Chih-Hong Sun

An ontology-enabled framework for a geospatial problem-solving environment

Abstract This paper proposes an ontology-enabled framework for a geospatial problem-solving environment (geospatial PSE) that allows collaboration among Web-service providers, domain experts, and solution seekers to semantically discover and use geographic information services (GI services) to solve a target class of geospatial problems. The framework contains (1) ontologies that provide knowledge bases for domain experts to formalize geospatial semantics and conceptually model workflows of geospatial problems for semantic references and inferences, (2) an ontology-enabled catalog (ONTOCAT) that provides an interface for Web-service providers to annotate GI services using formal semantics for semantic discoveries, (3) a Web portal that supplies an interface for solution seekers to submit geospatial problems and evaluate the discovered GI services, and (4) an ontology engine that parses the problems and performs semantic inferences to discover the required GI services based on annotated semantics and conceptualized processes. To validate the applicability of the framework, a prototype was implemented using an earthquake as an example. This paper concludes by not only highlighting the framework, which may lay a foundation for fully integrating ontologies and GI services to solve geospatial problems, but also identifying future directions for the geospatial PSE.

Monitoring Street-Level Spatial-Temporal Variations of Carbon Monoxide in Urban Settings Using a Wireless Sensor Network (WSN) Framework

Air pollution has become a severe environmental problem due to urbanization and heavy traffic. Monitoring street-level air quality is an important issue, but most official monitoring stations are installed to monitor large-scale air quality conditions, and their limited spatial resolution cannot reflect the detailed variations in air quality that may be induced by traffic jams. By deploying wireless sensors on crossroads and main roads, this study established a pilot framework for a wireless sensor network (WSN)-based real-time monitoring system to understand street-level spatial-temporal changes of carbon monoxide (CO) in urban settings. The system consists of two major components. The first component is the deployment of wireless sensors. We deployed 44 sensor nodes, 40 transmitter nodes and four gateway nodes in this study. Each sensor node includes a signal processing module, a CO sensor and a wireless communication module. In order to capture realistic human exposure to traffic pollutants, all sensors were deployed at a height of 1.5 m on lampposts and traffic signs. The study area covers a total length of 1.5 km of Keelung Road in Taipei City. The other component is a map-based monitoring platform for sensor data visualization and manipulation in time and space. Using intensive real-time street-level monitoring framework, we compared the spatial-temporal patterns of air pollution in different time periods. Our results capture four CO concentration peaks throughout the day at the location, which was located along an arterial and nearby traffic sign. The hourly average could reach 5.3 ppm from 5:00 pm to 7:00 pm due to the traffic congestion. The proposed WSN-based framework captures detailed ground information and potential risk of human exposure to traffic-related air pollution. It also provides street-level insights into real-time monitoring for further early warning of air pollution and urban environmental management.

Climate protection and newly industrialized countries: dilemmas and opportunities in Taiwan

Abstract Global climate policy currently is making efforts to mitigate greenhouse gas emissions of industrialized countries through implementation of the Kyoto Protocol. Yet, the development of greenhouse gas emissions in newly industrialized and developing countries is deeply influencing the perspectives of stabilizing the global climate system. This study takes a closer look at the challenges facing Taiwan as an illustrative example to analyze the trends of greenhouse gas emissions, to assess current strategies and their controversies, and to explore strategies for mobilizing national climate policies. Analyzing the aspects of emission caps, carbon taxes, and clean development mechanisms may shed light on the necessity of involving newly industrialized countries and joint reduction into global climate protection schemes. Hopefully, this analysis may provide inspiring insights about the international climate regime and to other newly industrialized and developing countries which can then adopt effective policies for stabilizing the global climate system.

Development of a GIService based on spatial data mining for location choice of convenience stores in Taipei City

Motivated by the increasing accessibility of technology, more and more spatial data are being made digitally available. How to extract the valuable knowledge from these large (spatial) databases is becoming increasingly important to businesses, as well. It is essential to be able to analyze and utilize these large datasets, convert them into useful knowledge, and transmit them through GIS-enabled instruments and the Internet, conveying the key information to business decision-makers effectively and benefiting business entities. In this research, we combine the techniques of GIS, spatial decision support system (SDSS), spatial data mining (SDM), and ArcGIS Server to achieve the following goals: (1) integrate databases from spatial and non-spatial datasets about the locations of businesses in Taipei, Taiwan; (2) use the association rules, one of the SDM methods, to extract the knowledge from the integrated databases; and (3) develop a Web-based SDSS GIService as a location-selection tool for business by the product of ArcGIS Server.

MAKOCI: A WEB PORTAL FOR INTEGRATING AND SHARING GEOGRAPHIC INFORMATION SERVICES

The lack of integration and communication of various geographic information services (GI services) has resulted in many duplication collection of earth observation data, and challenges of semantic interoperability. This paper proposes an ontology-based multi-agents platform, called MAKOCI (multi-agent knowledge oriented cyberinfrastructure), which acts as GI service one stop to manage, publish, share, and discover GI services semantically. By ontologies, formal meanings of concepts are defined to annotate and discover GI services on a conceptual level for semantic interoperability. With the assistance of multi-agents, the processes in MAKOCI can be divided into various modules and be communicated based on the same semantics in ontologies. A prototype was implemented to test MAKOCI. Finally, we conclude the advantages and disadvantages of MAKOCI and point out several future works.

A Location-Based Client-Server Framework for Assessing Personal Exposure to the Transmission Risks of Contagious Diseases

Human mobility is an important risk factor affecting contagious disease transmission. Therefore, understanding spatial behaviors and interactions among individuals is a fundamental issue. Past studies using high-resolution human contacts data with sequential location data from global positioning systems (GPS) receivers have captured spatial-temporal heterogeneity and daily contact patterns among individuals. However, how to measure effectively personalized exposure to the risk of contagious disease transmission is still under development. The purpose of the study is to establish a location-based client-server framework for assessing personalized exposure to the transmission risk of contagious disease. The location-based framework consists of two major components: one is a client-side smartphone-based risk assessment module. We developed an Android application for collecting course-attending records and real-time location data for displaying personalized exposure scores. The other component is a server-side epidemic simulation model. The simulation model calculated the personalized exposure score based on GPS logs and individual mobility data from the client-side Android application. We used National Taiwan University (NTU) main campus as a pilot study to demonstrate the feasibility of the framework. The records of students attending courses and GPS logs were used for capturing mobility of students around the campus. We then generated a space-time mobility network based on individual mobility trajectories. Based on the epidemic simulation model with individual mobility network, each student who uses his/her smartphone as a personalized platform, can understand the epidemic progression and make better decisions, such as wearing a face mask or reducing the contact frequency, based on personalized exposure scores from the server-side computation. The proposed location-based framework presents complex interactions among personal risk reception, behavioral changes, and epidemic progression. More scenarios can be implemented in future studies for quantifying the effects of risk reception or behavioral changes with epidemic progression.