Geospatial Areas Research Articles

AGING WELL: OLDER ADULTS AND DATA MAPPING

Abstract This presentation will feature how the National Council on Aging (NCOA) has advanced a program of work on data mapping and visualization. It will explain how this work is part of NCOA’s mission to improve the lives of millions of older adults, especially those who are struggling. Guided by the idea that how we age should not be determined by gender, color, sexuality, income, or zip code, the mapping tools display area-level data to highlight different aspects of the lives of low-income older adults. This presentation will describe two of NCOA’s mapping tools and how they can influence policy and advocacy efforts toward aging equity at the local, regional, state, and national levels. The first tool, released in 2021, displays, in part, area-level access to smartphones and high-speed internet among low-income older adults. This map makes visible the invisible by demonstrating low rates of access even in geo-spatial areas with overall high levels of connectivity. The second tool, to be released in the third quarter of 2023, presents sub-state level data to identify gaps between area-level eligibility for each of three essential benefit programs [Supplemental Nutrition Assistance Program (SNAP)], Supplemental Security Income (SSI), and Medicare Savings Program (MSP)] and area-level enrollment rates. This map demonstrates how socio-spatial positions influence access to these national benefits, and how maps can be used to identify regions and communities to prioritize as part of strategic efforts to improve benefit access and promote aging equity.

Evaluation of Various Resolution DEMs in Flood Risk Assessment and Practical Rules for Flood Mapping in Data-Scarce Geospatial Areas: A Case Study in Thessaly, Greece

Floods are lethal and destructive natural hazards. The Mediterranean, including Greece, has recently experienced many flood events (e.g., Medicanes Zorbas and Ianos), while climate change results in more frequent and intense flood events. Accurate flood mapping in river areas is crucial for flood risk assessment, planning mitigation measures, protecting existing infrastructure, and sustainable planning. The accuracy of results is affected by all simplifying assumptions concerning the conceptual and numerical model implemented and the quality of geospatial data used (Digital Terrain Models—DTMs). The current research investigates flood modelling sensitivity against geospatial data accuracy using the following DTM resolutions in a mountainous river sub-basin of Thessaly’s Water District (Greece): (a) open 5 m and (b) 2 m data from Hellenic Cadastre (HC) and (c) 0.05 m data from an Unmanned Aerial Vehicle (UAV) topographical mission. RAS-Mapper and HEC-RAS are used for 1D (steady state) hydraulic simulation regarding a 1000-year return period. Results include flood maps and cross section-specific flow characteristics. They are analysed in a graphical flood map-based empirical fashion, whereas a statistical analysis based on the correlation matrix and a more sophisticated Machine Learning analysis based on the interpretation of nonlinear relationships between input–output variables support and particularise the conclusions in a quantifiable manner.

Social Vulnerability Index Predicts Violent Crime in Chicago

INTRODUCTION: The Social Vulnerability Index (SVI), composed of 4 social factors, is used to predict communities most at risk for negative consequences during natural disasters and public health crises. Violence, especially with a firearm, is one such crisis previously shown to behave like a transmissible disease. We hypothesized that SVI could predict community risk for violence. METHODS: The CDC’s 2018 SVI data were merged by census tract with violent crimes (assault, battery, and homicide) from the Chicago Data Portal (Figure). Cubic regression was used to show correlations between crime rate, overall SVI, and each SVI social factor; univariate and multivariate negative binomial regression was performed to calculate risk ratios.SVI, Social Vulnerability Index.RESULTS: SVI predicted risk of violent crime in Chicago. Negative binomial regression found that each percentile increase in SVI resulted in a 7 times increase in violent crime (RR 7.09, 95% CI 6.06 to 8.29, p < 0.001). The factors most strongly associated with violence were socioeconomic status (RR 3.63, 95% CI 2.76 to 4.78, p < 0.001), household composition/disability (RR 2.59, 95% CI 2.09 to 3.22, p < 0.001) and housing type/transportation (RR 1.41, 95% CI 1.60 to 2.25, p < 0.001). Minority status/language was associated with a decreased risk of violent crime (RR 0.34, 95% CI 0.26 to 0.42, p < 0.001). CONCLUSION: This is the first study to demonstrate SVI may predict community risk for violence. SVI can identify geospatial areas of focus for violence intervention and prevention. Academic researchers and trauma surgeons should use SVI when demonstrating disparities related to violent injury risk and outcomes, because it encompasses the effect of social, economic, and built environments at a granular level that is standardized across the US.

The role of high-risk geographies in the perpetuation of the HIV epidemic in rural South Africa: A spatial molecular epidemiology study.

In this study, we hypothesize that HIV geographical clusters (geospatial areas with significantly higher numbers of HIV positive individuals) can behave as the highly connected nodes in the transmission network. Using data come from one of the most comprehensive demographic surveillance systems in Africa, we found that more than 70% of the HIV transmission links identified were directly connected to an HIV geographical cluster located in a peri-urban area. Moreover, we identified a single central large community of highly connected nodes located within the HIV cluster. This module was composed by nodes highly connected among them, forming a central structure of the network that was also connected with the small sparser modules located outside of the HIV geographical cluster. Our study supports the evidence of the high level of connectivity between HIV geographical high-risk populations and the entire community.

Investigating the transmission risk of infectious disease outbreaks through the Aotearoa Co-incidence Network (ACN): a population-based study

SummaryBackgroundThe emergence and re-emergence of infectious diseases presents a significant challenge to public health and broader society. This study utilises novel nationwide data to calculate the transmission risk and potential inequity of infectious disease outbreaks through use of network analysis.MethodsNationwide employment and education microdata (∼4.7 million individuals in Aotearoa New Zealand) were used to develop the Aotearoa Co-incidence Network (ACN). The ACN considers connections generated when individuals are employed at the same workplaces or enrolled at the same schools. Through forms of network analysis, connections between geospatial areas can be established and provide proxy measures of infectious disease transmission risk. The ACN was also overlayed with nationwide population vulnerability data based on the number of older adults (>65 years) and individuals with long-term health conditions.FindingsWe identify areas that have both high potential transmission risk (i.e., highly connected) and high vulnerability to infectious diseases. Community detection identified geographic boundaries that can be relevant to the application of regional restrictions for limiting infectious disease transmission.InterpretationIntegrating novel network science and geospatial analytics provides a simple way to study infectious disease transmission risk and population vulnerability to outbreaks. Our replicable method has utility for researchers globally with access to such data. It can help inform equitable preparation for, and responses to infectious disease outbreaks.FundingThis project was funded by the Health Research Council of New Zealand (20/1442) and from the NZ Government via Ministry for Business Innovation and Employment and Department of Prime Minister and Cabinet.

Geospatial Data Disaggregation through Self-Trained Encoder–Decoder Convolutional Models

Datasets collecting demographic and socio-economic statistics are widely available. Still, the data are often only released for highly aggregated geospatial areas, which can mask important local hotspots. When conducting spatial analysis, one often needs to disaggregate the source data, transforming the statistics reported for a set of source zones into values for a set of target zones, with a different geometry and a higher spatial resolution. This article reports on a novel dasymetric disaggregation method that uses encoder–decoder convolutional neural networks, similar to those adopted in image segmentation tasks, to combine different types of ancillary data. Model training constitutes a particular challenge. This is due to the fact that disaggregation tasks are ill-posed and do not entail the direct use of supervision signals in the form of training instances mapping low-resolution to high-resolution counts. We propose to address this problem through self-training. Our method iteratively refines initial estimates produced by disaggregation heuristics and training models with the estimates from previous iterations together with relevant regularization strategies. We conducted experiments related to the disaggregation of different variables collected for Continental Portugal into a raster grid with a resolution of 200 m. Results show that the proposed approach outperforms common alternative methods, including approaches that use other types of regression models to infer the dasymetric weights.

Localizing hot spots in Poisson radiation data matrices: nonnegative tensor factorization and phase congruency

Detecting and delineating hot spots in data from radiation sensors is required in applications ranging from monitoring large geospatial areas to imaging small objects in close proximity. This paper describes a computational method for localizing potential hot spots in matrices of independent Poisson data where, in numerical terms, a hot spot is a cluster of locally higher sample mean values (higher Poisson intensity) embedded in lower sample mean values (lower background intensity). Two numerical algorithms are computed sequentially for a 3D array of 2D matrices of gross Poisson counts: (1) nonnegative tensor factorization of the 3D array to maximize a Poisson likelihood and (2) phase congruency in pertinent matrices. The indicators of potential hot spots are closed contours in phase congruency in these matrices. The method is illustrated for simulated Poisson radiation datasets, including visualization of the phase congruency contours. The method may be useful in other applications in which there are matrices of nonnegative counts, provided that a Poisson distribution fits the dataset.

Analysis of geospatial areas using electrical resistance tomography

Analysis of geospatial areas using electrical resistance tomography

Comparative Assessment of the Recent Global Mean Sea Surface Models using Tidal Bench Mark Observation Data around Korea

Lee, D.H.; Acharya, T.D., and Jang, W.H., 2019. Comparative assessment of the recent global mean sea surface models using tidal bench mark observation data around Korea. In: Lee, J.L.; Yoon, J.-S.; Cho, W.C.; Muin, M., and Lee, J. (eds.), The 3rd International Water Safety Symposium. Journal of Coastal Research, Special Issue No. 91, pp.401-405. Coconut Creek (Florida), ISSN 0749-0208.Mean Sea Surface (MSS) provides the basic information of mean height of the water level above a reference surface over a year. These are used to understand the characters of the undersea topography and are widely used in geodesy, geophysics, oceanography (operational oceanography, modeling, ocean variability studies). MSS can be obtained from range of techniques such as LiDAR, altimetry, etc. Depending upon the data source and processing method the quality is varied. In the past decade, significant advances in global MSS modelling have been made with the release of the altimetry satellites. These data cover most of the oceans and are freely available, which benefits areas with poor geospatial infrastructure or inaccessible areas. Since, their applications depend on the accuracy of global MSS, their evaluation should be done as preliminary step. MSS models for specific regions can be analyzed based on comparison with the coastal Tidal Bench Marks (TBMs) observations. This paper aims to assess the accuracy of the recent global MSS models in comparison with TBM observation data around Korea. First data were collected, and necessary statistics were calculated. As the characteristics of the sea around Korea are different, there different cases of East, West and South seas were also calculated along with profile plots. Result shows WHU13 shows less difference with TBM while CNESCLS15 and DTU13 are more correlated. In case of Korean sea, combined used of TBM near coast and WHU13 far in sea can be used.

Hotspots vs. harmspots: Shifting the focus from counts to harm in the criminology of place

The prevailing pattern in much of the social sciences, including geography and criminology, relies on count data. “Hotspots” — geospatial areas with disproportionally more crime than the rest of the city — are usually identified by the number of events in these areas. Yet no attention is given to their severity, or any other weighting system of harm, despite the common-sense view that not all crimes are created equal. To illustrate the value of focusing on harm in addition to count data, we turn to a spatial analysis of crime by observing crime concentrations (hotspots) against harm concentrations (harmspots), across fifteen councils in the United Kingdom. The definition of “harm” is based on the Sentencing Guidelines for England and Wales, as each crime category (n = 415) attracts a different severity weight. Both “hotspots” and “harmspots” are defined as being at least 2 standard deviations from the mean distribution within each city: This procedure creates comparable datasets. The data suggest that half of all crime events are concentrated within 3% of all street segments in the selected councils, yet harm is even more heavily concentrated, with half of all harm located in just 1% of each council [OR = 3.49; 95% CI 3.268–3.728]. The intra-unit variance was also reduced by approximately half — from 0.75% to 0.45%. We discuss the implications of using harm, in addition to counts, for research and policy by arguing that a shift in focus is required both for the development of theories and for cost-effective prevention strategies.

Modeling background radiation in Southern Nevada

Aerial gamma ray surveys are an important tool for national security, scientific, and industrial interests in determining locations of both anthropogenic and natural sources of radioactivity. There is a relationship between radioactivity and geology and in the past this relationship has been used to predict geology from an aerial survey. The purpose of this project is to develop a method to predict the radiologic exposure rate of the geologic materials by creating a high resolution background model. The intention is for this method to be used in an emergency response scenario where the background radiation environment is unknown. Two study areas in Southern Nevada have been modeled using geologic data, images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), geochemical data, and pre-existing low resolution aerial surveys from the National Uranium Resource Evaluation (NURE) Survey. Using these data, geospatial areas that are homogenous in terms of K, U, and Th, referred to as background radiation units, are defined and the gamma ray exposure rate is predicted. The prediction is compared to data collected via detailed aerial survey by the Department of Energy's Remote Sensing Lab - Nellis, allowing for the refinement of the technique. By using geologic units to define radiation background units of exposed bedrock and ASTER visualizations to subdivide and define radiation background units within alluvium, successful models have been produced for Government Wash, north of Lake Mead, and for the western shore of Lake Mohave, east of Searchlight, NV.

Geospatial Area Embedding Based on the Movement Purpose Hypothesis Using Large-Scale Mobility Data from Smart Card

With the deployment of modern infrastructure for public transportation, several studies have analyzed movement patterns of people using smart card data and have characterized different areas. In this paper, we propose the “movement purpose hypothesis” that each movement occurs from two causes: where the person is and what the person wants to do at a given moment. We formulate this hypothesis to a synthesis model in which two network graphs generate a movement network graph. Then we develop two novel-embedding models to assess the hypothesis, and demonstrate that the models obtain a vector representation of a geospatial area using movement patterns of people from large-scale smart card data. We conducted an experiment using smart card data for a large network of railroads in the Kansai region of Japan. We obtained a vector representation of each railroad station and each purpose using the developed embedding models. Results show that network embedding methods are suitable for a large-scale movement of data, and the developed models perform better than existing embedding methods in the task of multi-label classification for train stations on the purpose of use data set. Our proposed models can contribute to the prediction of people flows by discovering underlying representations of geospatial areas from mobility data.

Open Source Cloud Computing: An Experience Case of Geo-based Image Handling in Amazon Web Services

In the view from most application system developers and users, cloud computing becomes popular in recent years and is still evolving. But in fact it is not easy to reach at the level of actual operations. Despite, it is known that the cloud in the practical stage provides a new pattern for deploying a geo-spatial application. However, domestically geo-spatial application imptementation and operation based on this concept or scheme is on the beginning stage. It is the motivation of this works. Although this study is an introductory level, a simple and practical processed result was presented. This study was carried out on Amazon web services platform, as infrastructure as a service in the geo-spatial areas. Under this environment, cloud instance, a web and mobile system being previously implemented in the multi-layered structure for geo-spatial open sources of database and application server, was generated. Judging from this example, it is highly possible that cloud services with the functions of geo-processing service and large volume data handling are the crucial point, leading a new business model for civilian remote sensing application and geo-spatial enterprise industry. The further works to extend geo-spatial applications in cloud computing paradigm are left.

Streaming Route Panorama for Seamless City Guidance

This paper introduces an application of route panoramas (RP) in geospatial information visualization. An RP is a long image archiving scenes along a street. We model large geospatial areas such as a town with RPs at a more detailed level than a road map. This paper focuses on the indexing, transmission, and visualization of scenes. The pervasively embedded view-to-space links in the RPs guide transition to detailed spaces. We stream RPs on the Internet in response to the viewer's interaction in virtual city traversing. Various visualization styles of the RP are designed for city guidance on different platforms. Combined with other image media, viewers can look around, traverse a grid of routes, and retrieve information associated to various locations.