College Of Science Engineering Health: GIS at UWF

Transcription

1 College Of Science Engineering Health: GIS at UWF GIS here on campus GeoData Center, Nathan McKinney + student lab assistants GIS Certificate, Dr. Zhiyong Hu, GA s helping with courses GIS Online Director, Coordinator, Outreach Coordinator, 3 GA s, 5 adjuncts GIS Certificate Program 40 students GIS Training Academy Courses for CEU 8 students PSM GIS Masters 1, current, 8 students accepted for spring MOOC Intro to GIS in dev. expecting +/- 500 students Certificate in Geospatial Computing New!

2 College Of Science Engineering Health What is COSEH? Biology, Chemistry, Computer Science, Electrical & Computer Engineering, Environmental Studies, Exercise Science & Community Health, Mathematics & Statistics, Nursing, Physics, Psychology, Allied Health & Life Sciences, and Environmental Diagnostics & Bioremediation. About you: What department are you from? Used GIS?

3 What is GIS? A geographic information system (GIS) is a computer-based tool for mapping and analyzing things that exist and events that happen on earth. GIS technology integrates common database operations such as query and statistical analysis with the unique visualization and geographic analysis benefits offered by maps. ~ ESRI Everything exists or happens at a location Mapping software allows us to organize and model events and things as themed layers Ask questions about relationships through query and spatial analysis Visual outputs, compelling display through maps

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5 Relevance Discussion COSEH Applications COSEH Student Examples COSEH Jobs Internships Raffle

6 GIS Electric Outages a43e86b1d3118a67&_ga=

7 Mapping Grey Nurse Shark Habitat, Australia ews/arcnews/winter03 04articles/volunteerdivers.html

8 Planetary GIS

9 GIS Applications for COSEH Biology Chemistry Electrical and Computer Engineering Community Health Mathematics and Statistics Nursing Physics Psycology Allied Health and Life Services Research tool ection/wildareas-v2/maps/services Environmental Chemistry - Any analysis with a geographic data element... northwest_gis.htm html?appid=f a43e86b1d31 Electrical utility companies use GIS to understand generation, transmission and distribution, predicting outages 18a67&_ga= and asset damages, complying with regulations and communicating with customers. 343 Manage pandemics, prevent chronic diseases, monitor environmental quality to transform health of a community. Allocate resources, enact policies to help issues, communicate public health issues, manage disasters uwf-students-turn-quality-life-datadetectives/ / Spatial statistics identify community health service needs, streamline facilities, asset management, tracking patients, managing bed use, lower potential for infection examine gravity data, examine aeromagnetic data, collect solar angles and shadow data, examine remotely sensed imagery and the reflectivity of different features in different spectral bands, hurricane movements and the coriolis effect, seismic waves, earthquake depth and magnitude relative to plate boundaries and volcanoes, 3D mapping of the solar system, mapping of lightning data (polarity and amplitude) for a given storm, hydrology (e.g. impact on a stream from decreased pervious surface in a watershed), earth shape (imperfect sphere leading to datum issues) how people perceive and represent spaces around them and how they use perceptual information to guide their movements in those spaces, use GPS to analyze planning and spatial memory abilities understand the links between social service infrastructure and challenges such as aging, homelessness, and poverty. Streamline processes to better serve the community. Some offices even use GIS to help identify potential fraud and abuse of benefits Emergency Management Flood Maps, Evacuation Zones egories/anamarketplace/anaperiodicals/o JIN/Columns/Informatics/Integrating-a- Geographic-Information-System-into- Research.html ap/viewer.html?webmap=cbe088e7c aa0fc34eb99e7f30

10 COSEH Student Examples Natural Hazards The goal was to use 2011 USGS 30M Digital Elevation Models (topography) and the Hydrology Tools found within ESRI s Spatial Analyst Extension to determine drainage flow in a potential crisis situation US Census Data to create a population analysis for the areas in proximity to the drainage areas. These findings will aid local and state officials in hazard planning and response time. Disaster Relief The goal was to map the evacuation zones around the Fukushima Daiichi Nuclear Power Plant and determine the at-risk population within each of those zones. Evacuation & Emergency Management Students were presented with a disaster scenario in which a major storm surge flooded into Tampa Bay, students were responsible for routing the relief workers and refugees to the nearest shelter.

11 COSEH Jobs Health Masters ~$50,000 Bureau of HIV/AIDS Epidemiology, Albany, NY Contribute to formative research, and prepare maps to display project data using ArcGIS Health BS City of Maricopa, Phoenix, AZ Design, develop, establish, implement, and analyze complex real-time disease surveillance systems and electronic data exchange to prepare for and detect disease during mass gatherings Engineering Bachelors HGA Pipeline Services Perform research and Analysis, construct maps, analyze and geographically assess customer requests Engineering Bachelors West Burchell Gather, Create, Manage, and Analyze spatial datasets related to the natural gas industry Biology PhD University of Tampa Spatial Ecologist: Blend traditional biology, biostatistics, and GIS in classroom environment

12 Internships Current Internships: Past Internships Adam Taylor, FDEP Tampa Bay Aquatic Preserves Phil Coppola, Western Washington University Talia Smith, NASA Andrea Miller-Hinz, The Nature Conservancy Jelly Riedel, NASA

13 Dr. Raid Amin Professor, UWF Mathematics and Statistics Research Interests: Cluster Analysis in Environmental and Demographic Studies, Public Health, Statistical Quality Control & Statistical Computing

14 APPLICATIONS IN SPATIAL EPIDEMIOLOGY Dr. Raid W. Amin Department of Mathematics and Statistics

15 OUTLINE SaTScan: A disease surveillance software Cancer Rates in Florida Particulate Matter PM2.5 Quality of Life Index for US Counties Homicide-Suicide in Florida Shark Attacks High School Performance in Florida

16 SaTScan: A disease surveillance software Spatial analysis for this study was done using SaTScan, developed by Dr. Martin Kulldorff (Harvard University). The model maximizes the likelihood function over circles of varying sizes. The circle with the highest maximum log likelihood ratio is least likely to have occurred by chance. The p-value for the cluster is obtained using Monte Carlo replications. Monte Carlo hypothesis testing uses the data set to create random sets of the data, performs the same analysis, and compares the maximum likelihood ratio of the real set with those of the random data sets.

17 For practical reasons the centroids of counties or zip codes are used to position the scanning windows. The size of the windows is varied continuously from zero size to maximum size (that the window never contains more than 50 percent of the total population). Therefore, the window is not fixed in position and size. The outcomes are several (a huge amount) windows spread over the map. Each could contain a cancer case cluster.

18 The standard purely spatial scan statistic imposes a circular window on the map. The window is in turn centered on each of several possible grid points positioned throughout the study region. For each grid point, the radius of the window varies continuously in size from zero to some upper limit specified by the user. In this way, the circular window is flexible both in location and size. In total, the method creates an infinite number of distinct geographical circles with different sets of neighboring data locations within them. Each circle is a possible candidate cluster. The space-time scan statistic may be used for either a single retrospective analysis, using historic data, or for time-periodic prospective surveillance, where the analysis is repeated for example every day, week, month or year. Ref:

19 SaTScan o Purely Spatial Analysis o Space-time Analysis

20 Probability Model: Poisson Model The Poisson likelihood function for each window is calculated by the following formula. Here, N = total number of cancer cases over the whole area and n = number of cancer cases within the window = expected number of cancer cases within the window (under the null hypothesis) n n N n N N n

21 THE NORMAL MODEL The normal model10 is designed for continuous data. For each individual or for each observation, called a case, there is a single continuous attribute that may be either negative or positive. The model can also be used for ordinal data when there are many categories. That is, different cases are allowed to have the same attribute value. Example: For the normal model, the data may consist of the birth weight and residential census tract for all newborns, with an interest in finding clusters with lower birth weight. One individual is then a case. Alternatively, the data may consist of the average birth weight in each census tract. It is then the census tract that is the case, and it is important to use the weighted normal model, since each average will have a different variance due to a different number of births in each tract. It is important to note that while the normal model uses a likelihood function based on the normal distribution, the true distribution of the continuous attribute must not be normal. The statistical inference (p-value) is valid for any continuous distribution. The reason for this is that the randomization is not done by generating simulated data from the normal distribution, but rather, by permuting the space-time locations and the continuous attribute (e.g. birth weight) of the observations. While still being formally valid, the results can be greatly influenced by extreme outliers, so it may be wise to truncate such observations before doing the analysis.

22 A scan statistic for continuous data based on the normal probability Model The circular spatial scan statistic is defined through a large number of overlapping circles. For each circle z, a log likelihood ratio LLR(z) is calculated, and the test statistic is defined as the maximum LLR over all circles. The statistical significance of the most likely cluster is evaluated using Monte Carlo hypothesis testing. Rather than generating random data from the normal distribution, a large set of random data sets are created by randomly permuting the observed values xi and their corresponding locations s. Martin Kulldorff, Lan Huang, and Kevin Konty. Ref:

23 Testing Procedure The window with the maximum likelihood is determined (labeled as most likely cluster). Repeating this procedure for random replications of the dataset by a Monte Carlo simulation leads to the p-value for the test. If the null hypothesis could be rejected (p-value below defined alpha-level) the certain cluster can be stated as significant.

24 Special Issue in Statistics and Public Policy Letter from the Editor (David Banks) Background on the Florida Pediatric Cancer Cluster Studies (Raid Amin) 1. Wombling Analysis of Childhood Tumor Rates in Florida (Matthew Heaton) 2. A Cluster Analysis of Pediatric Cancer Incidence Rates in Florida: (Amin, Hendryx, Shull, Bohnert) 3.Identifying Pediatric Cancer Clusters in Florida Using Log-Linear Models and Generalized Lasso Penalties (Hao Wang and Abel Rodriguez) 4. Childhood Brain Cancer in Florida: A Bayesian Clustering Approach (Andrew Lawson and Chawarat Rotejanaprasert)

25 Cluster Analysis Output: Poisson on Brain Tumors Purely Spatial analysis: Brain Tumors (adjusted for age and sex) scanning for clusters with high rates using the Discrete Poisson model. SUMMARY OF DATA Study period...: 2000/1/1 to 2010/12/31 Number of locations...: 983 Total population...: Total number of cases...: 1194 Annual cases / : 2.5 MOST LIKELY CLUSTER 1.Location IDs included.: Coordinates / radius..: ( N, W) / km Population...: Number of cases...: 409 Expected cases...: Annual cases / : 3.0 Observed / expected...: 1.22 Relative risk...: 1.34 Log likelihood ratio..: P-value...: 0.012

26 Poisson on Brain ( ) Purely Spatial Adjusted for age and sex

27 Age & Sex Adjusted Rates per 100,000 Age and Sex Adjusted Brain Tumor Rates in Florida (out of 100,000) Most Significant Cluster Outside Significant Cluster All of Florida

28 SaTScan Output: Leukemia Purely Spatial analysis (Leukemia) scanning for clusters with high rates using the Discrete Poisson model. SUMMARY OF DATA Study period...: 2000/1/1 to 2010/12/31 Number of locations...: 983 Total population...: Total number of cases...: 1833 Annual cases / : 3.8 MOST LIKELY CLUSTER 1.Location IDs included.: Coordinates / radius..: ( N, W) / km Population...: Number of cases...: 279 Expected cases...: Annual cases / : 4.9 Observed / expected...: 1.30 Relative risk...: 1.35 Log likelihood ratio..: P-value...: 0.029

29 Poisson on Leukemia ( ) Adjusted for age and sex

30 Age & Sex Adjusted Rates per 100,000 Age and Sex Adjusted Leukemia Rates in Florida (out of 100,000) Most Significant Cluster Outside Significant Cluster All of Florida

31 SaTScan Output: Lymphoma Purely Spatial analysis: Lymphoma scanning for clusters with high rates using the Discrete Poisson model. SUMMARY OF DATA Study period...: 2000/1/1 to 2010/12/31 Number of locations...: 983 Total population...: Total number of cases...: 745 Annual cases / : 1.5 MOST LIKELY CLUSTER 1.Location IDs included.: Coordinates / radius..: ( N, W) / km Population...: Number of cases...: 160 Expected cases...: Annual cases / : 2.2 Observed / expected...: 1.41 Relative risk...: 1.52 Log likelihood ratio..: P-value...: 0.024

32 Poisson on Lymphoma ( ) Adjusted for age and sex

33 Age & Sex Adjusted Rates per 100,000 Age and Sex Adjusted Lymphoma Rates in Florida (out of 100,000) Most Significant Cluster Outside Significant Cluster All of Florida

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35 Table 1. SaTScan TM Output AYA thyroid cancer purely spatial analysis adjusted for age, sex and race. State Primary cluster Secondary cluster AYA Population 5,358,013 1,695,088 58,637 Number of Cases 3,526 1, Annual cases per ,000 Relative risk Log Likelihood ratio P-value <

36 . Increasing overall incidence of thyroid cancer in AYA

37 RSEI Air (1 st and 2 nd Round Clusters) and Lung Cancer

38 Age-Adjusted FAPTP Brain and Stream Condition Index 2007

39 Particulate Matter PM2.5 for 2010

40 Quality of Life Index for 2010

41 MURDER CLUSTERS: CLUSTER ANALYSIS. RED CLUSTERS IDENTIFY HIGH MURDER RATES. BLUE CLUSTERS INDENTIFY LOW MURDER RATES.

42 Rape Clusters: Red clusters show high rates of rape cases. Blue clusters show low rates.

43 Crime Index: Murder, Rape, Assault, Robbery, Drugs. Blue cluster are LOW ( good ) and red clusters are HIGH ( bad )

44 Drugs

45 AVERAGE HOMICIDE- SUICIDE RATE PER YEAR (PER 100,000)

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48 FACTOR 1: HIGH SAT/ACT SCORES, HIGH STABILITY RATE, LOW FREE AND REDUCED LUNCH RATE, LOW MINORITY RATE (BY MEGAN PODZIAD)

49 13 high schools, in Brevard County, shown in overlap of high rate clusters OVERLAPPING FACTORS 1 AND 2:

50 Thanks for attending. Before you go, It s raffle time!!!