Daniel W. Goldberg

An effective and efficient approach for manually improving geocoded data

BackgroundThe process of geocoding produces output coordinates of varying degrees of quality. Previous studies have revealed that simply excluding records with low-quality geocodes from analysis can introduce significant bias, but depending on the number and severity of the inaccuracies, their inclusion may also lead to bias. Little quantitative research has been presented on the cost and/or effectiveness of correcting geocodes through manual interactive processes, so the most cost effective methods for improving geocoded data are unclear. The present work investigates the time and effort required to correct geocodes contained in five health-related datasets that represent examples of data commonly used in Health GIS.ResultsGeocode correction was attempted on five health-related datasets containing a total of 22,317 records. The complete processing of these data took 11.4 weeks (427 hours), averaging 69 seconds of processing time per record. Overall, the geocodes associated with 12,280 (55%) of records were successfully improved, taking 95 seconds of processing time per corrected record on average across all five datasets. Geocode correction improved the overall match rate (the number of successful matches out of the total attempted) from 79.3 to 95%. The spatial shift between the location of original successfully matched geocodes and their corrected improved counterparts averaged 9.9 km per corrected record. After geocode correction the number of city and USPS ZIP code accuracy geocodes were reduced from 10,959 and 1,031 to 6,284 and 200, respectively, while the number of building centroid accuracy geocodes increased from 0 to 2,261.ConclusionThe results indicate that manual geocode correction using a web-based interactive approach is a feasible and cost effective method for improving the quality of geocoded data. The level of effort required varies depending on the type of data geocoded. These results can be used to choose between data improvement options (e.g., manual intervention, pseudocoding/geo-imputation, field GPS readings).

Prostate Cancer and Ambient Pesticide Exposure in Agriculturally Intensive Areas in California

In a population-based case-control study in Californias intensely agricultural Central Valley (2005-2006), the authors investigated relations between environmental pesticide/fungicide exposure and prostate cancer. Cases (n = 173) were obtained from a population-based cancer registry, and controls (n = 162) were obtained from Medicare listings and tax assessor mailings. Past ambient exposures to pesticides/fungicides were derived from residential history and independently recorded pesticide and land-use data, using a novel geographic information systems approach. In comparison with unexposed persons, increased risks of prostate cancer were observed among persons exposed to compounds which may have prostate-specific biologic effects (methyl bromide (odds ratio = 1.62, 95% confidence interval: 1.02, 2.59) and a group of organochlorines (odds ratio = 1.64, 95% confidence interval: 1.02, 2.63)) but not among those exposed to other compounds that were included as controls (simazine, maneb, and paraquat dichloride). The authors assessed the possibility of selection bias due to less-than-100% enrollment of eligible cases and controls (a critical methodological concern in studies of this kind) and determined that there was little evidence of bias affecting the estimated effect size. This study provides evidence of an association between prostate cancer and ambient pesticide exposures in and around homes in intensely agricultural areas. The associations appear specific to compounds with a plausible biologic role in prostate carcinogenesis.

The joint effects of census tract poverty and geographic access on late-stage breast cancer diagnosis in 10 US States

This study evaluated independent and joint effects of census tract (CT) poverty and geographic access to mammography on stage at diagnosis for breast cancer. The study included 161,619 women 40+ years old diagnosed with breast cancer between 2004 -2006 in ten participating US states. Multilevel logistic regression was used to estimate the odds of late-stage breast cancer diagnosis for the entire study population and by state. Poverty was independently associated with late-stage in the overall population (poverty rates >20% OR=1.30, 95% CI=1.26- 1.35) and for 9 of the 10 states. Geographic access was not associated with late-stage diagnosis after adjusting for CT poverty. State-specific analysis provided little evidence that geographic access was associated with breast cancer stage at diagnosis, and after adjusting for poverty, geographic access mattered in only 1 state. Overall, compared to women with private insurance, the adjusted odds ratios for late stage at diagnosis among women with either no insurance, Medicaid, or Medicare were 1.80 (95% CI = 1.65, 1.96), 1.75 (95% CI = 1.68, 1.84), and 1.05 (95% CI 1.01, 1.08), respectively. Although geographic access to mammography was not a significant predictor of late-stage breast cancer diagnosis, women in high poverty areas or uninsured are at greatest risk of being diagnosed with late-stage breast cancer regardless of geographic location and may benefit from targeted interventions.

Extracting geographic features from the Internet to automatically build detailed regional gazetteers

The utility of every imaginable application which incorporates a gazetteer hinges on the simple fact that the resulting system will only be as useful, complete, or accurate as the underlying gazetteer itself. A major issue confronting gazetteers utilized in systems today is that they are not complete and measures of their accuracy are largely unknown. In this paper we describe a methodology which addresses this problem by automatically generating highly complete and detailed regional gazetteers from Internet sources. We utilize information extraction and integration techniques to automatically obtain geographic features and associated footprints and feature types from freely and widely available online data which could be applied to create a gazetteer for nearly any area. We discuss the distinguishing characteristics of the generated gazetteer and extend previous work to define measures which can be used to assess the completeness and accuracy of gazetteers. Using these measures, the generated gazetteer is evaluated against the Alexandria Digital Library Gazetteer and the Los Angeles Comprehensive Bibliographic Database. Our results indicate that a gazetteer created by our methods will be at least as complete as any gazetteer currently available for certain feature classes, while falling short in others. We conclude by offering suggestions to address these shortcomings.

World of workout: a contextual mobile RPG to encourage long term fitness

In todays digital world many individuals spend their day in front of a computer or mobile phone for entertainment. Individuals enjoy a more sedentary lifestyle from advances in technology. This is one of the leading factors contributing to a decrease in fitness level for large parts of the populations in developed countries. We want to design a mobile role-playing game (RPG) where the character evolves based on the exercises the user performs in reality. This design can motivate and persuade a potentially large demographic of users to engage in physical activity for an extended period of time through the enjoyment of an engaging game. This novel application has shown the capability of automatically identifying and counting the exercises performed by the user. This automatic activity recognition and numeration is performed solely through the accelerometer of a single smartphone held by the user while exercising. The type and amount of exercise improve the characters speed, strength, and stamina based on the type and amount of exercise performed.

The effect of administrative boundaries and geocoding error on cancer rates in California.

Geocoding is often used to produce maps of disease rates from the diagnosis addresses of incident cases to assist with disease surveillance, prevention, and control. In this process, diagnosis addresses are converted into latitude/longitude pairs which are then aggregated to produce rates at varying geographic scales such as Census tracts, neighborhoods, cities, counties, and states. The specific techniques used within geocoding systems have an impact on where the output geocode is located and can therefore have an effect on the derivation of disease rates at different geographic aggregations. This paper investigates how county-level cancer rates are affected by the choice of interpolation method when case data are geocoded to the ZIP code level. Four commonly used areal unit interpolation techniques are applied and the output of each is used to compute crude county-level five-year incidence rates of all cancers in California. We found that the rates observed for 44 out of the 58 counties in California vary based on which interpolation method is used, with rates in some counties increasing by nearly 400% between interpolation methods.

Advances in geocoding for the health sciences.

The process of geocoding – turning textual address data into geographic representations – is one of the fundamental building blocks used throughout health science research. The ability to place individuals, groups, health services, etc. into a geographic context provided by these tools enables researchers, community groups, and policy makers to perform spatially-based research into the causes of diseases, the efficacy of treatment and outreach approaches, and the equitability of access to care and services. Other essential services that geocoded data facilitate include disease surveillance and tracking, exploratory visualization and analysis techniques, and the development of targeted prevention policies and services. This importance of geocoding and geocoded data to the full breadth and depth of the health science community is clearly evidenced by an ever-increasing number of scientific articles that rely on these techniques and data. Among numerous other examples, the last few decades of health science research have seen a marked rise in the number and diversity of studies that have employed a spatial lens to uncover links between ambient environmental exposures and health outcomes, to identify medically underserved communities, and to understand the nature of how, why, where, and when individuals and groups make decisions that affect their lifestyles and health. The vast majority these studies have used some form of geocoding as a first methodological step to place people, services, or other forms of resources with a geographic component onto a map for further spatio-temporal analysis. However, since its earliest uses in health science research, geocoding and geocoded data have been both a blessing and a curse. Along with the new analytical avenues that these data open up, they also introduce a host of potential pitfalls when researchers or policy makers do not understand (or simply ignore) the challenges and/or limitations inherent in these data and what they can be reliably used for. The quality of geocoded data are often considered along four axes, each of which has important consequences on subsequent analyses and decision-making: (1) match rates – the percentage of records capable of be geocoding to a sufficient level of quality; (2) match score – the confidence associated with any particular record describing how likely it is that the input address

Step up life: a context aware health assistant

A recent trend in the popular health news is, reporting the dangers of prolonged inactivity in ones daily routine. The claims are wide in variety and aggressive in nature, linking a sedentary lifestyle with obesity and shortened lifespans [25]. Rather than enforcing an individual to perform a physical exercise for a predefined interval of time, we propose a design, implementation, and evaluation of a context aware health assistant system (called Step Up Life) that encourages a user to adopt a healthy life style by performing simple, and contextually suitable physical exercises. Step Up Life is a smart phone application which provides physical activity reminders to the user considering the practical constraints of the user by exploiting the context information like the user location, personal preferences, calendar events, time of the day and the weather [9]. A fully functional implementation of Step Up Life is evaluated by user studies.

An Evaluation Framework for Comparing Geocoding Systems

BackgroundGeocoding, the process of converting textual information describing a location into one or more digital geographic representations, is a routine task performed at large organizations and government agencies across the globe. In a health context, this task is often a fundamental first step performed prior to all operations that take place in a spatially-based health study. As such, the quality of the geocoding system used within these agencies is of paramount concern to the agency (the producer) and researchers or policy-makers who wish to use these data (consumers). However, geocoding systems are continually evolving with new products coming on the market continuously. Agencies must develop and use criteria across a number axes when faced with decisions about building, buying, or maintaining any particular geocoding systems. To date, published criteria have focused on one or more aspects of geocode quality without taking a holistic view of a geocoding system’s role within a large organization. The primary purpose of this study is to develop and test an evaluation framework to assist a large organization in determining which geocoding systems will meet its operational needs.MethodsA geocoding platform evaluation framework is derived through an examination of prior literature on geocoding accuracy. The framework developed extends commonly used geocoding metrics to take into account the specific concerns of large organizations for which geocoding is a fundamental operational capability tightly-knit into its core mission of processing health data records. A case study is performed to evaluate the strengths and weaknesses of five geocoding platforms currently available in the Australian geospatial marketplace.ResultsThe evaluation framework developed in this research is proven successful in differentiating between key capabilities of geocoding systems that are important in the context of a large organization with significant investments in geocoding resources. Results from the proposed methodology highlight important differences across all axes of geocoding system comparisons including spatial data output accuracy, reference data coverage, system flexibility, the potential for tight integration, and the need for specialized staff and/or development time and funding. Such results can empower decisions-makers within large organizations as they make decisions and investments in geocoding systems.

Maps & GIS Data Libraries in the Era of Big Data and Cloud Computing

In upcoming years, two major changes in the computing landscape will reshape how map and GIS data libraries (MGDLs) will be required to perform their core functions in the future. These advancements—cloud computing and the “Big Data era”—offer opportunities and challenges for libraries, but most dramatically changes. Commercial cloud computing solutions are available as on-demand service; low-cost, internal private clouds are now a financial possibility. Simultaneously, Geographic Information Science (GISci) data and services have advanced at an ever-increasing pace into the “Big Data,” swelling the types and amounts of GIS data and services available. These two shifts have and will impact the entire GIS world and MGDLs, in response, and will now be required to collect, curate, and make available more data and services than ever. The MGDL community must be prepared to respond and react in order to remain effective. This article explores the evolving landscapes within which MGDLs must operate and examines how their roles and operational organization will be impacted. The hope of the authors is that such analyses will spark community-driven discussion to motivate the next major phase of research and implementation in the world of MGDLs.