Geocoding

The ability to obtain accurate predictions of bus arrival time on a real-time basis is a vital element to both bus operations control and passenger information systems. Several studies had been devoted to this arrival time prediction problem; however, few resulted in completely satisfactory algorithms. This paper presents a new system that can be used to predict the expected bus arrival times at individual bus stops along a service route. The proposed prediction algorithm combines real-time location data from Global Positioning System receivers with average travel speeds of individual route segments, taking into account historical travel speed as well as temporal and spatial variations of traffic conditions. A geographic information system–based map-matching algorithm is used to project each received location onto the underlying transit network. The system is implemented as a finite state machine to ensure its regularity, stability, and robustness under a wide range of operating con...

In recent years, the increase in the number of hurricanes and other costal hazards in the US pose a tremendous threat to the residents of coastal states. According to the National Hurricane Center, Florida is the most vulnerable coastal state to hurricanes. Mitigation policies have been formulated to reduce mortality and provide emergency services by evacuating people from the hazard zone. Many of these evacuees, particularly the elderly or lower income populations, rely on evacuation shelters for temporary housing. Because of the cost and limited use, evacuation shelters are almost exclusively dual use shelters where the primary purpose of the facility is for some other public function (e.g. school, hospital, etc.). In 2000, the estimated shortage of public shelter spaces in Florida was about 1.5 million. The purpose of this study was to rank the existing and candidate shelters (schools, colleges, churches and community centers) available in the state based on their site suitability. The research questions examined in this study include: (1) How many candidate shelters are located in physically suitable areas (e.g. not in a flood prone area, not near hazardous facilities, etc.)?; (2) How many existing shelters are located in physically unsuitable areas, but in socially suitable areas (situated in areas with demand)?; (3) How many alternative existing and/or candidate shelters with high/very high physical suitability are located near physically unsuitable existing shelters and thus, may be better choices for a shelter?; and (4) How many existing shelters located in physically unsuitable areas are not near alternative existing and/or candidate shelters? A Geographic Information System-based suitability model integrating Weighted Linear Combination (WLC) with a Pass/Fail screening technique was implemented for the 17 counties of Southern Florida. It was found that 48% of the existing shelters are located in physically unsuitable areas. Out of all the candidate shelters, 57% are located in physically unsuitable areas. For 15 of the existing shelters in unsuitable locations, no alternative candidate or existing shelter with medium to high physical suitability exists within 10 miles (16.1 km).

In spatial epidemiologic and public health research it is common to use spatially aggregated units such as centroids of postal/zip codes, census tracts, dissemination areas, blocks or block groups as proxies for sample unit locations. Few studies, however, address the potential problems associated with using these units as address proxies. The purpose of this study is to quantify the magnitude of distance errors and accessibility misclassification that result from using several commonly-used address proxies in public health research. The impact of these positional discrepancies for spatial  epidemiology is illustrated by examining misclassification of accessibility to several health-related facilities, including hospitals, public recreation spaces, schools, grocery stores, and junk food retailers throughout the City of London and Middlesex County, Ontario, Canada. Positional errors are quantified by multiple neighborhood types, revealing that address proxies are most problematic when used to represent residential locations in small towns and rural areas compared to suburban and urban areas. Findings indicate that the shorter the threshold distance used to measure accessibility between subject population and health-related facility, the greater the proportion of misclassified addresses. Using address proxies based on large aggregated units such as centroids of census tracts or dissemination areas can result in very large positional discrepancies (median errors up to 343 and 2088 m in urban and rural areas, respectively), and therefore should be avoided in spatial epidemiologic research. Even smaller, commonly-used, proxies for residential address such as postal code centroids can have large positional discrepancies (median errors up to 109 and 1363 m in urban and rural areas, respectively), and are prone to misrepresenting accessibility in small towns and rural Canada; therefore, postal codes should only be used with caution in spatial epidemiologic research.

This study examined the sources of error involved in geocoding, by systematically evaluating the strengths and weaknesses of three widely used tools for geocoding. We tested them against a random sample of addresses from a state administrative address master file and found considerable variation in identification of census block geocodes of addresses. This high variation was mainly attributable to differences in preprocessing of addresses before geocoding and the reference street data used for geocoding. Preprocessing includes not only parsing and standardizing, but also correcting addresses against the US Postal Service Zip+4 Database, the master mailing address database maintained and updated regularly by USPS.

Big, fine-grained enterprise registration data that includes time and location information enables us to quantitatively analyze, visualize, and understand the patterns of industries at multiple scales across time and space. However, data quality issues like incompleteness and ambiguity, hinder such analysis and application. These issues become more challenging when the volume of data is immense and constantly growing. High Performance Computing (HPC) frameworks can tackle big data computational issues, but few studies have systematically investigated imputation methods for enterprise registration data in this type of computing environment. In this paper, we propose a big data imputation workflow based on Apache Spark as well as a bare-metal computing cluster, to impute enterprise registration data. We integrated external data sources, employed Natural Language Processing (NLP), and compared several machine-learning methods to address incompleteness and ambiguity problems found in enterprise registration data. Experimental results illustrate the feasibility, efficiency, and scalability of the proposed HPC-based imputation framework, which also provides a reference for other big georeferenced text data processing. Using these imputation results, we visualize and briefly discuss the spatiotemporal distribution of industries in China, demonstrating the potential applications of such data when quality issues are resolved.

A risk-based model is presented for identifying highway-rail grade crossing blackspots. This model consists of two prediction components: collision frequency and collision consequence. A graphic approach is adopted to identify crossings with unacceptable risks (high expected frequencies or consequences or both). These crossings are referred to as blackspots. The model was applied to Canadian inventory and collision occurrence data for the period 1997-2001. Poisson and negative binomial (NB) frequency prediction expressions were developed for crossings with three types of warning devices (signs, flashing lights, and gates). The NB model was found to provide a better fit to the collision frequency data. A weighted consequence score was introduced to represent combined collision severity. The weights used in this combined consequence score were obtained from insurance claims. An NB expression was developed for the collision consequence model. The spatial distribution of blackspots is d...

The purpose of this paper is to expand empirical research on bus transit operation costs and test the hypothesis that physical and geographical characteristics are plausible explanatory cost factors. A translog cost function has been estimated, using a panel dataset of 1053 observations over 1996–2002 for a cross-section of 264 transit bus agencies in the US, combined with geographical and physical data processed with GIS technology. The results confirm the importance of geographical factors as determinants of bus transit costs. The trade-offs between geographical factors in shaping the frontier between economies and diseconomies of density are assessed. The implications of the results for public policy regarding competition are discussed.

The increasing use of geographic information systems (GIS) in epidemiological population studies requires careful attention to the methods employed in accomplishing geocoding and creating a GIS. Studies have provided limited details, hampering the ability to assess validity of spatial data. The purpose of this paper is to describe the multiphase geocoding methods used to retrospectively create a GIS in the Jackson Heart Study (JHS). We used baseline data from 5,302 participants enrolled in the JHS between 2000 and 2004 in a multiphase process to accomplish geocoding 2 years after participant enrollment. After initial deletion of ungeocodable addresses (n = 52), 96% were geocoded using ArcGIS. An interactive method using data abstraction from participant records, use of additional maps and street reference files, and verification of existence of address, yielded successful geocoding of all but 13 addresses. Overall, nearly 99% (n = 5,237) of the JHS cohort was geocoded retrospectively using the multiple strategies for improving and locating geocodable addresses. Geocoding validation procedures revealed highly accurate and reliable geographic data. Using the methods and protocol developed provided a reliable spatial database that can be used for further investigation of spatial epidemiology. Baseline results were used to describe participants by select geographic indicators, including residence in urban or rural areas, as well as to validate the effectiveness of the study’s sampling plan. Further, our results indicate that retrospectively developing a reliable GIS for a large, epidemiological study is feasible. This paper describes some of the challenges in retrospectively creating a GIS and provides practical tips that enhanced the success.

Geocoding is a method used to convert address information into geographical coordinates. It plays a vital role in displaying the relationship between geographic features and semantic information expressed in texts. The objective of this study is to reveal the quality of online geocoding from postal addresses in Turkey provided by Google Maps and Bing Maps services. The quality of geocoding services in urban areas is evaluated using two particular metrics; positional accuracy and address similarity. Positional accuracy measures the distances between point features obtained through the online geocoding and reference data. Address similarity indicates the relationship between two postal addresses based on a similarity index known as the Levenshtein distance. The same performance assessment was also made with the United States’ address data to make comparisons and discussions. The results show that services have different geocoding capabilities in both countries because of the differences in the addressing formats.

The purpose of text geolocation is to associate geographic information contained in a document with a set (or sets) of coordinates , either implicitly by using linguistic features and/or explicitly by using geographic metadata combined with heuristics. We introduce a geocoder (location mention disambiguator) that achieves state-of-the-art (SOTA) results on three diverse datasets by exploiting the implicit lexical clues. Moreover, we propose a new method for systematic encoding of geographic metadata to generate two distinct views of the same text. To that end, we introduce the Map Vector (MapVec), a sparse representation obtained by plotting prior geographic probabilities, derived from population figures, on a World Map. We then integrate the implicit (language) and explicit (map) features to significantly improve a range of metrics. We also introduce an open-source dataset for geoparsing of news events covering global disease outbreaks and epidemics to help future evaluation in geoparsing.

The combination of GPS services and information technology is increasing with the use of geotagging now occurring as a default action in many commodity based devices functionality for example mobile phones and cameras. The camera in suitably enabled phones takes the picture and embeds GPS co-ordinates of the location into the metadata of the resulting image file. Furthermore there are now online services such as foursquare that are targeted at the geotagging or geocaching community. In addition to this overtly designed service other social online services such Flickr, Twitter are providing a means of geotagging users. This enablement of technology has significant and profound effects on bpersonal security and also extending into corporate security.

A first evaluation of ALOS PALSAR data for forest stem volume estimation has been performed at a coniferous dominated test site in Sweden. In total, 7 Fine Beam Single polarization (FBS, look angle 34.3deg, HH-polarization) and 7 Polarimetric (PLR, look angle 21.5deg, HH-, HV-, VH-, and W- polarization) SAR images were used. In total, 56 forest stands with stem volume

Strategies for improving geocoded data often rely on interactive manual processes that can be time-consuming and impractical for large-scale projects. In this study, we evaluated different automated strategies for improving address quality and geocoding matching rates using a large dataset of addresses from death records in Rio de Janeiro, Brazil. Mortality data included 132,863 records with address information in a structured format. We performed regular expressions and dictionary-based methods for address standardization and enrichment. All records were linked by their postal code or street name to the Brazilian National Address Directory (DNE) obtained from Brazil's Postal Service. Residential addresses were geocoded using Google Maps. Records with address data validated down to the street level and location type returned as rooftop, range interpolated, or geometric center were considered a geocoding match. The overall performance was assessed by manually reviewing a sample of addresses. Out of the original 132,863 records, 85.7% (n = 113,876) were geocoded and validated, out of which 83.8% were matched as rooftop (high accuracy). Overall sensitivity and specificity were 87% (95%CI: 86-88) and 98% (95%CI: 96-99), respectively. Our results indicate that address quality and geocoding completeness can be reliably improved with an automated geocoding process. R scripts and instructions to reproduce all the analyses are

We developed and deployed, in July 2005, a wideband 8-channel synthetic aperture radar (SAR) at Summit Camp, Greenland (72.5783° N and 38.4596° W). The radar was designed to map internal layers, measure topography, and generate backscatter maps - all in a single pass. Information on ice thickness, bed topography and basal conditions is essential to the refinement of glaciological models of ice sheets, which are used to predict ice-sheet behavior (especially mass balance) and to select deep ice-core sites. This work focuses on the use of fine-resolution D imaging algorithms for combining all 8-channels to form cross-track image slices through the ice. As compared with traditional 2D depth sounding, these 3D images allow for the characterization of bed topography with very fine resolution. They also allow for the generation of strip-map SAR images with absolute geocoding without ground control points (these are unavailable at the bottom of the ice), and the ability to analyze the ice-...

Historical itineraries, often accessible as lists or tables describing places visited in sequence, are abundant resources and also important objects of study for humanities scholars. This article advances a novel method for automatically geocoding tabular itineraries, combining approximate string matching with a cost optimization algorithm based on dynamic programming. Experiments with a dataset of historical itineraries, with ground-truth geocoding annotations provided by domain experts and leveraging also the GeoN-ames gazetteer, attest to the effectiveness of the proposed method. The obtained results show that while approximate string matching can already achieve very low median errors, with many toponyms matching exactly against GeoNames entries, the combination with cost optimization can significantly improve results in terms of the average distance towards the correct disambiguations.

One of the most important features in many applications, especially in geographic information systems (GIS), is the capability tolocate addresses, i.e. to geocode the address. During the last year there has been also a significant progress to geocode addresses bycommercial Internet mapping APIs and Internet services. Geocoding became more popular and online mapping tools havepopularized the concept of using an address as an initial map navigation tool. In contrast to these commercial solutions with focuson industrialized countries, the project described in this paper is an implementation of a free, participatory, community orientedgeocoding service. The application offers a free geocoding tool as a substitute for commercial solutions and can be found at http://www.opengeocoding.org/

While commercial solutions concentrate on industrial countries – and offer only their service forpostal addresses in these countries – this service offers geocoding for developing countries. Hence, the application can be analternative to the existing commercial geocoding services, independent from the commercial solutions provided that a critical massof participants can be achieved.

Libraries and researchers face a big challenge: sometimes thousands, millions of maps are being conserved in archives to retard deterioration, but this makes it hard to obtain specific knowledge of their content, sometimes even of their existence. In recent years, the efforts have increased to digitise historical documents and maps to preserve them digitally, make them accessible and allow researchers and the general public a less restricted access to their heritage. But maps are more than a cultural artifact: they are data. Data about the past that can very well be important for science and decision-making today.
The problem is, the vast amount alone makes it hard to know what to look for. Maps are usually archived and catalogued with limited meta-information, sometimes obscuring their actual content. This makes it impossible to find specific information without expert knowl-edge on history and cartography. Extracting the content, i.e. data, of scanned historical maps is a necessity to make the content searchable and to use modern digital tools for automatic processing and analyses.
To combine data from different maps and compare them with modern geospatial data, georeferencing is paramount. This is usually done by hand and needs a lot of time and spe-cialised training. We explore if and how usable GCP can be found automatically in historical maps with computer vision and document analysis methods of today. In this work-in-progress report, we use OCR on map labels and geocoding of persisting geographical feature designation for successful first experiments. This shows text recognition and vectorisation to be a promising research direc-tion for large-scale automated georeferencing of historical maps.