Image courtesy Journal of Hydrology

AMANAMBU, MOSSAGroundwater System and Climate Change: Present Status and Future Considerations

Amobichukwu C. Amanambu, Omon A. Obarein, Joann Mossa, Lanhai Li, Shamusideen S. Ayeni, Olalekan Balogun, Abiola Oyebamiji, Friday U. Ochege

Article first published online: 12 JUN 2020 Journal of Hydrology

DOI: 10.1016/j.jhydrol.2020.125163

ABSTRACT: Climate change will impact every aspect of biophysical systems and society. However, unlike other components of the climate system, the impact of climate change on the groundwater system has only recently received attention. This focus is due to the realization that groundwater is a vital freshwater resource crucial to global food and water security, and is essential in sustaining ecosystems and human adaptation to climate variability and change. This paper synthesizes findings on the direct and indirect impacts of climate change on the entire groundwater system and each component. Also, we appraise the use of coupled groundwater-climate and land surface models in groundwater hydrology as a means of improving existing knowledge of climate change-groundwater interaction, finding that most models anticipate decreases in groundwater recharge, storage and levels, particularly in the arid/semi-arid tropics. Reducing uncertainties in future climate projections and improving our understanding of the physical processes underlying models to improve their simulation of real-world conditions remain a priority for climate and earth scientists. Despite the enormous progress made, there are still few and inadequate local and regional aquifer studies, especially in less developed regions. The paper proposes two key considerations. First, physical basis: the need for a deeper grasp of complex physical processes and feedback mechanism with the use of more sophisticated models. Second, the need to understand the socioeconomic dimensions of climate-groundwater interaction through multidisciplinary synergy, leading to the development of better adaptation strategies and groundwater-climate change adaptation modelling.

Read the full publication at Journal of Hydrology.

Dr. Alexis (Lexi) White

Postdoctoral Researcher

Member, Quantitative Disease Ecology & Conservation Lab (QDEC Lab)

Researcher, Southeastern Center of Excellence in Vector Borne Diseases

Researcher, Emerging Pathogens Institute

alexis.white@ufl.edu

@lexiwhite27

Curriculum Vitae

Linked In

Google Scholar

Impact Story

Focus Area:

Research Statement: I am a mathematical ecologist interested in the disease ecology dynamics between wildlife and humans. Thus far, my research has focused on tick ecology and modeling of tick management strategies. In my current work, I explore the spatial ecology of vector-borne diseases

Adviser: Dr. Sadie Ryan

Educational Background:

  • PhD in Ecological Sciences, Old Dominion University, 2019
  • Graduate Certificate in Modeling and Simulation, Old Dominion University, 2015
  • B.S. in Wildlife Biology, Unity College, 2013​

Publications:

–top–

A young patient provides a blood sample to a study nurse in Machala, Ecuador. Un paciente joven provee una muestra de sangre a una enfermera del estudio en Machala, Ecuador. Image credit: Dany Krom

GAINESVILLE, FL – Helping patients with dengue can be challenging – especially in countries with multiple diseases spread by mosquitoes. Dengue, chikungunya, and Zika are viruses spread by the same type of mosquito; all three viruses are present in Ecuador and many other countries in Latin America and the Caribbean. Patients infected with one of these viruses tend to have similar, indistinct symptoms, which can make it difficult for a clinician to determine which disease is affecting the patient and which treatment steps to take. Most critically, the clinician must determine whether the patient should be checked into the hospital for close monitoring, or if the patient can be sent home to rest and recover. Failing to hospitalize a sick patient can be a deadly mistake, but sending too many patients to be hospitalized can overwhelm health systems and increase costs to the public health sector.

New clinical prediction research through a collaboration between the Quantitative Disease Ecology and Conservation (QDEC) Lab Group at the University of Florida and SUNY—Upstate Medical University tests the ability of machine learning to predict whether these patients should be hospitalized or not, using patient information collected by the clinician. The study, Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection was recently published in PLoS Neglected Tropical Diseases and was completed through collaborative efforts between students and researchers at the University of Florida, SUNY – Upstate Medical University, Cornell University, and the Ecuadorian Ministry of Public Health. Patient information and laboratory test results were collected from patients recruited into an ongoing arbovirus surveillance study as well as hundreds of paper medical records that were processed using machine learning.

Machine learning is a method wherein data are used to make predictions by applying a model or series of calculations (called algorithms) to the data. There are hundreds of machine learning algorithms – some might work really well or might fail entirely to make accurate predictions for a given dataset. “Our approach is unique in that we don’t assume that one algorithm is superior – we test and compare multiple algorithms to find the one that works best for our data” says Dr. Rachel Sippy, a postdoctoral researcher with QDEC and SUNY–Upstate Medical University and lead author of the paper. “Now that we have found an algorithm that works well with these types of patient information, we can test it on new groups of patients and confirm that it works under many circumstances.”

The published research lays the groundwork for the creation of a tool that clinicians could use at the bedside. The authors envision a mobile app where the clinician enters the patient data and receives a recommendation to hospitalize the patient or send them home. “While the experience of clinicians could never be replaced with an app, these kinds of decision-support tools can provide valuable additional information that can be taken into account by the clinician who is faced with making a potentially lifesaving decision,” explains Dr. Anna Stewart Ibarra, co-author of the publication.

Read Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection, in PLoS Neglected Tropical Diseases.

Predicciones de Aprendizaje Automático de Pacientes con Dengue Arrojan Resultados Prometedores

GAINESVILLE, FL – Ayudar a los pacientes con dengue puede ser un desafío – especialmente en países con múltiples enfermedades transmitidas por mosquitos. Dengue, chikungunya y zika son virus transmitidos por el mismo tipo de mosquito, los tres virus están presentes en el Ecuador y en muchos otros países de América Latina y el Caribe. Pacientes infectados con uno de estos virus tienden a tener síntomas similares, indistintos lo cual puede dificultar al médico a determinar cuál enfermedad está afectando al paciente y qué pasos de tratamiento debe tomar. Lo más crítico es que el médico debe determinar si el paciente debería ser ingresado en el hospital para un monitoreo más cercano o si el paciente puede ser enviado a casa para descansar y recuperarse. No hospitalizar a un paciente enfermo puede ser un error mortal, pero enviar muchos pacientes a ser hospitalizados puede abrumar los sistemas de salud e incrementar los costos al sector de salud pública.

Una nueva investigación de predicción clínica a través de una colaboración entre el Quantitative Disease Ecology and Conservation (QDEC) Lab Group de la Universidad de Florida y la Universidad Médica de SUNY Upstate, prueba la capacidad del aprendizaje automático para predecir si estos pacientes deberían ser hospitalizados o no, utilizando la información del paciente recogido por el médico. El estudio, Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection, se publicó recientemente en PLoS Neglected Tropical Diseases y se completó mediante esfuerzos de colaboración entre estudiantes e investigadores de la Universidad de Florida, SUNY – Upstate, Universidad de Cornell y el Ministerio de Salud Pública de Ecuador. La información del paciente y los resultados de las pruebas de laboratorio se obtuvieron de los pacientes reclutados en un estudio de vigilancia de arbovirus en curso, así como también de cientos de registros médicos en papel que se procesaron mediante el aprendizaje automático.

El aprendizaje automático es un método donde los datos son usados para hacer predicciones aplicando un modelo o series de cálculos (llamados algoritmos) a los datos. Existen cientos de algoritmos de aprendizaje automático – algunos pueden funcionar realmente o pueden fallar por completo para hacer predicciones precisas para un conjunto de datos dados. “Nuestro enfoque es único en el sentido de que nosotros no asumimos que un algoritmo es superior – probamos y comparamos múltiples algoritmos para encontrar el que mejor funcione para nuestros datos” dice Dra. Rachel Sippy, una investigadora posdoctoral con QDEC y SUNY-Upstate y autora principal de la publicación. “Ahora que hemos encontrado un algoritmo que funciona bien con este tipo de información del paciente, podemos probarlo en nuevos grupos de pacientes y confirmar que funciona bajo muchas circunstancias.”

La investigación publicada sienta las bases para la creación de una herramienta que los médicos podrían usar como cabecera. Los autores visualizan una aplicación en teléfono donde los médicos ingresen los datos de los pacientes y reciban una recomendación para hospitalizar al paciente o enviarlos a casa. “Mientras la experiencia de los médicos podría nunca ser reemplazada con la aplicación, este tipo de herramientas de apoyo a la toma de decisiones puede proporcionar información adicional valiosa que puede tener en cuenta el médico quien se enfrenta a tomar una decisión potencialmente vital”, explica la Dra. Anna Stewart Ibarra, coautora de la publicación.

Lee Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection, en PLoS Neglected Tropical Diseases.

Media contact: Mike Ryan Simonovich

GAINESVILLE, FL – Fighting mosquito-borne disease can be costly – in both health and financial resources. While mosquito control is an effective way to combat the spread of diseases like dengue fever, the cost of spraying every house in a city quickly adds up. These costs limit public health vector control programs, which often operate on limited annual budgets.

The team of UF researchers used methods from transportation geography to model the most efficient driving routes from Ministry of Health mosquito control centers (black squares) to neighborhoods throughout the city of Machala, Ecuador.
El equipo de investigadores de la Universidad de Florida usó métodos de transportación geográfica para hacer una maqueta de las rutas más eficientes del Ministerio de Salud Pública de los centros del control del zancudo (cuadrados negros) hacia vecindarios en la ciudad de Machala, Ecuador.

New geospatial science research from the Quantitative Disease Ecology and Conservation (QDEC) Lab Group at the University of Florida aims to improve the efficiency of mosquito control programs by borrowing methods from transportation geography to solve medical geography problems – optimizing routes using road network analysis. The study, “A network analysis framework to improve the delivery of mosquito abatement services in Machala, Ecuador”, recently published in the International Journal of Health Geographics, was conducted in the city of Machala, Ecuador in collaboration with the Ecuadorian Ministry of Health. Locations with potentially higher risk of mosquito-borne disease activity were identified, and the researchers created models of the best delivery routes from ministry-operated mosquito control facilities and every neighborhood in Machala.

In addition to identifying the most efficient driving routes, the team also determined which control facility locations were ideal under different management priorities. Public health workers typically spray for mosquitoes in response to incoming disease cases reported to the health department. Instead, some neighborhoods could be targeted ahead of outbreaks based on environmental risk factors. Moving control facilities closer to at-risk neighborhoods translates into monetary savings. “Health ministry priorities and strategies may change over time,” says Cat Lippi, a PhD student in QDEC and lead author on the paper. “This work provides a new way for health officials to compare the delivery cost of different management strategies when developing their control programs.”

These models can be used to improve public vector control services and reduce operating costs. “The results of this study have implications beyond Ecuador – and beyond dengue,” says senior author Dr. Sadie Ryan, the principal investigator for QDEC. “The framework we present can be used by any agency that plans for intervention through mosquito control operators.”

Read A network analysis framework to improve the delivery of mosquito abatement services in Machala, Ecuador, at the International Journal of Health Geographics.

Nuevo Enfoque a través de la Red para la Distribución del Control del Zancudo rinde ideas en una Ciudad del Sur del Ecuador

GAINESVILLE, FL – Pelear contra enfermedades causadas por el zancudo puede ser costoso. Aún cuando el control del zancudo es una manera efectiva de combatir la propagación de enfermedades como la fiebre del dengue, el precio de fumigar cada casa en una ciudad aumenta las cifras de costo rápidamente. Estos costos limitan programas del control del vector para la salud pública, el mismo que opera con presupuestos muy limitados.

Investigaciones nuevas de ciencias geoespaciales de el Quantitative Disease Ecology and Conservation (QDEC) Lab Group de la Universidad de Florida tienen como objetivo mejorar la eficiencia de programas para el control del zancudo al métodos de la transportación geográfica para resolver problemas médicos basados en la geografía, optimizando rutas de análisis de la red. El estudio “A network analysis framework to improve the delivery of mosquito abatement services in Machala, Ecuador”, recientemente publicado en la Revista Internacional de la Geografía de la Salud, se llevó a cabo en la ciudad de Machala, Ecuador en colaboración con el Ministerio de Salud Pública del Ecuador. Se identificaron lugares con más potenciales de actividad de riesgo para la transmición de enfermedades causadas por zancudos, y los investigadores crearon modelos de las rutas de más eficacia, manejados por el ministerio, en el entrego de los servicios para el control del zancudo y todos los vecindarios de Machala

A más de identificar las rutas de entrega más eficientes, el equipo también determinó cuáles ubicaciones de servicio de control fueron las más ideales bajo diferentes prioridades de adminis

tración. Los trabajadores de salud pública típicamente fumigan para zancudos respondiendo a casos de enfermedades reportados al departamento de salud. En vez, algunos vecindarios podrían ser asistidos antes de un brote basado en riesgos del ambiente. El mover servicios de control más cerca a los vecindarios en riesgo se traduce como ahorros monetarios. “Las prioridades y estrategias del ministerio de salud pueden cambiar al pasar del tiempo,” dice Cat Lippi, estudiante de PhD en QDEC y autora que encabeza este estudio. “Este trabajo de investigación provee una nueva pauta para dirigentes de la salud para comparar el costo de entrega de diferentes estrategias de administración cuando desarrollan sus programas de control.”

Estos modelos pueden ser usados para mejorar servicios de control del vector y reducir costos de operación. “Los resultados de este estudio tienen implicaciones más allá del Ecuador – y más allá del dengue,” dice la autora mayor Dra. Sadie Ryan, la investigadora principal de QDEC. “El armazón que presentamos puede ser utilizado por cualquier agencia que planea la intervención a través de operadores de control del zancudo.”

Lee A network analysis framework to improve the delivery of mosquito abatement services in Machala, Ecuador, en International Journal of Health Geographics.

Media contact: Mike Ryan Simonovich

Image courtesy Journal of Infrastructure Systems

HUImpact of Coastal Hazards on Residents’ Spatial Accessibility to Health Services

Georgios P. Balomenos, Yujie Hu, Jamie E. Padgett, and Kyle Shelton

Article first published online: 1 DEC 2019 Journal of Infrastructure Systems

DOI: 10.1061/(ASCE)IS.1943-555X.0000509

ABSTRACT: The mobility of residents and their access to essential services can be highly affected by transportation network closures that occur during and after coastal hazard events. Few studies have used geographic information systems coupled with infrastructure vulnerability models to explore how spatial accessibility to goods and services shifts after a hurricane. Models that explore spatial accessibility to health services are particularly lacking. This study provides a framework to examine how the disruption of transportation networks during and after a hurricane can impact a resident’s ability to access health services over time. Two different bridge-closure conditions—inundation and structural failure—along with roadway inundation are used to quantify posthurricane accessibility at short- and long-term temporal scales. Inundation may close a bridge for hours or days, but a structural failure may close a route for weeks or months. Both forms of closure are incorporated using probabilistic vulnerability models coupled with GIS-based models to assess spatial accessibility in the aftermath of a coastal hazard. Harris County, an area in southeastern Texas prone to coastal hazards, is used as a case study. The results indicate changes in the accessibility scores of specific areas depending on the temporal scale of interest and intensity of the hazard scenario. Sociodemographic indicators are also examined for the study region, revealing the populations most likely to suffer from lack of accessibility. Overall, the presented framework helps to understand how both short-term functionality loss and long-term damage affect access to critical services such as healthcare after a hazard. This information, in turn, can shape decisions about future mitigation and planning efforts, and the presented framework can be expanded to other hazard-prone areas.

Read the full publication at Journal of Infrastructure Systems

Dr. Yujie Hu

Assistant Professor

Affiliate Faculty, UF Informatics Institute

Affiliate Faculty,  UF Transportation Institute

yujiehu@ufl.edu

@TheDrWho

Personal Webpage

(352) 294-7514

Curriculum Vitae

Focus Areas

Research Statement:

I am a geographer with research and teaching interests in urban transportation, human mobility, and accessibility. My current research focuses on three main areas: 1) relationships between people’s mobility within cities—including commuting, healthcare-seeking, and crime—and the urban built environment, 2) accessibility to opportunities, such as jobs, healthcare, food, and transportation infrastructure, and how it is affected by natural hazards, and, 3) network flow analysis and optimization of travel patterns related to commuting, bike sharing, healthcare, and food delivery.

My main research approach is the development and application of GIS, spatial analysis, and network analysis techniques to reveal patterns of individual and group behaviors from big geospatial data associated with point patterns (traffic crashes, crime incidents) and networks (movement trajectory such as taxi cab GPS trajectory, smart card transaction data, and origin-destination flow such as commuting, bike sharing usage, and inpatient discharge). The goal is to convert data into knowledge to inform and evaluate place-based policies focused on transportation, land use, public health, and community safety.

Recent Courses

  • GEO 3602 Urban/Business Geography
  • GIS 4113 Introduction to Spatial Networks
  • GIS 6104 Spatial Networks

Recent Funded Projects

  • 2019-2020

Automated Delineation of Hospital Service Areas and Hospital Referral Regions, ORAU Ralph E. Powe Junior Faculty Enhancement Awards, $10,000.

  • 2019-2020

Incorporating mixed automated vehicle traffic in capacity analysis and system planning decisions, the Center for Transportation Equity, Decisions and Dollars (a USDOT Tier-1 University Transportation Center) SEED Grant, $35,000.

Recent Publications

Hu, Y. and Wang, F. (2019). GIS-Based Simulation and Analysis of Intra-Urban Commuting. Boca Raton, FL: CRC Press.

Hu, Y. and Downs, J. (2019). Measuring and Visualizing Space-Time Job Accessibility. Journal of Transport Geography 74: 278-288. DOI: 10.1016/j.jtrangeo.2018.12.002

Hu, Y., Zhang, Y., Lamb, D., Zhang, M., and Jia, P. (2019). Examining and optimizing the BCycle bike-sharing system–A pilot study in Colorado, US. Applied Energy, 247: 1-12. DOI: 10.1016/j.apenergy.2019.04.007

Hu, Y., Zhang, Y., and Shelton, K. S. (2018). Where are the Dangerous Intersections for Pedestrians and Cyclists: A Colocation-Based Approach. Transportation Research Part C: Emerging Technologies 95: 431-441. DOI: 10.1016/j.trc.2018.07.030

Hu, Y., Wang, F., Guin, C., and Zhu, H. (2018). A Spatio-Temporal Kernel Density Estimation Framework for Predictive Crime Hotspot Mapping and Evaluation. Applied Geography 99: 89-97. DOI: 10.1016/j.apgeog.2018.08.001

Hu, Y., Wang, F., and Xierali, I. (2018). Automated Delineation of Hospital Service Areas and Hospital Referral Regions by Modularity Optimization. Health Services Research 53(1): 236-255. DOI: 10.1111/1475-6773.12616

Wang, F., Hu, Y., Wang, S., and Li, X. (2017). Local Indicator of Colocation Quotient with a Statistical Significance Test: Examining Spatial Association of Crime and Facilities. The Professional Geographer 69(1): 22-31. DOI: 10.1080/00330124.2016.1157498

Hu, Y. and Wang, F. (2016). Temporal Trends of Intraurban Commuting in Baton Rouge 1990-2010. Annals of the American Association of Geographers 106(2): 470-479. DOI: 10.1080/00045608.2015.1113117

Hu, Y. and Wang, F. (2015). Decomposing Excess Commuting: A Monte Carlo Simulation Approach. Journal of Transport Geography 44: 43-52. DOI: 10.1016/j.jtrangeo.2015.03.002

Hu, Y. and Wang, F. (2015). Monte Carlo Method and Application in Urban Traffic Simulation. in Quantitative Methods and Socioeconomic Applications in GIS (ed. Wang, F.) Boca Raton, FL: Taylor & Francis.

Ikram, S. Z., Hu, Y., and Wang, F. (2015). Disparities in spatial accessibility of pharmacies in Baton Rouge, Louisiana. Geographical Review, 105(4): 492-510. DOI: 10.1111/j.1931-0846.2015.12087.x

Hu, Y., Miller, H. J., and Li, X. (2014). Detecting and Analyzing Mobility Hotspots using Surface Networks. Transactions in GIS 18(6): 911-935. DOI: 10.1111/tgis.12076

Educational Background

  • PhD in Geography, Louisiana State University, 2016
  • M.S. in Cartography and GIS, East China Normal University, 2012
  • B.S. in GIS, North China University of Water Resources and Electric Power, 2009

Current Graduate Students

Currently accepting applications for graduate students to begin in Fall 2020.

–top–

Dr. Rachel Sippy

Postdoctoral Researcher

rsippy@ufl.edu

@GermCurves

https://sippy.pophealth.wisc.edu/

Google Scholar

(319) 551-1000

Curriculum Vitae

Focus Areas:

Research Statement:

I am interested in epidemiology and disease seasonality in a causal framework as well as predictions of disease outcomes or epidemics. I primarily focus on vectorborne illness but also study environmental exposures. For my work I like to use statistical models, spatial analyses, machine learning, and compartmental models.

Adviser: Dr. Sadie Ryan

Educational Background:

  • PhD in Epidemiology, University of Wisconsin-Madison, 2018
  • M.P.H., University of Utah, 2013
  • B.S. in Genetics, Iowa State University, 2007

Recent Publications

Sippy R, Moreira F. Aedes albopictus en América del Sur y su relación con la distribución, y mantenimiento de enfermedades. Práctica Familiar Rural. (2016)

Oh CS, Sippy J, Charbonneau B, Hutchinson JC, Romero OE, Barton M, Patel P, Sippy R, Feiss M. DNA Topology and the Initiation of Virus DNA Packaging. PLoS One. (2016) DOI:10.1371/journal.pone.0154785

Sippy R, Kolesar JE, Darst BF, Engelman CD. Prioritization of Family Member Sequencing for the Detection of Rare Variants. BMC Proceedings. (2016) DOI:10.1186/s12919-016-0035-8

Sippy J, Patel P, Vahanian N, Sippy R, Feiss M. Genetics of critical contacts and clashes in the DNA packaging specificities of bacteriophages λ and 21. Virology. (2015) doi:10.1016/j.virol.2014.11.028

Wu Z, Sippy R, Sahin O, Plummer P, Vidal A, Newell D, Zhang Q. Genetic diversity and antimicrobial susceptibility of Campylobacter jejuni isolates associated with sheep abortion in the United States and the United Kingdom. J Clin Microbiol. (2014) DOI:10.1128/JCM.00355-14

Sippy R, Sandoval-Green CMJ, Sahin O, Plummer P, Fairbanks WS, Zhang Q, Blanchong JA. Occurrence and molecular analysis of Campylobacter in wildlife on livestock farms. Veterinary Microbiology. (2012) DOI:10.1016/j.vetmic.2011.12.026

Luo Y, Sahin O, Dai L, Sippy R, Wu Z, Zhang Q. Development of a loop-mediated isothermal amplification assay for rapid, sensitive and specific detection of a Campylobacter jejuni clone. Journal of Veterinary Medical Science. (2012)

Sahin O, Fitzgerald C, Stroika S, Zhao S, Sippy R, Kwan P, Plummer P, Han J, Yaeger M, Zhang Q. Molecular Evidence for Zoonotic Transmission of an Emergent Highly Pathogenic Campylobacter jejuni Clone in the United States. Journal of Clinical Microbiology. (2012) DOI:10.1128/JCM.06167-11

Plummer P, Sahin O, Burrough E, Sippy R, Mou K, Rabenold J, Yaeger M, Zhang Q. LuxS in the fitness and virulence of Campylobacter jejuni. Infection & Immunity. (2012) DOI:10.1128/IAI.05766-11

Dr. Kevin Ash

Assistant Professor

kash78@ufl.edu

@scatterazure

ResearchGate

Google Scholar

352-294-6956

Affiliate Member, Florida Climate Institute

Focus Areas

Research Statement:

I am a human-environment geographer with expertise in environmental hazards topics such as vulnerability, resilience, risk communication and perception, evacuation, and disaster loss data.  My primary analysis tools are geospatial and statistical modeling, though I also have training and experience with qualitative methods. Currently, my research is primarily focused on social vulnerability, risk communication, risk perception, and decision-making for weather and climate related hazards in the southeastern USA. I look forward to expanding my research into new places and hazard types through collaborations at UF and beyond.

Recent Courses

  • GEO 4938/6938 GIS Analysis of Hazard Vulnerability, Fall 2018

  • GEO 4938/6938 Geographic Perspectives on Hazards, Spring 2019

Recent Funded Projects

  • National Oceanic and Atmospheric Administration. Verification of the Origin of Rotation  of Tornadoes Experiment, Southeast US (VORTEX-SE), Role: Consultant with PI  Stephen Strader (Villanova Univ.), 2017-2019, Title: Tornadoes and Mobile  Homes: An Inter-science Approach to Reducing Vulnerabilities and Improving  Capacities for the Southeast’s Most Susceptible Population.
  • National Science Foundation Doctoral Dissertation Research Improvement, Geography  and Spatial Sciences, Role: Co-PI with PI Susan L. Cutter (Dept. of Geography,  Univ. of South Carolina); 2013-2015, Title: Beliefs, Attitudes, and Intended  Tornado Sheltering Strategies of Mobile Home Residents.

Recent Publications

Saunders, M.E., K.D. Ash, and J.M. Collins, 2018. Usefulness of the United States  National Weather Service Radar Display as Rated by Website Users, Weather,  Climate, and Society, 10(4): 673-691.

Schumann III, R.L., K.D. Ash, and G.C. Bowser, 2018. Tornado Warning Perception and  Response: Integrating the Roles of Visual Design, Demographics, and Hazard  Experience, Risk Analysis, 38(2): 311-332.

Ash, K.D., 2017. A Qualitative Study of Mobile Home Resident Perspectives on    Tornadoes and Tornado Protective Actions in South Carolina, USA, GeoJournal,  82(3): 533-552.

Cutter, S.L., K.D. Ash, and C.T. Emrich, 2016. Urban-Rural Differences in Disaster  Resilience, Annals of the American Association of Geographers, 106(6): 1236- 1252.

Cutter, S.L., K.D. Ash, and C.T. Emrich, 2014. The Geographies of Community Disaster  Resilience. Global Environmental Change, 29:65-77.

Ash, K.D., R.L. Schumann III, and G.C. Bowser, 2014. Tornado Warning Trade-offs:  Evaluating Choices for Visually Communicating Risk. Weather, Climate, and  Society, 6(1):104-118.

Ash, K.D., S.L. Cutter, and C.T. Emrich, 2013. Acceptable Losses? The Relative Impacts  of Natural Hazards in the United States, 1980-2009. International Journal of  Disaster Risk Reduction, 5:61-72.

Educational Background

  • Postdoctoral Fellow, Advanced Study Program, National Center for Atmospheric Research, 2017-2018
  • PhD in Geography, University of South Carolina, 2015
  • M.S. in Geography, University of Florida, 2010
  • B.S. in Geography, University of Oklahoma, 2004

Current Graduate Students

Masters

  • Christopher Williams

–top–

Description

The Department of Geography at the University of Florida, College of Liberal Arts and Sciences, and the UF Informatics Institute (UFII), invite applications for a full-time, nine-month, tenure-accruing position, at the level of Assistant Professor to begin August 16, 2019. The department seeks an outstanding candidate in Spatial Networks with expertise in Geographic Information Systems (GIS) and Geospatial Analysis, who will complement existing strengths in the department and across campus. The department seeks dynamic, highly innovative candidates and possible areas of focus include, but are not limited to, transmission and disease spread, transportation and mobility networks, social networks, complex networks, or big data. Specific interest would be in applying complex networks to real world problems and data. The candidate will make a substantial contribution to a research program in their own area of expertise within the field of networks. This program should link with the Informatics Institute, and the department’s long-term strategic goal in growing the field of Geographical Information Science (GISc). The candidate should also strengthen one or more of the department’s other focus areas: Medical Geography in Global Health, Earth System Science, and Global Environmental and Social Change.

Primary responsibilities include high-quality research in Spatial Networks, and a 2-2 teaching assignment in the Department of Geography, particularly developing introductory and advanced courses at both undergraduate and graduate levels. In addition, the candidate will be a member of UFII, whose mission is to develop and nurture integrative informatics research and education studies at the University of Florida. This institute brings together preeminent researchers that explore contemporary application areas across the university (e.g., those arising in science and medicine, the humanities, social sciences, and engineering), with UF experts developing the tools and technologies that support and complement these studies. Collaborative efforts nurtured by the UFII will yield insights into complex physical, natural, social, and engineered systems, aid decision-makers in diagnosing and treating diseases, and power the next generation of technologies that will position UF to meet emerging challenges in the coming decades. Applicants should have a strong record of research and scholarly activities within the area of Spatial Networks; experience and demonstrated commitment to excellence in teaching; and a proven ability to communicate effectively with students, professionals, and the general public. Additionally, potential to engage with the University of Florida’s ‘Beyond 120’ and ‘Quest’ Programs would be considered an advantage and should be addressed in the application materials, as well as links with existing units on campus – such as International Studies, Sustainability Studies, African-American Studies etc. if applicable.

The Department of Geography has 17 active faculty members, who conduct teaching and research in a broad spectrum across our four focus areas, which are Geospatial Analysis & Techniques, Medical Geography in Global Health, Earth System Science, and Global Environmental & Social Change. It offers BS/BA, MS/MA, and PhD degree tracks, including online and distance learning options for coursework. The department has two computer-lab classrooms and provides a high-speed on-line computing environment via UFAPPs, where any student and faculty can access software applications, such as ArcGIS, GeoDa, ENVI, ERDAS, MATLAB, and SPSS, from any computing device, from any location, at any time. High Performance Computing facilities are available on campus. Applicants are encouraged to visit the website to learn more about the Department of Geography. The vision of the UFII is to provide support for UF Faculty to collaborate toward the creation and application of information processing and decision-making systems, which are driven by the greatest challenges facing society today, leverage next-generation computing technology and analytical methods, and provide insights that position UF as a leader in ongoing research endeavors.

The Department particularly welcomes applicants who can contribute to a diverse and inclusive environment through their scholarship, teaching, mentoring, and professional service. The university and greater Gainesville communities enjoy a diversity of cultural events, restaurants, year-round outdoor recreational activities, and social opportunities. The University of Florida is an Equal Opportunity Institution.

Qualifications

The successful candidate should possess a doctoral degree in Geography, Computer Science, Information Science or a related field prior to August 15, 2019. Candidates must use geospatial techniques in their research – such as GIS, RS and/or spatial modeling – with a substantive focus on networks.

Application Instructions

For full consideration, applications must be submitted online at: http://apply.interfolio.com/56504 and must include: (1) a letter summarizing the applicant’s qualifications, ongoing research directions, and interests; (2) a complete curriculum vitae; (3) teaching/

research statement that discusses qualifications to teach courses in the stated area of expertise, and a discussion of what these courses might be; (4) a diversity statement that addresses past and/or potential contributions to diversity through teaching, research, and service; and (5) three confidential letters of recommendation. Review of applications will begin October 30, 2018 and will continue until the position is filled.

Inquiries about the position should be directed to the Search Committee Chair, Dr. Liang Mao, at liangmao@ufl.edu.

All candidates for employment are subject to a pre-employment screening which includes a review of criminal records, reference checks, and verification of education.

The selected candidate will be required to provide an official transcript to the hiring department upon hire. A transcript will not be considered “official” if a designation of “Issued to Student” is visible. Degrees earned from an educational institution outside of the United States require evaluation by a professional credentialing service provider approved by the National Association of Credential Evaluation Services (NACES), which can be found at http://www.naces.org/.

The University of Florida is an equal opportunity institution dedicated to building a broadly diverse and inclusive faculty and staff. The University of Florida invites all qualified applicants, including minorities, women, veterans and individual with disabilities to apply. The University of Florida is a public institution and subject to all requirements under Florida Sunshine and Public Records laws. Searches are conducted in accordance with Florida’s Sunshine Law. If an accommodation due to disability is needed to apply for this position, please call (352) 392-2477 or the Florida Relay System at (800) 955-8771 (TDD)