Browsing by Author "Johnson, Daniel P. (Daniel Patrick), 1971-"
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Item Comparing Methods for Estimation of Daytime Population in Downtown Indianapolis, Indiana(2011-08-23) Bell, Karen Denise; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Banerjee, Aniruddha; Johnson, Daniel P. (Daniel Patrick), 1971-This paper compares two new methods for estimating daytime population against two existing models within downtown Indianapolis in Marion County, Indiana. The two existing models consist of the 2009 USA Daytime Population model created by ESRI and the LandScan Global Population Project developed by the Oak Ridge National Laboratory. A parking study of downtown Indianapolis, as prepared by the City of Indianapolis, Division of Metropolitan Development, is the basis of the first new method of estimating daytime population. The second method is a direct count of the daytime population using a methodology previously developed. Additionally, these four population estimates will be compared when applied to a scenario involving a hypothetical toxic gas plume.Item Comparison between high-resolution aerial imagery and lidar data classification of canopy and grass in the NESCO neighborhood, Indianapolis, Indiana(2014) Ye, Nan; Johnson, Daniel P. (Daniel Patrick), 1971-; Bein, Frederick L. (Frederick Louis), 1943-; Lulla, Vijay O.Urban forestry is a very important element of urban structures that can improve the environment and life quality within the urban areas. Having an accurate classification of urban forests and grass areas would help improve focused urban tree planting and urban heat wave mitigation efforts. This research project will compare the use of high – resolution aerial imagery and LiDAR data when used to classify canopy and grass areas. The high – resolution image, with 1 – meter resolution, was captured by The National Agriculture Imagery Program (NAIP) on 6/6/2012. Its coordinate system is the North American Datum of 1983 (NAD83). The LiDAR data, with 1.0 – meter average post spacing, was captured by Indiana Statewide Imagery and LiDAR Program from 03/13/2011 to 04/30/2012.The study area is called the Near East Side Community Organization (NESCO) neighborhood. It is located on the east side of downtown Indianapolis, Indiana. Its boundaries are: 65 interstate, East Massachusetts Avenue, East 21st Street, North Emerson Avenue, and the rail road tracks on the south of the East Washington Street. This research will also perform the accuracy assessment based on the results of classifications using high – resolution aerial imagery and LiDAR data in order to determine and explain which method is more accurate to classify urban canopy and grass areas.Item A comparison of geocoding baselayers for electronic medical record data analysis(2014-01-16) Severns, Christopher Ray; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Johnson, Daniel P. (Daniel Patrick), 1971-; Martin, Pamela A.Identifying spatial and temporal patterns of disease occurrence by mapping the residential locations of affected people can provide information that informs response by public health practitioners and improves understanding in epidemiological research. A common method of locating patients at the individual level is geocoding residential addresses stored in electronic medical records (EMRs) using address matching procedures in a geographic information system (GIS). While the process of geocoding is becoming more common in public health studies, few researchers take the time to examine the effects of using different address databases on match rate and positional accuracy of the geocoded results. This research examined and compared accuracy and match rate resulting from four commonly-used geocoding databases applied to sample of 59,341 subjects residing in and around Marion County/ Indianapolis, IN. The results are intended to inform researchers on the benefits and downsides to their selection of a database to geocode patient addresses in EMRs.Item The correlation of sea surface temperatures, sea level pressure and vertical wind shear with ten tropical cyclones between 1981-2010(2013-11-12) Compton, Andrea Jean; Martin, Pamela A.; Johnson, Daniel P. (Daniel Patrick), 1971-; Wilson, Jeffrey S. (Jeffrey Scott), 1967-Item Exploring Spatial Optimization Techniques for the Placement of Flow Monitors Utilized in RDII Studies(2010-08-31T14:31:55Z) Skehan, Christopher A.; Banerjee, Aniruddha; Johnson, Daniel P. (Daniel Patrick), 1971-; Wilson, Jeffrey S. (Jeffrey Scott), 1967-The aging infrastructure of a wastewater collection system can leak, capture ground water, and capture precipitation runoff. These are some of the most common problems in many of today’s US collection systems and are often collectively referred to as Rain Derived Inflow and Infiltration (RDII or I/I). The goal of this study is to investigate such optimized methods and their potential to improve flow monitor placement, especially for RDII studies, and to improve upon Stevens (2005) methodology. This project adopts a methodology from the “facility location problem”, a branch of operations research and graph theory. Solutions to a facility location problem will be adapted and utilized within a transportation GIS application to determine optimal placement.Item EXTREME HEAT EVENT RISK MAP CREATION USING A RULE-BASED CLASSIFICATION APPROACH(2012-03-19) Simmons, Kenneth Rulon; Johnson, Daniel P. (Daniel Patrick), 1971-; Banerjee, Aniruddha; Wilson, Jeffrey S. (Jeffrey Scott), 1967-During a 2011 summer dominated by headlines about an earthquake and a hurricane along the East Coast, extreme heat that silently killed scores of Americans largely went unnoticed by the media and public. However, despite a violent spasm of tornadic activity that claimed over 500 lives during the spring of the same year, heat-related mortality annually ranks as the top cause of death incident to weather. Two major data groups used in researching vulnerability to extreme heat events (EHE) include socioeconomic indicators of risk and factors incident to urban living environments. Socioeconomic determinants such as household income levels, age, race, and others can be analyzed in a geographic information system (GIS) when formatted as vector data, while environmental factors such as land surface temperature are often measured via raster data retrieved from satellite sensors. The current research sought to combine the insights of both types of data in a comprehensive examination of heat susceptibility using knowledge-based classification. The use of knowledge classifiers is a non-parametric approach to research involving the creation of decision trees that seek to classify units of analysis by whether they meet specific rules defining the phenomenon being studied. In this extreme heat vulnerability study, data relevant to the deadly July 1995 heat wave in Chicago’s Cook County was incorporated into decision trees for 13 different experimental conditions. Populations vulnerable to heat were identified in five of the 13 conditions, with predominantly low-income African-American communities being particularly at-risk. Implications for the results of this study are given, along with direction for future research in the area of extreme heat event vulnerability.Item IDENTIFYING VARIATIONS OF SOCIO-SPATIAL VULNERABILITY TO HEAT-RELATED MORTALITY DURING THE 1995 EXTREME HEAT EVENT IN CHICAGO, IL, USA(2011-08-23) Stanforth, Austin Curran; Johnson, Daniel P. (Daniel Patrick), 1971-; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Dwyer, Owen J.Extreme Heat Events are the leading cause of weather-related mortalities in the continental United States. Recent publications have suggested that vulnerability to extreme heat is impacted by variations in environmental and socioeconomic conditions, even across small spatial units. This study evaluated the usefulness of socioeconomic variables and satellite-derived environmental measurements as predictors of heat-related vulnerability during the July 14-17, 1995 heat wave in Chicago, IL. Geospatial analysis and statistical processes were implemented to identify and rank characteristics of vulnerable populations. Results suggest population density, educational attainment, age, and financial indicators are among the best predictors of heat vulnerability. Proximity to and intensity of Urban Heat Islands also appears to influence neighborhood vulnerability levels. Identification and mapping of vulnerability variables can distinguish locations of increased vulnerability during extreme weather conditions. These vulnerability maps could be utilized by city officials to plan and implement aid programs to specific high risk neighborhoods before an extreme heat event, and resulting health implications, occur. Continued study and implementation of these variables could also assist in identifying vulnerable populations in other urban environments, improve utilization of location-specific heat warning systems and impact new building policies to decrease vulnerability variables across the country.Item Predicting locations for urban tree planting(2014) King, Steven M.; Johnson, Daniel P. (Daniel Patrick), 1971-; Bein, Frederick L. (Frederick Louis), 1943-; Lulla, Vijay O.The purpose of this study was to locate the most suitable blocks to plant trees within Indianapolis, Indiana’s Near Eastside Community (NESCO). LiDAR data were utilized, with 1.0 meter average post spacing, captured by the Indiana Statewide Imagery and LiDAR Program from March 13, 2011 to April 30, 2012, to conduct a covertype classification and identify blocks that have low canopies, high impervious surfaces and high surface temperatures. Tree plantings in these blocks can help mitigate the effects of the urban heat island effect. Using 2010 U.S. Census demographic data and the principal component analysis, block groups with high social vulnerability were determined, and tree plantings in these locations could help reduce mortality from extreme heat events. This study also determined high and low priority plantable space in order to emphasize plantable spaces with the potential to shade buildings; this can reduce cooling costs and the urban heat island, and it can maximize the potential of each planted tree.Item Predicting Water Quality By Relating Secchi Disk Transparency Depths To Landsat 8(2015-08) Hancock, Miranda J.; Lulla, Vijay O.; Johnson, Daniel P. (Daniel Patrick), 1971-; Bein, Frederick L. (Frederick Louis), 1943-Monitoring lake quality remotely offers an economically feasible approach as opposed to in-situ field data collection. Researchers have demonstrated that lake clarity can be successfully monitored through the analysis of remote sensing. Evaluating satellite imagery, as a means of water quality detection, offers a practical way to assess lake clarity across large areas, enabling researchers to conduct comparisons on a large spatial scale. Landsat data offers free access to frequent and recurring satellite images. This allows researchers the ability to make temporal comparisons regarding lake water quality. Lake water quality is related to turbidity which is associated with clarity. Lake clarity is a strong indicator of lake health and overall water quality. The possibility of detecting and monitoring lake clarity using Landsat8 mean brightness values is discussed in this report. Lake clarity is analyzed in three different reservoirs for this study; Brookeville, Geist, and Eagle Creek. In-situ measurements obtained from Brookeville Reservoir were used to calibrate reflectance from Landsat 8’s Operational Land Imager (OLI) satellite. Results indicated a correlation between turbidity and brightness values, which are highly correlated in algal dominated lakes.Item Public perception and response to extreme heat events(2014-01-03) Porter, Raymond E.; Johnson, Daniel P. (Daniel Patrick), 1971-; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Dwyer, Owen J.In the United States extreme heat events have grown in size and stature over the past 20 years. Urban Heat Islands exacerbate these extreme heat events leaving a sizable portion of people at risk for heat related fatalities. The evidence of this is seen in the Chicago heat wave of 1995 which killed 500 people over the course of a week and the European heat wave of 2003 which killed 7,000 people in the course of a month. The main guiding questions then become how government and the media can most effectively warn people about the occurrence of extreme heat events? Should extreme heat warnings be issued by T.V., newspaper or by radio? Even if warnings are issued will the population at large still change their behavior? Another possible question is whether people most vulnerable to extreme heat will change their behavior? A survey in 2010 by NASA will be the main basis for this analysis. This survey set out to see how well people in Phoenix, Philadelphia, and Dayton responded to extreme heat alerts by changing their behavior.