systematic literature review aims at identifying current research and directions for future research in terms of Volunteered Geographic Information (VGI) within natural hazard analysis. Focusing on both the preparedness and mitigation phase results in eleven articles from two literature databases. A qualitative analysis for in-depth information extraction reveals auspicious approaches regarding community engagement and data fusion, but also important research gaps. Mainly based in Europe and North America, the analysed studies deal primarily with floods and forest fires, applying geodata collected by trained citizens who are improving their knowledge and making their own interpretations. Yet, there is still a lack of common scientific terms and concepts. Future research can use these findings for the adaptation of scientific models of natural hazard analysis in order to enable the fusion of data from technical sensors and VGI. The development of such general methods shall contribute to establishing the user integration into various contexts, such as natural hazard analysis.
TL;DR: Using multi-source remote sensing data, including satellite and UAV imagery simultaneously, results in an effective assessment of gully erosion over multiple spatial scales, and the combined approach should be continued to regularly monitor gull erosion to understand the erosion process and its relationship with the environment from a comprehensive perspective.
Abstract: This research is focused on gully erosion mapping and monitoring at multiple spatial scales using multi-source remote sensing data of the Sancha River catchment in Northeast China, where gullies extend over a vast area. A high resolution satellite image (Pleiades 1A, 0.7 m) was used to obtain the spatial distribution of the gullies of the overall basin. Image visual interpretation with field verification was employed to map the geometric gully features and evaluate gully erosion as well as the topographic differentiation characteristics. Unmanned Aerial Vehicle (UAV) remote sensing data and the 3D photo-reconstruction method were employed for detailed gully mapping at a site scale. The results showed that: (1) the sub-meter image showed a strong ability in the recognition of various gully types and obtained satisfactory results, and the topographic factors of elevation, slope and slope aspects exerted significant influence on the gully spatial distribution at the catchment scale; and (2) at a more detailed site scale, UAV imagery combined with 3D photo-reconstruction provided a Digital Surface Model (DSM) and ortho-image at the centimeter level as well as a detailed 3D model. The resulting products revealed the area of agricultural utilization and its shaping by human agricultural activities and water erosion in detail, and also provided the gully volume. The present study indicates that using multi-source remote sensing data, including satellite and UAV imagery simultaneously, results in an effective assessment of gully erosion over multiple spatial scales. The combined approach should be continued to regularly monitor gully erosion to understand the erosion process and its relationship with the environment from a comprehensive perspective.
TL;DR: A multi-elemental location inference method that puts the geotagging aside and tries to predict the location of tweets by exploiting the other inherently attached data elements, which is a significant improvement compared with that of the current methods that can predict the locations with much larger distance errors or at a city-level resolution at best.
Abstract: Since its inception, Twitter has played a major role in real-world events—especially in the aftermath of disasters and catastrophic incidents, and has been increasingly becoming the first point of contact for users wishing to provide or seek information about such situations. The use of Twitter in emergency response and disaster management opens up avenues of research concerning different aspects of Twitter data quality, usefulness and credibility. A real challenge that has attracted substantial attention in the Twitter research community exists in the location inference of twitter data. Considering that less than 2% of tweets are geotagged, finding location inference methods that can go beyond the geotagging capability is undoubtedly the priority research area. This is especially true in terms of emergency response, where spatial aspects of information play an important role. This paper introduces a multi-elemental location inference method that puts the geotagging aside and tries to predict the location of tweets by exploiting the other inherently attached data elements. In this regard, textual content, users’ profile location and place labelling, as the main location-related elements, are taken into account. Location-name classes in three granularity levels are defined and employed to look up the location references from the location-associated elements. The inferred location of the finest granular level is assigned to a tweet, based on a novel location assignment rule. The location assigned by the location inference process is considered to be the inferred location of a tweet, and is compared with the geotagged coordinates as the ground truth of the study. The results show that this method is able to successfully infer the location of 87% of the tweets at the average distance error of 12.2 km and the median distance error of 4.5 km, which is a significant improvement compared with that of the current methods that can predict the location with much larger distance errors or at a city-level resolution at best.
TL;DR: Experiments showed that the proposed spatiotemporal gradient–boosted regression tree model obtained better results than gradient boosting, random forest, or autoregressive integrated moving average approaches for urban link travel time prediction.
Abstract: The prediction of travel times is challenging because of the sparseness of real-time traffic data and the intrinsic uncertainty of travel on congested urban road networks. We propose a new gradient–boosted regression tree method to accurately predict travel times. This model accounts for spatiotemporal correlations extracted from historical and real-time traffic data for adjacent and target links. This method can deliver high prediction accuracy by combining simple regression trees with poor performance. It corrects the error found in existing models for improved prediction accuracy. Our spatiotemporal gradient–boosted regression tree model was verified in experiments. The training data were obtained from big data reflecting historic traffic conditions collected by probe vehicles in Wuhan from January to May 2014. Real-time data were extracted from 11 weeks of GPS records collected in Wuhan from 5 May 2014 to 20 July 2014. Based on these data, we predicted link travel time for the period from 21 July 2014 to 25 July 2014. Experiments showed that our proposed spatiotemporal gradient–boosted regression tree model obtained better results than gradient boosting, random forest, or autoregressive integrated moving average approaches. Furthermore, these results indicate the advantages of our model for urban link travel time prediction.
TL;DR: A two-step clustering approach to extract individuals’ locations according to their GPS trajectory data using the spatio-temporal clustering algorithm based on time and distance is proposed.
Abstract: High-accuracy location identification is the basis of location awareness and location services. However, because of the influence of GPS signal loss, data drift and repeated access in the individual trajectory data, the efficiency and accuracy of existing algorithms have some deficiencies. Therefore, we propose a two-step clustering approach to extract individuals’ locations according to their GPS trajectory data. Firstly, we defined three different types of stop points; secondly, we extracted these points from the trajectory data by using the spatio-temporal clustering algorithm based on time and distance. The experimental results show that the spatio-temporal clustering algorithm outperformed traditional extraction algorithms. It can avoid the problems caused by repeated access and can substantially reduce the effects of GPS signal loss and data drift. Finally, an improved clustering algorithm based on a fast search and identification of density peaks was applied to discover the trajectory locations. Compared to the existing algorithms, our method shows better performance and accuracy.
TL;DR: The dynamics of dengue fever in China is reported using messages from Weibo to predict how infectious diseases develop and evolve both spatially and temporally.
Abstract: Social media activity has become an important component of daily life for many people. Messages from Twitter (US) and Weibo (China) have shown their potential as important data sources for detecting and analyzing infectious diseases. Such emerging and dynamic new data sources allow us to predict how infectious diseases develop and evolve both spatially and temporally. We report the dynamics of dengue fever in China using messages from Weibo. We first extract and construct a list of keywords related to dengue fever in order to analyze how frequently these words appear in Weibo messages based on the Latent Dirichlet Allocation (LDA). Spatial analysis is then applied to detect how dengue fever cases cluster spatially and spread over time.
TL;DR: In this article, the authors argue that the confusion and frustration arise from traditional Euclidean geometric thinking, in which locations, directions, and sizes are considered absolute, and it is now time to revise this conventional thinking.
Abstract: Scale is a fundamental concept that has attracted persistent attention in geography literature over the past several decades. However, it creates enormous confusion and frustration, particularly in the context of geographic information science, because of scale-related issues such as image resolution and the modifiable areal unit problem (MAUP). This paper argues that the confusion and frustration arise from traditional Euclidean geometric thinking, in which locations, directions, and sizes are considered absolute, and it is now time to revise this conventional thinking. Hence, we review fractal geometry, together with its underlying way of thinking, and compare it to Euclidean geometry. Under the paradigm of Euclidean geometry, everything is measurable, no matter how big or small. However, most geographic features, due to their fractal nature, are essentially unmeasurable or their sizes depend on scale. For example, the length of a coastline, the area of a lake, and the slope of a topographic surface are all scale-dependent. Seen from the perspective of fractal geometry, many scale issues, such as the MAUP, are inevitable. They appear unsolvable, but can be dealt with. To effectively deal with scale-related issues, we present topological and scaling analyses illustrated by street-related concepts such as natural streets, street blocks, and natural cities. We further contend that one of the two spatial properties, spatial heterogeneity, is de facto the fractal nature of geographic features, and it should be considered the first effect among the two, because it is global and universal across all scales, which should receive more attention from practitioners of geography.
TL;DR: A novel local implementation of the well-known morphological descriptors called pattern spectra, computationally efficient histogram-like structures describing the global distribution of arbitrarily defined attributes of connected image components, for the first time in this context.
Abstract: The rapidly increasing volume of visual Earth Observation data calls for effective content based image retrieval solutions, specifically tailored for their high spatial resolution and heterogeneous content. In this paper, we address this issue with a novel local implementation of the well-known morphological descriptors called pattern spectra. They are computationally efficient histogram-like structures describing the global distribution of arbitrarily defined attributes of connected image components. Besides employing pattern spectra for the first time in this context, our main contribution lies in their dense calculation, at a local scale, thus enabling their combination with sophisticated visual vocabulary strategies. The Merced Landuse/Landcover dataset has been used for comparing the proposed strategy against alternative global and local content description methods, where the introduced approach is shown to yield promising performances.
TL;DR: It is found that interpolation error strongly affects the correct interpretation of the car’s dynamics, whereas its impact on the path was moderate and recommendations for recording of FCD using the GPS were given.
Abstract: Floating car data (FCD) recorded with the Global Positioning System (GPS) are an important data source for traffic research. However, FCD are subject to error, which can relate either to the accuracy of the recordings (measurement error) or to the temporal rate at which the data are sampled (interpolation error). Both errors affect movement parameters derived from the FCD, such as speed or direction, and consequently influence conclusions drawn about the movement. In this paper we combined recent findings about the autocorrelation of GPS measurement error and well-established findings from random walk theory to analyse a set of real-world FCD. First, we showed that the measurement error in the FCD was affected by positive autocorrelation. We explained why this is a quality measure of the data. Second, we evaluated four metrics to assess the influence of interpolation error. We found that interpolation error strongly affects the correct interpretation of the car’s dynamics (speed, direction), whereas its impact on the path (travelled distance, spatial location) was moderate. Based on these results we gave recommendations for recording of FCD using the GPS. Our recommendations only concern time-based sampling, change-based, location-based or event-based sampling are not discussed. The sampling approach minimizes the effects of error on movement parameters while avoiding the collection of redundant information. This is crucial for obtaining reliable results from FCD.
TL;DR: This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.
Abstract: The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully-affected areas detection is the basic work in this region for gully erosion assessment and monitoring. For the first time, an unmanned aerial vehicle (UAV) was applied to extract gully features in this region. Two typical catchments in Changwu and Ansai were selected to represent loess tableland and loess hilly regions, respectively. A high-powered quadrocopter (md4-1000) equipped with a non-metric camera was used for image acquisition. InPho and MapMatrix were applied for semi-automatic workflow including aerial triangulation and model generation. Based on the stereo-imaging and the ground control points, the highly detailed digital elevation models (DEMs) and ortho-mosaics were generated. Subsequently, an object-based approach combined with the random forest classifier was designed to detect gully-affected areas. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The overall extraction accuracy in Changwu and Ansai achieved was 84.62% and 86.46%, respectively, which indicated the potential of the proposed workflow for extracting gully features. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.
TL;DR: Assessment of the land degradation dynamic using a time series of summed normalized difference vegetation index (NDVI) based on a trend analysis of the Theil-Sen slope and Mann-Kendall test provides a scientific basis for the management of ecological restoration programs.
Abstract: Land degradation is a major threat to the sustainability of human habitation, and it is essential to assess it quantitatively. Assessment of the human-induced aspect is especially important for planning appropriate prevention measures. This paper used the Three-North Shelter Forest Program region as the study area, and assessed the land degradation dynamic using a time series of summed normalized difference vegetation index (NDVI) based on a trend analysis of the Theil-Sen slope and Mann-Kendall test. The human-induced land degradation was separated from degradation driven by climate using the meteorological dataset through the residual trend (RESTREND) method for the period 1982–2006. The results showed that (1) the NDVI in the study area mainly exhibited an increasing trend, approximately 13.00% of the study area experienced significantly positive NDVI trends and 6.20% showed decline. Furthermore, (2) the correlation between the summed NDVI and precipitation was higher than the correlation between NDVI and temperature, suggesting that precipitation was the most essential factor that impacted NDVI dynamic in the study area; (3) The significant trends of vegetation by anthropogenic disturbances were detected, which were significant positive and negative trends of 11.93% and 6.19%, respectively. All of these findings enrich our knowledge of human activities that impact land degradation in arid or semi-arid regions and provide a scientific basis for the management of ecological restoration programs.
TL;DR: This study developed a method to identify spatiotemporal patterns of human convergence and divergence and their evolutions over time using large-scale mobile phone location data from Shenzhen, China, and discussed in the context of urban functional regions.
Abstract: Investigating human mobility patterns can help researchers and agencies understand the driving forces of human movement, with potential benefits for urban planning and traffic management. Recent advances in location-aware technologies have provided many new data sources (e.g., mobile phone and social media data) for studying human space-time behavioral regularity. Although existing studies have utilized these new datasets to characterize human mobility patterns from various aspects, such as predicting human mobility and monitoring urban dynamics, few studies have focused on human convergence and divergence patterns within a city. This study aims to explore human spatial convergence and divergence and their evolutions over time using large-scale mobile phone location data. Using a dataset from Shenzhen, China, we developed a method to identify spatiotemporal patterns of human convergence and divergence. Eight distinct patterns were extracted, and the spatial distributions of these patterns are discussed in the context of urban functional regions. Thus, this study investigates urban human convergence and divergence patterns and their relationships with the urban functional environment, which is helpful for urban policy development, urban planning and traffic management.
TL;DR: This case study divided Beijing into regular grid cells and extracted activity centers for each social media user and found that sets obtained by aggregating user activity centers have a better delimitating effect than sets obtained without aggregation.
Abstract: Delimitating trade areas is a major business concern. Today, mobile communication technologies make it possible to use social media data for this purpose. Few studies however, have focused on methods to extract suitable samples from social media data for trade area delimitation. In our case study, we divided Beijing into regular grid cells and extracted activity centers for each social media user. Ten sample sets were obtained by selecting users based on the retail agglomerations they visited and aggregating user activity centers to each grid cell. We calculated distance and visitation frequency attributes for each user and each grid cell. The distance value of a grid cell is the average distance of user activity centers in this grid cell to a retail agglomeration. The visitation frequency of a grid cell refers to the average count of visits to retail agglomerations by user activity centers for a cell. The calculated attribute values of 10 sets were input into a Huff model and the delimitated trade areas were evaluated. Results show that sets obtained by aggregating user activity centers have a better delimitating effect than sets obtained without aggregation. Differences in the distribution and intensity of trade areas also became apparent.
TL;DR: This paper identifies three fields of transport modeling where the spatial perspective can significantly contribute to a more efficient modeling process and more reliable model results, namely, geospatial data, disaggregated transport models and the role of geo-visualization.
Abstract: The movement and transport of people and goods is spatial by its very nature. Thus, geospatial fundamentals of transport systems need to be adequately considered in transport models. Until recently, this was not always the case. Instead, transport research and geography evolved widely independently in domain silos. However, driven by recent conceptual, methodological and technical developments, the need for an integrated approach is obvious. This paper attempts to outline the potential of Geographical Information Systems (GIS) for transport modeling. We identify three fields of transport modeling where the spatial perspective can significantly contribute to a more efficient modeling process and more reliable model results, namely, geospatial data, disaggregated transport models and the role of geo-visualization. For these three fields, available findings from various domains are compiled, before open aspects are formulated as research directions, with exemplary research questions. The overall aim of this paper is to strengthen the spatial perspective in transport modeling and to call for a further integration of GIS in the domain of transport modeling.
TL;DR: The results showed that theme parks established in the early 1990s were the most popular tourist attractions in Shenzhen, but a negative trend was detected in the check-in population and the homogeneity and inconvenient traffic conditions of major tourist destinations leading to the construction of a tourism tour chain has become a challenge.
Abstract: Location-based service information, provided by social networks, provides new data sources and perspectives to research tourism activities, especially in highly populated mega-cities. Based on three years (2012–2014) of approximately 340,000 check-in records collected from Sina micro-blog at 86 tourist attractions in Shenzhen, a first-tier city in southern China, we conducted a comprehensive study of the attraction features involving different aspects, such as tourist source, duration of stay, check-in activity index, and attraction correlation degree. The results showed that (1) theme parks established in the early 1990s were the most popular tourist attractions in Shenzhen, but a negative trend was detected in the check-in population; (2) compared with check-in times from surrounding activities and the kernel density of tourists, most destinations in Shenzhen showed a lack of attraction, failing to make the most of their geographic accessibility; and (3) the homogeneity and inconvenient traffic conditions of major tourist destinations leading to the construction of a tourism tour chain has become a challenge. The results of this study demonstrate the potential of big-data mining and provide valuable insights into tourism market design and management in mega-cities.
TL;DR: This paper presents a geocoding service based on an optimized Chinese address matching method, including address modeling, address standardization and address matching, implemented in practice using a large quantity of data from Shenzhen Municipality.
Abstract: With the coming era of big data and the rapid development and widespread applications of Geographical Information Systems (GISs), geocoding technology is playing an increasingly important role in bridging the gap between non-spatial data resources and spatial data in various fields. However, Chinese geocoding faces great challenges because of the complexity of the address string format in Chinese, which contains no delimiters between Chinese words, and the poor address management resulting from the existence of multiple address authorities spread among different governmental agencies. This paper presents a geocoding service based on an optimized Chinese address matching method, including address modeling, address standardization and address matching. The address model focuses on the spatial semantics of each address element, and the address standardization process is based on an address tree model. A geocoding service application is implemented in practice using a large quantity of data from Shenzhen Municipality. More than 1,460,000 data records were used to test the geocoding service, and good matching rates were achieved with good adaptability and intelligence.