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.     

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).     

Assessing the Completeness of Bicycle Trail and Lane Features in OpenStreetMap for the United States

This article assesses the completeness of bicycle trail and on‐street lane features in OpenStreetMap (OSM). Comparing OSM cycling features with reference data from local planning agencies for selected US Urbanized Areas shows that OSM bicycle trails tend to be more completely mapped than bicycle lanes. Manual evaluation of mapped cycling features in OSM and Google Maps for selected test areas within the Central Business Districts of Portland (OR) and Miami (FL) through comparison with governmental datasets, satellite imagery, and Google Street View, shows that the Bicycle layer in Google Maps can help to identify some missing or erroneously mapped OSM cycling links. However, Google Maps was also found to have some gaps in its data layers, suggesting that consultation of current trail and lane data from local planning authorities, if available, should be considered as an additional data source for bicycle related planning projects.     

A probabilistic framework for improving reverse geocoding output

Reverse geocoding, which transforms machine‐readable GPS coordinates into human‐readable location information, is widely used in a variety of location‐based services and analysis. The output quality of reverse geocoding is critical because it can greatly impact these services provided to end‐users. We argue that the output of reverse geocoding should be spatially close to and topologically correct with respect to the input coordinates, contain multiple suggestions ranked by a uniform standard, and incorporate GPS uncertainties. However, existing reverse geocoding systems often fail to fulfill these aims. To further improve the reverse geocoding process, we propose a probabilistic framework that includes: (1) a new workflow that can adapt all existing address models and unitizes distance and topology relations among retrieved reference data for candidate selections; (2) an advanced scoring mechanism that quantifies characteristics of the entire workflow and orders candidates according to their likelihood of being the best candidate; and (3) a novel algorithm that derives statistical surfaces for input GPS uncertainties and propagates such uncertainties into final output lists. The efficiency of the proposed approaches is demonstrated through comparisons to the four commercial reverse geocoding systems and through human judgments. We envision that more advanced reverse geocoding output ranking algorithms specific to different application scenarios can be built upon this work.     

基于Lucene引擎构建在线地址匹配服务

随着我国城市化和信息化的发展,地址编码已经成为建设数字城市的基础工作。地址匹配是地址编码的关键环节,但面临着中文地址分词困难的问题。本文基于Lucene检索引擎,结合三叉树分词词典机制和基于规则的地址分词技术,设计了具有地址分词和地址匹配功能的地址匹配引擎,并构建了Rest风格的在线地址匹配服务,取得了良好的应用效果。     

城市地理编码数据库的研究与实践

地理编码数据库是地理编码技术的重要组成部分,它直接影响到地址匹配的效果。本文在青岛市地址体系的分析研究的基础上,详细阐述了青岛市地理编码数据库的建设流程,包括地址数据获取、地址数据标准化、地址数据入库和地址数据库组织的建设,并对地址数据的更新提出了建议。     

The Potential of Maps APIs for Internet GIS Applications

Since the launching of Maps Application Programming Interfaces (APIs) in 2005, many web developers, including geographers and non‐geographers, applauded the freely adaptable tools and used them to spawn numerous Internet applications. The success of the Maps APIs is largely attributable to its no‐cost policy, the availability of global data coverage, dynamic navigation, query capability, and ease of implementation. Despite its versatility in dynamic exploration of geographic data online, the existing Maps APIs lack Geographic Information System (GIS) functionalities compared to other Internet Mapping Services. The goal of this research was to review the potential of the Maps APIs for Internet GIS applications. This research employed the Google Maps API and developed a web prototype that disseminates spatial information of urban sprawl in Mundy Township, Michigan. The results revealed that both vector and raster data could be effectively represented by using the Maps API. Moreover, the Geographic Markup Language (GML) approach illustrated great potential for developing Internet GIS solutions around open specifications. This research suggested several potential solutions to expand the spectrum of GIS operations of the Maps APIs by incorporating the XML‐related technology and extending the JavaScript library.     

基于Google Map API的校园电子地图开发

Google Map API为用户提供向Google地图添加各种个性化内容的功能,用户在此基础上进行二次开发,可实现各类网络地图服务。利用Google Map API开发校园电子地图,实现电子地图的各项基本功能,并探讨功能实现中的关键部分设计。     

Geocoding Large Population‐level Administrative Datasets at Highly Resolved Spatial Scales

Using geographic information systems to link administrative databases with demographic, social, and environmental data allows researchers to use spatial approaches to explore relationships between exposures and health. Traditionally, spatial analysis in public health has focused on the county, ZIP code, or tract level because of limitations to geocoding at highly resolved scales. Using 2005 birth and death data from North Carolina, we examine our ability to geocode population‐level datasets at three spatial resolutions – zip code, street, and parcel. We achieve high geocoding rates at all three resolutions, with statewide street geocoding rates of 88.0% for births and 93.2% for deaths. We observe differences in geocoding rates across demographics and health outcomes, with lower geocoding rates in disadvantaged populations and the most dramatic differences occurring across the urban‐rural spectrum. Our results suggest that highly resolved spatial data architectures for population‐level datasets are viable through geocoding individual street addresses. We recommend routinely geocoding administrative datasets to the highest spatial resolution feasible, allowing public health researchers to choose the spatial resolution used in analysis based on an understanding of the spatial dimensions of the health outcomes and exposures being investigated. Such research, however, must acknowledge how disparate geocoding success across subpopulations may affect findings.     

Positional Accuracy of Spatial Data: Non-Normal Distributions and a Critique of the National Standard for Spatial Data Accuracy

Spatial data quality is a paramount concern in all GIS applications. Existing spatial data accuracy standards, including the National Standard for Spatial Data Accuracy (NSSDA) used in the United States, commonly assume the positional error of spatial data is normally distributed. This research has characterized the distribution of the positional error in four types of spatial data: GPS locations, street geocoding, TIGER roads, and LIDAR elevation data. The positional error in GPS locations can be approximated with a Rayleigh distribution, the positional error in street geocoding and TIGER roads can be approximated with a log‐normal distribution, and the positional error in LIDAR elevation data can be approximated with a normal distribution of the original vertical error values after removal of a small number of outliers. For all four data types considered, however, these solutions are only approximations, and some evidence of non‐stationary behavior resulting in lack of normality was observed in all four datasets. Monte‐Carlo simulation of the robustness of accuracy statistics revealed that the conventional 100% Root Mean Square Error (RMSE) statistic is not reliable for non‐normal distributions. Some degree of data trimming is recommended through the use of 90% and 95% RMSE statistics. Percentiles, however, are not very robust as single positional accuracy statistics. The non‐normal distribution of positional errors in spatial data has implications for spatial data accuracy standards and error propagation modeling. Specific recommendations are formulated for revisions of the NSSDA.     

利用Web挖掘技术改善公众网络地图查询服务

针对影响公众网络地图查询服务质量的一些因素,提出利用Web挖掘技术来加以改善,这主要体现于三个环节:从万维网中发现并提取地址信息以扩充空间数据库;通过对扩充后的数据库进行空间分析与推理来增强查询功能;根据分析用户查询日志来指导数据采编工作以及提供针对性的查询服务。在文章的最后给出了原型系统的设计框架与试验实例。     

Improving Spatial Accuracy of Roadway Networks and Geocoded Addresses

Exposure to traffic‐related pollutants is associated with both morbidity and mortality. Because vehicle‐exhaust are highly localized, within a few hundred meters of heavily traveled roadways, highly accurate spatial data are critical in studies concerned with exposure to vehicle emissions. We compared the positional accuracy of a widely used U.S. Geological Survey (USGS) roadway network containing traffic activity data versus a global positioning system (GPS)‐validated road network without traffic information; developed a geographical information system (GIS)‐based methodology for producing improved roadway data associated with traffic activities; evaluated errors from geocoding processes; and used the CALINE4 dispersion model to demonstrate potential exposure misclassifications due to inaccurate roadway data or incorrectly geocoded addresses. The GIS‐based algorithm we developed was effective in transferring vehicle activity information from the less accurate USGS roadway network to a GPS‐accurate road network, with a match rate exceeding 95%. Large discrepancies, up to hundreds of meters, were found between the two roadway networks, with the GPS‐validated network having higher spatial accuracy. In addition, identifying and correcting errors associated with geocoding resulted in improved address matching. We demonstrated that discrepancies in roadway geometry and geocoding errors, can lead to serious exposure misclassifications, up to an order of magnitude in assigned pollutant concentrations.     

A deep learning approach for rooftop geocoding

Geocoding has become a routine task for many research investigations to conduct spatial analysis. However, the output quality of geocoding systems is found to impact the conclusions of subsequent studies that employ this workflow. The published development of geocoding systems has been limited to the same set of interpolation methods and reference data sets for quite some time. We introduce a novel geocoding approach utilizing object detection on remotely sensed imagery based on a deep learning framework to generate rooftop geocoding output. This allows geocoding systems to use and output exact building locations without employing typical geocoding interpolation methods or being completely limited by the availability of reference data sets. The utility of the proposed approach is demonstrated over a sample of 22,481 addresses resulting in significant spatial error reduction and match rates comparable to typical geocoding methods. For different land‐use types, our approach performs better on low‐density residential and commercial addresses than on high‐density residential addresses. With appropriate model setup and training, the proposed approach can be extended to search different object locations and to generate new address and point‐of‐interest reference data sets.     

Every single street? Rethinking full coverage across street‐level imagery platforms

Street‐level images taken by vehicles and pedestrians have found a role in various companies’ location‐based intelligence services. Some platforms collect their images using their own cars and drivers, while others rely on crowdsourcing; however, to what extent can we expect crowdsourced approaches to reach the imagery coverage levels obtained by paid drivers? Is capturing every single street a useful or obtainable goal? We use online coverage maps to compare Google Street View, Mapillary, and OpenStreetCam in 24 major world cities and 25 differently sized cities in Brazil. We find that Google has often taken an all‐or‐nothing approach to collecting coverage in world cities, whereas crowdsourced platforms have achieved a more even distribution of coverage across space. Extremely low‐ and high‐income neighborhoods are sometimes omitted due to visible and invisible barriers. Coverage patterns are influenced by how and why each company procures imagery, along with other social, economic, and geographic factors.     

A scalable cyberinfrastructure and cloud computing platform for forest aboveground biomass estimation based on the Google Earth Engine

ABSTRACT

Earth observation (EO) data, such as high-resolution satellite imagery or LiDAR, has become one primary source for forests Aboveground Biomass (AGB) mapping and estimation. However, managing and analyzing the large amount of globally or locally available EO data remains a great challenge. The Google Earth Engine (GEE), which leverages cloud-computing services to provide powerful capabilities on the management and rapid analysis of various types of EO data, has appeared as an inestimable tool to address this challenge. In this paper, we present a scalable cyberinfrastructure for on-the-fly AGB estimation, statistics, and visualization over a large spatial extent. This cyberinfrastructure integrates state-of-the-art cloud computing applications, including GEE, Fusion Tables, and the Google Cloud Platform (GCP), to establish a scalable, highly extendable, and high-performance analysis environment. Two experiments were designed to demonstrate its superiority in performance over the traditional desktop environment and its scalability in processing complex workflows. In addition, a web portal was developed to integrate the cyberinfrastructure with some visualization tools (e.g. Google Maps, Highcharts) to provide a Graphical User Interfaces (GUI) and online visualization for both general public and geospatial researchers.     

基于Google Map API的天气实况显示系统

谷歌地图应用程序开发包提供了各种向地图添加内容的服务,用户能够在网站上创建功能全面的地图应用程序。利用Google Maps API和Ajax技术,建立了气温、湿度等天气实况信息在Google Map中快速显示的WebGIS系统。实践证明,该方法提高了系统的开发速度,缩短了开发周期,优化了人机交互。     

莱西市医疗设施空间可达性研究

针对当前医疗资源分布不均的问题,本文以莱西市为例,以行政村为最小研究单元,借助GIS空间分析技术,采用改进的两步移动搜索和潜能模型法,对莱西市医疗设施空间可达性进行研究.结果表明:莱西市整体的医疗资源分配不均,具有明显的空间集聚特征,高值区域主要集中在中部地区,包括水集街道、沽河街道、望城街道和莱西经济开发区;低值区域...     

百度地图服务在武汉市地理国情普查数据采集中的应用

提出了一种基于百度地图服务的地址解析方法,通过自动搜索和调用百度数据资源,实现了地名地址信息的快速、批量定位与上图,在武汉市第一次地理国情普查数据采集中取得了较好应用。     

A Hybrid Geocoding Methodology for Spatio‐Temporal Data

As tools for collecting data continue to evolve and improve, the information available for research is expanding rapidly. Increasingly, this information is of a spatio‐temporal nature, which enables tracking of phenomena through both space and time. Despite the increasing availability of spatio‐temporal data, however, the methods for processing and analyzing these data are lacking. Existing geocoding techniques are no exception. Geocoding enables the geographic location of people and events to be known and tracked. However, geocoded information is highly generalized and subject to various interpolation errors. In addition, geocoding for spatio‐temporal data is especially challenging because of the inherent dynamism of associated data. This article presents a methodology for geocoding spatio‐temporal data in ArcGIS that utilizes several additional supporting procedures to enhance spatial accuracy, including the use of supplementary land use information, aerial photographs and local knowledge. This hybrid methodology allows for the tracking of phenomenon through space and over time. It is also able to account for reporting inconsistencies, which is a common feature of spatio‐temporal data. The utility of this methodology is demonstrated using an application to spatio‐temporal address records for a highly mobile group of convicted felons in Hamilton County, Ohio.     

地址树模型的中文地址提取方法

地址是一种对个体地域空间位置信息的编码方法。在我国,由于城市快速发展,地址规划相对落后,非标准地址大量存在。本文在分析标准地址模型空间约束关系类型的基础上,提出了一种基于地址树模型的中文地址提取方法。该模型以拓扑关系作为空间约束关系是否一致的判断标准,可以从非标准地址中提取标准地址,并剔除非标准和错误地址元素。试验证明,该方法有较高的地址匹配率。