Yandong Wang

Three-dimensional modeling and application in geological exploration engineering

Taking geological exploration engineering as the topic of our research, we presented a hybrid data model integrating vector and raster data, and proposed a sub-model; i.e., a quasi tri-prism volume (QTPV) model. Based on QTPV, the concept model, logical model and data structure were designed in detail. Modeling, model cutting and partition algorithms on QTPV are proposed. The modeling features of QTPV have been discussed. The QTPV model is more efficient than 3D FDS and TEN in terms of partition, storage volume, modeling ability, and application in geological exploration engineering (GEE). A 3DgeoMV system prototype has been developed based on the proposed QTPV model. A case study has been conducted by using the real borehole sample data from a GEE area in Chinas Inner Mongolia China. The application shows that the QTPV has the ability to model real 3D in GEE.

Using Social Media to Detect Outdoor Air Pollution and Monitor Air Quality Index (AQI): A Geo-Targeted Spatiotemporal Analysis Framework with Sina Weibo (Chinese Twitter)

Outdoor air pollution is a serious problem in many developing countries today. This study focuses on monitoring the dynamic changes of air quality effectively in large cities by analyzing the spatiotemporal trends in geo-targeted social media messages with comprehensive big data filtering procedures. We introduce a new social media analytic framework to (1) investigate the relationship between air pollution topics posted in Sina Weibo (Chinese Twitter) and the daily Air Quality Index (AQI) published by China’s Ministry of Environmental Protection; and (2) monitor the dynamics of air quality index by using social media messages. Correlation analysis was used to compare the connections between discussion trends in social media messages and the temporal changes in the AQI during 2012. We categorized relevant messages into three types, retweets, mobile app messages, and original individual messages finding that original individual messages had the highest correlation to the Air Quality Index. Based on this correlation analysis, individual messages were used to monitor the AQI in 2013. Our study indicates that the filtered social media messages are strongly correlated to the AQI and can be used to monitor the air quality dynamics to some extent.

Evaluating Trade Areas Using Social Media Data with a Calibrated Huff Model

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.

A Hybrid Classification Matching Method for Geospatial Services

With the development of the Internet and GIS, large volumes of spatial data and powerful computing resources are increasingly published in the form of Web services. Given the variety and number of geospatial services advertised online, finding appropriate geospatial services has become a tremendous challenge for potential users. Geospatial service classification provides a basis for developing matching criteria to improve the efficiency of service discovery. At present, most classification-based matching methods require users to provide classification descriptions using a specified taxonomy. These requirements seriously limit the application of classification-based matching. To solve these kinds of problems, this article presents a hybrid geospatial service classification-matching method. Based on the differences in classification descriptions, three strategies are proposed: (1) the existing classification matching method is used for requests with classifications described using homogeneous taxonomies; (2) a formal-concept-analysis-based service classification matching method is proposed for service requests with classifications described using heterogeneous taxonomies; and (3) an interface-similarity-based service classification decision method is proposed for requests without classification descriptions. The feasibility of the hybrid geospatial service classification matching method is verified by two sets of experiments. The results reveal that this method can effectively broaden the application of classification-based matching in geospatial service discovery.

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas

Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction.

Geospatial semantic interoperability based on ontology

In GIS field, great varieties of information from different domains are involved in order to solve actual problems. But usually spatial information is stored in diverse spatial databases, manipulated by different GIS platforms. Semantic heterogeneity is caused due to the distinctions of conception explanations among various GIS implements. It will result in the information obtaining and understanding gaps for spatial data sharing and usage. An ontology-based model for spatial information semantic interoperability is put forward after the comprehensive review of progress in ontology theory, methodology and application research in GIS domain.

Inferring urban air quality based on social media

Abstract Outdoor air pollution is a serious environmental problem in many developing countries; obtaining timely and accurate information about urban air quality is a first step toward air pollution control. Many developing countries however, do not have any monitoring stations and therefore the means to measure air quality. We address this problem by using social media to collect urban air quality information and propose a method for inferring urban air quality in Chinese cities based on Chinas largest social media platform, Sina Weibo combined with other meteorological data. Our method includes a data crawler to locate and acquire air-quality associated historical Weibo data, a procedure for extracting indicators from these Weibo and factors from meteorological data, a model to infer air quality index (AQI) of a city based on the extracted Weibo indicators supported by meteorological factors. We implemented the proposed method in case studies at Beijing, Shanghai, and Wuhan, China. The results show that based the Weibo indicators and meteorological factors we extracted, this method can infer the air quality conditions of a city within narrow margins of error. The method presented in this article can aid air quality assessment in cities with few or even no air quality monitoring stations.

Multi-level Semantic matching of Geospatial Web Services

The appearance of semantic Web makes the automatic discovery and invocation of geospatial Web services become possible. The key technology of automatic discovery is service matching. This paper designs a common semantic service description model, which contains four aspects as the classification of service, input/output, precondition/effect and the quality of service (QoS), to describe the geospatial semantic service by geo-ontology. Then we propose a multi-level semantic web service matching approach which matches the geospatial services on four levels. Finally, a prototype system based on the above models and methods is designed and implemented, and it demonstrates that our solution yields high-quality results under realistic situation.

A construction schema for provincial spatial database of China

In order to provide a provincial spatial database, this paper presents a scheme for spatial database construction to meet the needs of China. The objective and overall technical route of spatial database construction are described. The logical and physical database models are designed. Key issues are addressed, such as integration of multi-scale heterogeneous spatial databases, spatial data version management based on metadata and integrative management of map cartography and spatial database.

SEMANTIC DESCRIPTION FOR THE TAXONOMY OF THE GEOSPATIAL SERVICES

With the advances in the World Wide Web and Geographic Information System, geospatial services have progressively developed to provide geospatial data and processing functions online. In order to efficiently discover and manage the large amount of geospatial services, these services are registered with semantic descriptions and categorized into classes according to certain taxonomies. Most taxonomies for geospatial services are only provided in the human readable format. The lack of semantic description for taxonomies limits the semantic-based discovery of geospatial services. The objectives of this paper are proposing an approach to semantically describe the taxonomy of geospatial services and using the semantic descriptions for taxonomy to improve the discovery of geospatial services. A semantic description framework is introduced for geospatial service taxonomy to describe not only the hierarchical structure of classes but also the definitions for all classes. The semantic description of taxonomy base on this framework is further used to simplify the semantic description and registration of geospatial services and enhance the semantic-based service matching method.