34 research outputs found
Rapid Detection of Land Cover Changes Using Crowdsourced Geographic Information: A Case Study of Beijing, China
No full textLand cover change (LCC) detection is a significant component of sustainability research including ecological economics and climate change. Due to the rapid variability of natural environment, effective LCC detection is required to capture sufficient change-related information. Although such information has been available through remotely sensed images, the complicated image processing and classification make it time consuming and labour intensive. In contrast, the freely available crowdsourced geographic information (CGI) contains easily interpreted textual information, and thus has the potential to be applied for capturing effective change-related information. Therefore, this paper presents and evaluates a method using CGI for rapid LCC detection. As a case study, Beijing is chosen as the study area, and CGI is applied to monitor LCC information. As one kind of CGI which is generated from commercial Internet maps, points of interest (POIs) with detailed textual information are utilised to detect land cover in 2016. Those POIs are first classified into land cover nomenclature based on their textual information. Then, a kernel density approach is proposed to effectively generate land cover regions in 2016. Finally, with GlobeLand30 in 2010 as baseline map, LCC is detected using the post-classification method in the period of 2010–2016 in Beijing. The result shows that an accuracy of 89.20% is achieved with land cover regions generated by POIs, indicating that POIs are reliable for rapid LCC detection. Additionally, an LCC detection comparison is proposed between remotely sensed images and CGI, revealing the advantages of POIs in terms of LCC efficiency. However, due to the uneven distribution, remotely sensed images are still required in areas with few POIs.</jats:p
A dynamic human activity‐driven model for mixed land use evaluation using social media data
No full textA Geoweb-Based Tagging System for Borderlands Data Acquisition
No full textBorderlands modeling and understanding depend on both spatial and non-spatial data, which were difficult to obtain in the past. This has limited the progress of borderland-related research. In recent years, data collection technologies have developed greatly, especially geospatial Web 2.0 technologies including blogs, publish/subscribe, mashups, and GeoRSS, which provide opportunities for data acquisition in borderland areas. This paper introduces the design and development of a Geoweb-based tagging system that enables users to tag and edit geographical information. We first establish the GeoBlog model, which consists of a set of geospatial components, posts, indicators, and comments, as the foundation of the tagging system. GeoBlog is implemented such that blogs are mashed up with OpenStreetMap. Moreover, we present an improvement to existing publish/subscribe systems with support for spatio-temporal events and subscriptions, called Spatial Publish/Subscribe, as well as the event agency network for routing messages from the publishers to the subscribers. A prototype system based on this approach is implemented in experiments. The results of this study provide an approach for asynchronous interaction and message-ordered transfer in the tagging system
Inferring Spatial Distribution Patterns in Web Maps for Land Cover Mapping
No full textWeb maps represent an effective source for land cover mapping in capturing human activities. However, due to spatial heterogeneity, previous research has mainly focused on generating land cover maps in partial areas. Inferring spatial distribution patterns in Web maps may provide an alternative perspective on improving map production on a larger scale. This paper represents a novel approach to investigating the spatial distribution in Web maps for land cover mapping. First, linear features from Web maps are utilised to delineate parcels with insufficient Web map data for classification. Then, spatial factors are constructed from point and polygon features to identify the spatial variety of Web maps, with an artificial neural network classifier being adopted to classify land cover automatically. Land cover mapping is finally proposed by combining classified parcels and existing polygon features. The proposed method is applied in Guangzhou, Guangdong Province, using a Web map from AutoNavi. The results show an approximately 88% classification accuracy and an overall mapping accuracy of 85.06%. The results indicate that the proposed approach has the potential to be utilised in land cover mapping, and the constructed spatial factors are effective at characterising land cover information
Exploring the Effects of Roadside Vegetation on the Urban Thermal Environment Using Street View Images
No full textRoadsides are important urban public spaces where residents are in direct contact with the thermal environment. Understanding the effects of different vegetation types on the roadside thermal environment has been an important aspect of recent urban research. Although previous studies have shown that the thermal environment is related to the type and configuration of vegetation, remote sensing-based technology is not applicable for extracting different vegetation types at the roadside scale. The rapid development and usage of street view data provide a way to solve this problem, as street view data have a unique pedestrian perspective. In this study, we explored the effects of different roadside vegetation types on land surface temperatures (LSTs) using street view images. First, the grasses–shrubs–trees (GST) ratios were extracted from 19,596 street view images using semantic segmentation technology, while LST and normalized difference vegetation index (NDVI) values were extracted from Landsat-8 images using the radiation transfer equation algorithm. Second, the effects of different vegetation types on roadside LSTs were explored based on geographically weighted regression (GWR), and the different performances of the analyses using remotely sensed images and street view images were discussed. The results indicate that GST vegetation has different cooling effects in different spaces, with a fitting value of 0.835 determined using GWR. Among these spaces, the areas with a significant cooling effect provided by grass are mainly located in the core commercial area of Futian District, which is densely populated by people and vehicles; the areas with a significant cooling effect provided by shrubs are mainly located in the industrial park in the south, which has the highest industrial heat emissions; the areas with a significant cooling effect provided by trees are mainly located in the core area of Futian, which is densely populated by roads and buildings. These are also the areas with the most severe heat island effect in Futian. This study expands our understanding of the relationship between roadside vegetation and the urban thermal environment, and has scientific significance for the planning and guiding of urban thermal environment regulation
Geographically Weighted Flow Cross K-Function for Network-Constrained Flow Data
No full textNetwork-constrained spatial flows are usually used to describe movements between two spatial places on a road network. The analysis of the spatial associations between different types of network-constrained spatial flows plays a key role in understanding the spatial relationships among different movements. However, existing studies usually do not consider the effect of distance decay, which may reduce the effectiveness of the detected bivariate spatial flow patterns. Moreover, most existing studies are based on the planar space assumption, which is not suitable for network-constrained spatial flows. To overcome these problems, this study proposed a new statistical method, the Geographically Weighted Network Flow Cross K-function, which improves the Flow Cross K-Function method by taking the distance decay effect and the constraints of road networks into account. Both global and local versions are extended in this study: the global version measures the overall spatial association and the local version identifies the exact locations where a spatial association occurs. The experiments on simulated datasets show that the proposed method can identify predefined bivariate flow patterns. In a case study, the proposed method is also applied to flow data comprising Xiamen taxi and ride-hailing datasets. The results demonstrate that the proposed method effectively identifies the competitive relationships between taxi and ride-hailing services
A Spatio-Temporal VGI Model Considering Trust-Related Information
No full textOver the past several years, volunteered geographic information (VGI) has expanded rapidly. VGI collection has been proven to serve as a highly successful means of acquiring timely and detailed global spatial data. However, VGI includes several special properties. For example, the contributor’s reputation affects the quality of objects edited, and a geographic object may have multiple versions. The existing spatio-temporal data model cannot describe the unique properties of VGI. Therefore, a spatio-temporal VGI model considering trust-related information is presented in this paper. In this model, central elements of the VGI environment, e.g., geographic entity, entity state, state version, contributor, reputation, geographic event, and edit event, and their interaction mechanisms are analysed. Major VGI objects and relations are determined using the object-oriented method and trust-related operations, and their relationships are analysed, and nine linkage rules among trust-related operations are found to maintain the consistency of corresponding data. A prototype system for the spatio-temporal VGI model is presented, and the effectiveness of the model is verified
Investigating Road-Constrained Spatial Distributions and Semantic Attractiveness for Area of Interest
No full textAn area of interest (AOI) refers to an urban area that attracts people’s attention within different urban functions through cities. The wide availability of big geo-data that are able to capture human activities and environmental socioeconomics enable a more nuanced identification of AOIs. Current research has proposed various approaches to delineate continuous AOI patterns using big geo-data. However, these approaches ignore the effects of urban structures such as road networks on reshaping AOIs, and fail to investigate the attractiveness and certain functions within AOIs. To fill this gap, this paper proposes a systematic framework to investigate the spatial distribution of road-constrained AOIs and analyze the semantic attractiveness. First, we propose an Epanechnikov-based kernel density estimation (KDE) with a bandwidth selection strategy to extract road-constrained AOIs. Then, we establish semantic attractiveness indices regarding AOIs based on the textual information and the number of review data. Finally, we investigate in detail the spatial distribution and semantic attractiveness of AOIs in Yuexiu, Guangzhou. The results show that road-constrained AOIs can not only effectively capture the human activity patterns influenced by urban structures, but also depict certain urban functions including entertainment, public, service, hotel, education, and food functions. This method provides a quantitative reference to monitor urban structures and human activities to support city planning
Employing Crowdsourced Geographic Information to Classify Land Cover with Spatial Clustering and Topic Model
No full textLand cover classification is the most important element of land cover mapping and is a key input to many societal benefits. Traditional classification methods require a large amount of remotely sensed images, which are time consuming and labour intensive. Recently, crowdsourcing geographic information (CGI), including geo-tagged photos and other sources, has been widely used with lower costs, but still requires extensive labour for data classification. Alternatively, CGI textual information is available from online sources containing land cover information, and it provides a useful source for land cover classification. However, the major challenge of utilising CGI is its uneven spatial distributions in land cover regions, leading to less reliability of regions for land cover classification with sparsely distributed CGI. Moreover, classifying various unorganised CGI texts automatically in each land cover region is another challenge. This paper investigates a faster and more automated method that does not require remotely sensed images for land cover classification. Spatial clustering is employed for CGI to reduce the effect of uneven spatial distributions by extracting land cover regions with high density of CGI. To classify unorganised various CGI texts in each extracted region, land cover topics are calculated using topic model. As a case study, we applied this method using points of interest (POIs) as CGI to classify land cover in Shandong province. The classification result using our proposed method achieved an overall accuracy of approximately 80%, providing evidence that CGI with textual information has a great potential for land cover classification
