Land Use Classification of Marion County, Indiana by Spectral Analysis of Digitized Satellite Data

Multispectral scanner data, obtained over Marion County (Indianapolis), Indiana at an altitude of 915 kilometers, were analyzed by computer-implemented techniques to evaluate the utility of satellite data for urban land use classification. Several land use classes, such as commerce/industry, single-family (newer) residential, trees, and water exhibited spectrally separable characteristics and were identified with greater than 90 per cent accuracy. Difficulties were encountered in the spectral separation of grassy (open, agricultural) areas and multi-family (older) housing. The confusion between these two classes was largely eliminated, however when spectral characteristics of samples (instead of individual data points) were considered. Another solution to the problem consisted of spatially dividing the data into urban and rural land uses prior to classification. Over 95 per cent accuracy of recognition may be achieved by this pre-processing step in an analysis

Urban Land Use Monitoring from Computer-Implemented Processing of Airborne Multispectral Sensor Data

A significant portion of the budget of every city and county planning agency is allocated to the collection of land use data. A planning agency must have information pertinent to a variety of users. Often these information systems are costly, require many people, and are slow. The purpose of this research is to investigate the alternative of using computer-implemented analysis of airborne multispectral scanner data to monitor urban land use. This monitoring should require a limited amount of human intervention. Computer-generated results from multispectral data can be ready for city and county officials to use within 24 hours after the data are acquired

Association of Fresh Waterways and Legionella pneumophila Infection in Eastern Wisconsin: A Case-Control Study

Preliminary research has suggested possible associations between natural waterways and Legionella infection, and we previously explored these associations in eastern Wisconsin using positive L. pneumophila serogroup 1 urine antigen tests (LUAT) as diagnostic. This case-control study was a secondary analysis of home address data from patients who underwent LUAT at a single eastern Wisconsin health system from 2013 to 2017. Only zip codes within the health system’s catchment area that registered ≥3 positive cases and ≥50 completed tests, as well as geographically adjacent zip codes with ≥2 positive cases and ≥50 tests, were included. A 1:3 ratio of cases to randomly selected controls was used. Home addresses were geocoded and mapped using ArcGIS software (Esri); nearest waterway and distance to home was identified. Distance to nearest waterway according to ArcGIS was verified/corrected using Google Maps incognito. Distances were analyzed using chi-squared and 2-sample t-tests. Overall, mean distance to nearest waterway did not differ between cases (2958 ± 2049 ft) and controls (2856 ± 2018 ft; P=0.701). However, in a subset of nonurban zip codes, cases were closer to nearest waterway than controls (1165 ± 905 ft vs 2113 ± 1710 ft; P=0.019). No association was found between cases and type of waterway. Further research is needed to investigate associations and differences between natural and built environmental water sources in relation to legionellosis

Association of Natural Waterways and \u3ci\u3eLegionella pneumophila\u3c/i\u3e Infection in Eastern Wisconsin: A Case-Control Study

Preliminary research has suggested possible associations between natural waterways and Legionella infection, and we previously explored these associations in eastern Wisconsin using positive L. pneumophila serogroup 1 urine antigen tests (LUAT) as diagnostic. This case-control study was a secondary analysis of home address data from patients who underwent LUAT at a single eastern Wisconsin health system from 2013 to 2017. Only zip codes within the health system’s catchment area that registered ≥ 3 positive cases and ≥ 50 completed tests, as well as geographically adjacent zip codes with ≥ 2 positive cases and ≥ 50 tests, were included. A 1:3 ratio of cases to randomly selected controls was used. Home addresses were geocoded and mapped using ArcGIS software (Esri); nearest waterway and distance to home was identified. Distance to nearest waterway according to ArcGIS was verified/corrected using Google Maps incognito. Distances were analyzed using chi-squared and 2-sample t-tests. Overall, mean distance to nearest waterway did not differ between cases (2958 ± 2049 ft) and controls (2856 ± 2018 ft; P = 0.701). However, in a subset of nonurban zip codes, cases were closer to nearest waterway than controls (1165 ± 905 ft vs 2113 ± 1710 ft; P = 0.019). No association was found between cases and type of waterway. Further research is needed to investigate associations and differences between natural and built environmental water sources in relation to legionellosis

Epidemiologic Survey of \u3ci\u3eLegionella\u3c/i\u3e Urine Antigen Testing Within a Large Wisconsin-Based Health Care System

Purpose: Legionella pneumophila pneumonia is a life-threatening, environmentally acquired infection identifiable via Legionella urine antigen tests (LUAT). We aimed to identify cumulative incidence, demographic distribution, and undetected disease outbreaks of Legionella pneumonia via positive LUAT in a single eastern Wisconsin health system, with a focus on urban Milwaukee County. Methods: A multilevel descriptive ecologic study was conducted utilizing electronic medical record data from a large integrated health care system of patients who underwent LUAT from 2013 to 2017. A random sample inclusive of all positive tests was reviewed to investigate geodemographic differences among patients testing positive versus negative. Statistical comparisons used chi-squared or 2-sample t-tests; stepwise regression followed by binary logistic regression was used for multivariable analysis. Positive cases identified by LUAT were mapped to locate hotspots; positive cases versus total tests performed also were mapped by zip code. Results: Of all LUAT performed (n = 21,599), 0.68% were positive. Among those in the random sample (n = 11,652), positive cases by LUAT were more prevalent in the June–November time period (86.2%) and younger patients (59.4 vs 67.7 years) and were disproportionately male (70.3% vs 29.7%) (P \u3c 0.0001 for each). Cumulative incidence was higher among nonwhite race/ethnicity (1.91% vs 1.01%, P \u3c 0.0001) but did not remain significant on multivariable analysis. Overall, 5507 tests were performed in Milwaukee County zip codes, yielding 82 positive cases by LUAT (60.7% of all positive cases in the random sample). A potential small 2016 outbreak was identified. Conclusions: Cumulative incidence of a positive LUAT was less than 1%. LUAT testing, if done in real time by cooperative health systems, may complement public health detection of Legionella pneumonia outbreaks

data for: Effect of climatic vs. biotic drivers on population growth rate varies with range size but not position within range

These are data downloaded from GBIF (the Global Biodiversity Information Facility) for all species described in the manuscript "Effect of climatic vs. biotic drivers on population growth rate varies with range size but not position within range" by Louthan et al. Column names are derived from GBIF records and are described on their website. </p

Development of Fast-Time Simulation Techniques to Model Safety Issues in the National Airspace System

Air Transportation, Design, Human Factors, Modeling and Network Analysis, Safety

TABLE OF CONTENTS

LIST OF FIGURES.......................................................................................................................iii LIST OF TABLES......................................................................................................................... iv SYMBOLS AND ABBREVIATIONS........................................................................................... v SUMMARY....................................................................................................................................