Duangduen Roongpiboonsopit

Comparative evaluation and analysis of online geocoding services

Geocoding is an uncertain process that associates an address or a place name with geographic coordinates. Traditionally, geocoding is performed locally on a stand-alone computer with the geocoding tools usually bundled in GIS software packages. The use of such tools requires skillful operators who know about the issues of geocoding, that is, reference databases and complicated geocoding interpolation techniques. These days, with the advancement in the Internet and Web services technologies, online geocoding provides its functionality to the Internet users with ease; thus, they are often unaware of such issues. With an increasing number of online geocoding services, which differ in their reference databases, the geocoding algorithms, and the strategy for dealing with inputs and outputs, it is crucial for the service requestors to realize the quality of the geocoded results of each service before choosing one for their applications. This is primarily because any errors associated with the geocoded addresses will be propagated to subsequent decisions, activities, modeling, and analysis. This article examines the quality of five online geocoding services: Geocoder.us, Google, MapPoint, MapQuest, and Yahoo!. The quality of each geocoding service is evaluated with three metrics: match rate, positional accuracy, and similarity. A set of addresses from the US Environmental Protection Agency (EPA) database were used as a baseline. The results were statistically analyzed with respect to different location characteristics. The outcome of this study reveals the differences among the online geocoding services on the quality of their geocoding results and it can be used as a general guideline for selecting a suitable service that matches an applications needs.

Design Considerations for a Personalized Wheelchair Navigation System

Individuals with mobility impairments such as wheelchair users are often at a disadvantage when traveling to a new place, as their mobility can be easily affected by environmental barriers, and as such, even short trips can be difficult and perhaps impossible. We envision a personalized wheelchair navigation system based on a PDA equipped with wireless Internet access and GPS that can provide adaptive navigation support to wheelchair users in any geographic environment. Requirements, architectures and components of such a system are described in this paper.

Quality Assessment of Online Street and Rooftop Geocoding Services

The widespread use of Internet-based mapping and geospatial analysis has caused an increase in the demand for online geocoding services. Although such services provide convenience, low (or free) cost and immediate solutions, their characteristics, sometimes, overshadow the expectation of producing quality of geocoded results. In recent years, several geocoding techniques have emerged, including rooftop geocoding, but they have yet to receive much attention in the literature. This paper examines and compares the quality of online rooftop and street geocoding services based on match rates and positional accuracy. Six geocoding services by five providers (i.e., Microsoft Virtual Earth, Google, Geocoder.us, MapQuest, and Yahoo!) were evaluated using addresses in Allegheny County, Pennsylvania. Results of the comparison indicate that rooftop geocoding produces slightly lower match rates but significantly higher positional accuracy than street geocoding. The hybrid service, which combines the two techniques, produces match rates as high as other street geocoding services but improves in positional accuracy close to the level of rooftop geocoding. Geocoding services employing reference databases with similar quality trend to produce compatible match rates and positional accuracy. This paper examines the sensitivity of different address types on geocoding quality. The results reveal that both rooftop and street geocoding produce high match rates and high accuracy for residential addresses. However, positional accuracies of agricultural and industrial address types are not very reliable due to the small sample sizes. With these, it is recommended to use online rooftop geocoding services if high positional accuracy is the priority, use street geocoding if high match rate is the priority, and use the hybrid approach if both high match rates and high positional accuracy are required.

A Multi-Constellations Satellite Selection Algorithm for Integrated Global Navigation Satellite Systems

The U.S. Global Positioning System (GPS) is currently the only fully operational global navigation satellite system (GNSS) that has been widely utilized in navigation and data collection, among other applications. In the near future, there will be other GNSSs such as the Russian GLONASS, the European Galileo, and the Chinese Compass that provide compatible services with GPS and will be available to all users. Besides operating in the single GNSS mode, future GNSS receivers will have the capability to operate in an integrated GNSS mode where they will be able to couple satellites from all available constellations for positioning solutions. However, tracking all visible satellites simultaneously increases the computational load of the receivers. This is particularly true with low-cost and compact receivers, which have limited computational resources and are commonly used in real-time intelligent transportation system (ITS) applications such as automatic vehicle location, route guidance, fleet management, vehicle dispatch, traveler information, and car navigation. These real-time applications also impose time constraints on positional updates (maximum required time interval between two epochs) while requiring high accuracy positioning solutions. To mitigate such burdens while maintaining benefits of the combined system, this article presents a new heuristic algorithm called a multi-constellations satellite selection algorithm (MCSSA). The algorithm allows an integrated GNSS receiver to track only a smaller set of visible satellites that can provide acceptable positioning solutions in the required time intervals. The MCSSA considers the geometric arrangements of visible satellites and selects the ones that spread evenly over the observed sky view. The performance of the MCSSA was validated by simulating the combined GPS and Galileo constellations. The accuracy and computation time of the MCSSA were compared with selected existing satellite selection algorithms. The results indicate that the MCSSA provides the set of satellites with near-optimal geometries giving high positional accuracy in a reasonably small computation time and thus is suitable for real-time applications.

Exploring Real-Time Geoprocessing in Cloud Computing: Navigation Services Case Study

Today, many real-time geospatial applications (e.g. navigation and location-based services) involve data- and/or compute-intensive geoprocessing tasks where performance is of great importance. Cloud computing, a promising platform with a large pool of storage and computing resources, could be a practical solution for hosting vast amounts of data and for real-time processing. In this article, we explored the feasibility of using Google App Engine (GAE), the cloud computing technology by Google, for a module in navigation services, called Integrated GNSS (iGNSS) QoS prediction. The objective of this module is to predict quality of iGNSS positioning solutions for prospective routes in advance. iGNSS QoS prediction involves the real-time computation of large Triangulated Irregular Networks (TINs) generated from LiDAR data. We experimented with the Google App Engine (GAE) and stored a large TIN for two geoprocessing operations (proximity and bounding box) required for iGNSS QoS prediction. The experimental results revealed that while cloud computing can potentially be used for development and deployment of data- and/or compute-intensive geospatial applications, current cloud platforms require improvements and special tools for handling real-time geoprocessing, such as iGNSS QoS prediction, efficiently. The article also provides a set of general guidelines for future development of real-time geoprocessing in clouds.

Geocoding Recommender: An Algorithm to Recommend Optimal Online Geocoding Services for Applications

Today, many services that can geocode addresses are available to domain scientists and researchers, software developers, and end-users. For a number of reasons, including quality of reference database and interpolation technique, a given address geocoded by different services does not often result in the same location. Considering that there are many widely available and accessible geocoding services and that each geocoding service may utilize a different reference database and interpolation technique, selecting a suitable geocoding service that meets the requirements of any application or user is a challenging task. This is especially true for online geocoding services which are often used as black boxes and do not provide knowledge about the reference databases and the interpolation techniques they employ. In this article, we present a geocoding recommender algorithm that can recommend optimal online geocoding services by realizing the characteristics (positional accuracy and match rate) of the services and preferences of the user and/or their application. The algorithm is simulated and analyzed using six popular online geocoding services for different address types (agricultural, commercial, industrial, residential) and preferences (match rate, positional accuracy).

Uncertainty in personal navigation services

The demand for navigation assistance and advances in several technologies has been paving the way for Personal Navigation Services (PerNavs). As users increasingly rely on PerNavs for navigation assistance, they gain a better understanding of what PerNavs can offer and how they operate. This trend, consequently, will increase the demand for PerNav that can provide high quality solutions. While there have been studies addressing uncertainties associated with selected individual navigation modules, there is a void in the literature addressing the overall uncertainty in PerNavs. In this paper, we discuss uncertainty in PerNavs by analyzing uncertainties associated with each of its modules and how they propagate and impact other modules. A Bayesian network is presented as one possible model to manage (by developers) and communicate (to users) uncertainty in PerNavs.

Integrated Global Navigation Satellite System (iGNSS) QoS Prediction

This article describes how navigation applications, among other location-based applications, that rely primarily on Global Navigation Satellite System (GNSS) are subject to positioning uncertainties. An integrated GNSS (iGNSS) QoS prediction methodology that can provide navigation application methodology with quality of GNSS positions on prospective roads in a selected route ahead of time is presented in this article. As a part of the methodology, this article discusses the technique for simulating signal predicting levels of satellite visibility, positional availability, and positional accuracy. Experiments were conducted and the predicted results by iGNSS QoS were evaluated against reference data (GPS coordinates) at various environment settings. Evaluation results indicated that iGNSS QoS can predict positioning quality with a reasonably high level of confidence In open sky locations and with some uncertainties in obstructed locations.

Grid-Based Geoprocessing for Integrated Global Navigation Satellite System Simulation

It is anticipated that the integration of global navigation satellite systems (GNSS) will improve the overall positioning performance benefiting many existing applications and paving the way for the emergence of new location-based or location-aware applications. However, such integrations of GNSSs (iGNSS) will also require the development of a new generation of receivers and methodologies that can utilize the new information efficiently and effectively. This is especially true for real-time applications, such as navigation, for which time performance is of the essence in providing highly accurate positioning solutions. In this paper, we address time performance of iGNSS by focusing only on the visibility parameter, among all parameters, because the visibility solution is a prerequisite for the other parameters, and it is very expensive computationally. We propose an efficient web service for visibility computation, called iGNSS-v and discuss our experimentation with a grid computing platform. The results of the experiment indicate that grids can potentially benefit iGNSS by improving the time performances of its various components, but further optimization is needed in order to address the real-time requirement of iGNSS-QoS prediction.

Are Clouds Ready for Geoprocessing

Cloud computing has gained popularity in recent years as a new means to quickly process and share information by using a pool of computing resources. Of existing and new applications that could benefit from cloud computing, geospatial applications, whose operations are based on geospatial data and computation, are of particular interest due to prevalence of large geospatial data layers and to complex geospatial computations. Problems in many compute- and/or data-intensive geospatial applications are even more pronounced when real-time response is needed. While researchers have been resorting to high-performance computing (HPC) platforms for efficient processing such as grids and supercomputers, cloud computing with new and advanced features is potential for geospatial problem solving and application implementation and deployment. In this chapter, we discuss the result of our experiments using Google App Engine (GAE), as a representative of existing cloud computing platforms, for real-time geospatial applications.