National address databases - a data grid approach

Geocoding refers to the process of assigning geographic identifiers and/or geographic coordinates to the description of a feature location, i.e. the words, codes or terms that describe a feature's location. Address matching is the specific case of geocoding where the description of a feature location comprises an address. Address matching is complicated by an incomplete or inaccurate incoming address or one that contains a misleading geographic identifier in its location type hierarchy. The Intiendo address matching tool is based on a data structure that is similar to the hierarchy of location types described in ISO 19112, but we have made some novel extensions to enable a spatial adjacency search. Intiendo does not rely purely on the alphanumeric match in the location type hierarchy but incorporates spatial proximity into the address matching. We also describe how the Intiendo address matching process can be configured and fine tuned, for example, by assigning weights to the location types in the hierarchy, and by specifying parameters for the spatial adjacency match. In this paper we present the hierarchical data structures of the Intiendo address matching tool and show how they are an extended implementation of the ISO 19112 general model. We show the similarities between the Intiendo and ISO 19112 models, and present the extensions that were implemented in Intiendo. By way of examples, we show that our extended model allows more efficient and accurate address matching incorporating spatial adjacency and hierarchical fine tuning.

Spatial data infrastructure (SDI) is the infrastructure that facilitates the discovery, access, management, distribution, reuse, and preservation of digital geospatial resources. These resources may include maps, data, geospatial services, and tools. As cyberinfrastructures, SDIs are similar to other infrastructures, such as water supplies and transportation networks, since they play fundamental roles in many aspects of the society. These roles have become even more significant in today's big data age, when a large volume of geospatial data and Web services are available. From a technological perspective, SDIs mainly consist of data, hardware, and software. However, a truly functional SDI also needs the efforts of people, supports from organizations, government policies, data and software standards, and many others. In this chapter, we will present the concepts and values of SDIs, as well as a brief history of SDI development in the U.S. We will also discuss the components of a typical SDI, and will specifically focus on three key components: geoportals, metadata, and search functions. Examples of the existing SDI implementations will also be discussed.

International Encyclopedia of the Social & Behavioral Sciences, 2001

In the analogue era, concern for spatial data and its distribution were tasks for mapping organisations earning a reputation for producing quality products based on their spatial characteristics, including their visualisation in printed map form to be used for many different types of problems. In contrast, from the second half of the twentieth century onwards, with the developments in computer technology, digital cartography end GIS were also developed to capture, store and analyse spatial data, replacing the tedious analogue map making process. Nowadays, there is a rapid growth of the availability of digital spatial data and a growing need to use it for all kinds of applications in the field of spatial activities. In addition, with the development of today's communication technology, it becomes possible for every user, surfing the Internet, to collect datasets from a variety of sources and different types of application. This requires translation from the original source data into the user's system, to make the received data user understandable and usable. Data transfer from one database to another, requires a spatial data standardisation [Moellering 1991, Moellering and Hogan 1997] in order to allow data providers to store their data into a commonly defined standard way that can be interpreted by the data receiver to display the data meaningful. With the growing number of users, nationally and internationally, the information about what data is available becomes an important knowledge. In many countries, governments, providers and private organisations have been building digital infra-structural networks to enable development of the countrywide spatial data and information flow. Provisions for the distribution of spatial (or geographic) data can also be viewed as an infrastructure (SDI, Spatial Data Infrastructure). Providers of such data often developed a common site in the Internet to display the type of data they have available, in order to display their products and promote its use. Sometimes, they also make the data available through these channels. In order to understand each other's descriptions also the description of the datasets were standardised: called metadatasets [Mouldering and Crane 2001]. Globally, GSDI aims at the linking of national and international SDI into a global and open process for the co-ordination, organisation management and use of spatial data and related activities. The focus of GSDI is rather on implementation of SDIs rather than on research. GSDI has published a "Cookbook" for implementing SDIs, which will be published on the Internet (). Using the forthcoming GSDI Cookbook may help each new SDI implementation to consider its capabilities and legal implications. A comparison of the aspects of the different implementations in different countries -that could be researched by the ICA Commission on Spatial Data Standards in future -will enhance these implementations. Hence, this article will summarise and review these aspects for the ICA Commission on Spatial Data Standards to potentially research the existing SDIs including the technological, legal, administrative, financial and organisational aspects.

International Journal of Geographical Information Science, 2013

The Commission on Geoinformation Infrastructures and Standards of the International Cartographic Association (ICA) is working on defining models of spatial data infrastructures (SDI). SDI models from the enterprise and information viewpoints of the Reference Model for Open Distributed Processing (RM-ODP) have already been presented. Our model from the computational viewpoint identifies the main computational objects of an SDI and their interfaces, which are modelled using Unified Modelling Language (UML) component diagrams. Presented here is the first comprehensive SDI model from the computational viewpoint, which enhances the understanding of the computational objects and their interactions in an SDI. This viewpoint complements the previous two and together, the three viewpoints contribute towards a more holistic interpretation of an SDI, which is independent of specific SDI legislation, technology and implementations. For the computational viewpoint, we identified six computational objects, SDI Registry, SDI Data, SDI Processing, SDI Application, SDI Portrayal and SDI Management, and their provided and required interfaces. We describe the interactions of the computational objects in stakeholder activities and the roles they play in the different processes of SDI development and use, which we identified as Initiation, Creation, Management, Manipulation, Access, Processing, Evaluation and Liaison. Tw o t a b l e s s um m a r i se th e S D I se r v i c e s t h a t a r e p r o v i d e d by computational objects for stakeholder activities and SDI processes.

The "United States Street, Landmark, and Postal Address Data Standard" is a draft data standard for United States address information. The draft standard defines and specifies elements and structures for organizing address data, defines tests of address data quality, and facilitates address data exchange. The draft standard has four parts: Data Content, Data Classification, Data Quality, and Data Exchange.

The widespread availability of powerful geocoding tools in commercial GIS software and the interest in spatial analysis at the individual level have made address geocoding a widely employed technique in many different fields. The most commonly used approach to geocoding employs a street network data model, in which addresses are placed along a street segment based on a linear interpolation of the location of the street number within an address range. Several alternatives have emerged, including the use of address points and parcels, but these have not received widespread attention in the literature. This paper reviews the foundation of geocoding and presents a framework for evaluating geocoding quality based on completeness, positional accuracy and repeatability. Geocoding quality was compared using three address data models: address points, parcels and street networks. The empirical evaluation employed a variety of different address databases for three different Counties in Florida. Results indicate that address point geocoding produces geocoding match rates similar to those observed for street network geocoding. Parcel geocoding generally produces much lower match rates, in particular for commercial and multi-family residential addresses. Variability in geocoding match rates between address databases and between geographic areas is substantial, reinforcing the need to strengthen the development of standards for address reference data and improved address data entry validation procedures.

Transactions in …, 2004

A growing number of services are now being offered over mobile devices. They typically combine positioning technology, wireless technology and spatial analysis methods applied to detailed geographical, time based data to offer services in support of time critical, spatial, mobile decision making. A collection of research issues need to be addressed in the successful delivery of such services that extend beyond issues of sophisticated network algorithms. Specifically, careful attention needs to be given to: (1) people and user environments; (2) access to networks; (3) policy, privacy and liability; (4) standards and interoperability; and (5) data quality. Spatial Data Infrastructure (SDI) is the collective term for these interconnected issues and has been a traditional area of research associated with geographic information science. In this paper the particular SDI requirements for the successful delivery of Location Based Services (LBS) are explored through the development of a prototype LBS for journey planning. The initial implementation and testing of this prototype has revealed that the SDI context is well suited as a framework within which to examine the related LBS issues. From a more technical perspective, the testing has revealed that data structure and the means by which large data sets are mined (in order to gather information to present to users) is critical to the success of timely information delivery over limited bandwidth media.

Lecture Notes in Computer Science, 2004

The growing importance of eGoverment is putting pressure on authorities to provide services online and accessible to the citizen. In order to satisfy this necessity, Goverments have to develop high-cost infrastructures in a short period of time and with a diffuse specifications of the objectives and services. In the other hand, there are a large set of initiatives that have been launched for the development of spatial data infrastructures. This kind of infrastructures are the basic tool for optimizing the management of geographic information because its amount and complexity. Most of these initiatives are being promoted and sponsored by public administrations because geographic information is a basic resource for their operative work. This paper proposes the development of eGoverment infrastructures over the spatial data infrastructures launched, viewing them as the providers of the first version of the eGoverment services.

2003

One of the most important features in an urban geographic information system is the ability to locate addresses, in any form employed by the population, in a quick and efficient way. From an organized set of georeferenced address data, local governments, infrastructure companies, and the various agents that need to work in urban spaces can reliably and precisely locate points of their interest, in fields of application as diverse as public health services, crime fighting, product distribution, postal delivery and many others. This activity is known as geocoding, which can be defined as the process of locating points on the surface of the Earth from alphanumeric addressing data associated to events. This paper shows how geocoding can be implemented over incomplete and possibly inaccurate addressing data (which is the prevailing situation in Brazilian cities) and how users can benefit from the results of such a process. A geocoding quality indicator is proposed, with the intention of providing an indication of the degree of certainty that is associated with each individual result of the geocoding process. This indicator can then be used in spatial analysis, by giving analysts the opportunity to include the uncertainty associated with the data in their work. This project is under development, and a set of (as yet unimplemented) geocoding tools is proposed and described in the paper.

The Digitising Scotland (DS) project aims to digitise the 24 million vital events record images (births, marriages and deaths) for all residents in Scotland since 1855 (ie transcribe them into machine encoded text). This will allow research access to information on individuals and their families for those who have ever lived in Scotland between 1855 to the present day. In this paper we present the methodology for geocoding these 24 million historical addresses in Scotland from 1855 to 1974 by introducing the Historical Address Geocoder – GIS (HAG-GIS) spatial framework and its matching algorithms implemented for the needs of the DS project. The matching processes link the historical addresses to the contemporary addresses by exact and fuzzy matching algorithms. Apart from geocoding the historical addresses, we also produce pseudo registration district boundaries using the pilot historical addresses from death event records in 1950 and 1951.