A multifaceted comparison of ArcGIS and MapMarker for automated geocoding

Kumar, Sunny; Liu, Ming; Hwang, Syni‐An

doi:10.4081/gh.2012.113

Cited by 11 publications

(10 citation statements)

References 10 publications

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“…For the core alleles, maps of their frequency distribution in human populations were generated using ArcGis 10 [69]. Population allele frequencies and location coordinates were downloaded from allelefrequencies.net [70].…”

Section: Methodsmentioning

confidence: 99%

Distinguishing functional polymorphism from random variation in the sequences of >10,000 HLA-A, -B and -C alleles

et al. 2017

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HLA class I glycoproteins contain the functional sites that bind peptide antigens and engage lymphocyte receptors. Recently, clinical application of sequence-based HLA typing has uncovered an unprecedented number of novel HLA class I alleles. Here we define the nature and extent of the variation in 3,489 HLA-A, 4,356 HLA-B and 3,111 HLA-C alleles. This analysis required development of suites of methods, having general applicability, for comparing and analyzing large numbers of homologous sequences. At least three amino-acid substitutions are present at every position in the polymorphic α1 and α2 domains of HLA-A, -B and -C. A minority of positions have an incidence >1% for the ‘second’ most frequent nucleotide, comprising 70 positions in HLA-A, 85 in HLA-B and 54 in HLA-C. The majority of these positions have three or four alternative nucleotides. These positions were subject to positive selection and correspond to binding sites for peptides and receptors. Most alleles of HLA class I (>80%) are very rare, often identified in one person or family, and they differ by point mutation from older, more common alleles. These alleles with single nucleotide polymorphisms reflect the germ-line mutation rate. Their frequency predicts the human population harbors 8–9 million HLA class I variants. The common alleles of human populations comprise 42 core alleles, which represent all selected polymorphism, and recombinants that have assorted this polymorphism.

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Section: Methodsmentioning

confidence: 99%

Distinguishing functional polymorphism from random variation in the sequences of >10,000 HLA-A, -B and -C alleles

et al. 2017

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“…Although it is used mostly in high-income contexts, its utility in LMICs is also appreciated (22,25,54,71,84). A 2012 study from China uses nighttime imagery from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS).…”

Section: Discussionmentioning

confidence: 99%

“…A number of GIS software utilities are available online, such as ArcGIS (54), and limited versions are sometimes made available for free. Open data policies of cities from countries such as the United States, the Netherlands, the United Kingdom, and Australia are powering a new civic movement that is changing the way citizens experience their cities and improving their understanding of health risks and health care.…”

Section: Discussionmentioning

confidence: 99%

Metrics in Urban Health: Current Developments and Future Prospects

Prasad¹,

Gray²,

Ross³

et al. 2016

Annu. Rev. Public Health

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The research community has shown increasing interest in developing and using metrics to determine the relationships between urban living and health. In particular, we have seen a recent exponential increase in efforts aiming to investigate and apply metrics for urban health, especially the health impacts of the social and built environments as well as air pollution. A greater recognition of the need to investigate the impacts and trends of health inequities is also evident through more recent literature. Data availability and accuracy have improved through new affordable technologies for mapping, geographic information systems (GIS), and remote sensing. However, less

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“…Alternatively, such a map tool can be used to identify where people test for HIV or might be finding high risk sex partners (it may not be easy to obtain a valid address for the latter location). Even given a known address, however, automated geocoding systems vary in the degree of accuracy, result in nonnormally distributed errors, and may be less accurate outside of urban areas [15,16]. …”

Section: Introductionmentioning

confidence: 99%

Use of a Google Map Tool Embedded in an Internet Survey Instrument: Is it a Valid and Reliable Alternative to Geocoded Address Data?

Dasgupta¹,

Vaughan²,

Kramer³

et al. 2014

JMIR Res Protoc

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BackgroundMen who have sex with men (MSM) in the United States are at high risk for human immunodeficiency virus (HIV) and poor HIV related outcomes. Maps can be used to identify, quantify, and address gaps in access to HIV care among HIV-positive MSM, and tailor intervention programs based on the needs of patients being served.ObjectiveThe objective of our study was to assess the usability of a Google map question embedded in a Web-based survey among Atlanta-based, HIV-positive MSM, and determine whether it is a valid and reliable alternative to collection of address-based data on residence and last HIV care provider.MethodsAtlanta-based HIV-positive MSM were recruited through Facebook and from two ongoing studies recruiting primarily through venue-based sampling or peer referral (VBPR). Participants were asked to identify the locations of their residence and last attended HIV care provider using two methods: (1) by entering the street address (gold standard), and (2) “clicking” on the locations using an embedded Google map. Home and provider addresses were geocoded, mapped, and compared with home and provider locations from clicked map points to assess validity. Provider location error values were plotted against home location error values, and a kappa statistic was computed to assess agreement in degree of error in identifying residential location versus provider location.ResultsThe median home location error across all participants was 0.65 miles (interquartile range, IQR, 0.10, 2.5 miles), and was lower among Facebook participants (P<.001), whites (P<.001), and those reporting higher annual household income (P=.04). Median home location error was lower, although not statistically significantly, among older men (P=.08) and those with higher educational attainment (P=.05). The median provider location error was 0.32 miles (IQR, 0.12, 1.2 miles), and did not vary significantly by age, recruitment method, race, income, or level of educational attainment. Overall, the kappa was 0.20, indicating poor agreement between the two error measures. However, those recruited through Facebook had a greater level of agreement (κ=0.30) than those recruited through VBPR methods (κ=0.16), demonstrating a greater level of consistency in using the map question to identify home and provider locations for Facebook-recruited individuals.ConclusionsMost participants were able to click within 1 mile of their home address and their provider’s office, and were not always able to identify the locations on a map consistently, although some differences were observed across recruitment methods. This map tool may serve as the basis of a valid and reliable tool to identify residence and HIV provider location in the absence of geocoded address data. Further work is needed to improve and compare map tool usability with the results from this study.

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A multifaceted comparison of ArcGIS and MapMarker for automated geocoding

Cited by 11 publications

References 10 publications

Distinguishing functional polymorphism from random variation in the sequences of >10,000 HLA-A, -B and -C alleles

Distinguishing functional polymorphism from random variation in the sequences of >10,000 HLA-A, -B and -C alleles

Metrics in Urban Health: Current Developments and Future Prospects

Use of a Google Map Tool Embedded in an Internet Survey Instrument: Is it a Valid and Reliable Alternative to Geocoded Address Data?

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