Category Archives: Geospatial Student Spotlight

GeoSpatial Student Spotlight: Collin O’Connor

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PhD Candidate/Geography                               June 2025 (Anticipated)
Department of Geography
University at Buffalo                                                                                Buffalo, NY

M.S. Epidemiology                                             May 2020
School of Public Health
Department of Epidemiology and Biostatistics
University of Albany                                                                                 Albany, NY

B.S. Biological Sciences                                    May 2018
Department of Biological Sciences
University at Albany                                                                                 Albany, NY

Research Focus

O’Connor’s doctoral research focuses on the spatial ecology and epidemiology of tick borne diseases in New York State. In particular, his dissertation work examines two genetic variants of Anaplasma phagocytophilum, the pathogen that causes anaplasmosis. Anaplasmosis is the second most frequent tick borne disease in New York State behind Lyme disease.  An August 2021 article in Medscape reported Anaplasma cases nearly quadrupled statewide from 2010 to 2018.  On a larger scale, Anaplasmosis became a nationally notifiable disease in the United States during 1999 after nationwide case counts increased significantly. Most of these infections occur in the upper midwestern and northeastern states, with eastern New York and the Hudson River Valley areas being of particular focus and concentration.

It comes as no surprise that confirmed Anaplasmosis are the highest during the summer months when people are active in the outdoors.

Background

O’Connor started his research on anaplasmosis while working on his masters degree at UAlbany with a team of other public health researchers and scientists in both academia and government. Their ongoing and collective research focuses on evaluating human risk of acquiring anaplasmosis (as well as Lyme Disease, babesiosis and other tickborne diseases) at publically accessible lands, based on measures of tick density and pathogen prevalence. Central to the investigation is looking at the relationships between human activity and environmental characteristics of specific land cover types supporting mammalian populations which serve as both transmitters and hosts of the A. phagocytophilum pathogen.

O’Connor’s research involves numerous datasets as part of his research, most notably human case data – which is analyzed at the ZIP Code Tabulation Area (ZCTA) level –  as reported to the New York State Department of Health (NYSDOH). Data is aggregated by NYS regions (Capital, Central, Metro, and Western) and ZCTA by using patient address and 2010 US Census population data and shapefiles.

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Geospatial Student Spotlight: Matthew Ward

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Academic Institution:

Hunter College                                                          New York City
Department of Geography and Environmental Science
M.S. Geoinformatics (June 2021)

Virginia Tech                                                             Blacksburg, VA
School of Public and International Affairs
B.S. Environmental Policy and Planning   (May 2010)

Research Focus:

Ward’s graduate research at Hunter College focuses on the expanding use of Augmented Reality (AR) in the geospatial disciplines  with a  particular  interest  in  supporting civic  and public engagment.   AR is an interactive experience of the real-world environment where the objects that reside in the real world are enhanced by computer-generated content and perceptual information.   His research inspired by spatial AR research and development work at companies such as Google and  ESRI.

Ward’s work was recently highlighted and included  in the 2022 NYC OpenData  Week which included  augmenting street level imagery with the NYC PLUTO database which offers over 70 data variables at the tax parcel level.  Built on the WebXR standard, the web app leverages the THREE.js graphics library with files and front-end hosted on Github and the data on a Heroku  Postgres cloud platform.  The code repository and web app is available on Github. A short demo of the application can be seen here.

Cubes represent PLUTO centroids along the street face.   The magenta cube is the one currently selected by the user and provides current PLUTO information of the selected parcel in the upper left hand corner of the screen.  Cyan cubes represent other available PLUTO centroids for query. 

Ward’s smart phone app is now available (iOS and Android) here for entire NYC footprint.    Additional documentation and instructions are available on his GitHub repository.  

Selected Additional Geospatial Projects

Gerrymandering Web Map

Ward performed a series of analyses on the proposed plans by the New York State Independent Redistricting Commission (NYRIC) based on data related to the 2020 census.  Because the Commission couldn’t agree on a single draft plan, the bipartisan members drafted two plans.  Two of the analyses are shown below (References to the “Names” (Republican)”vs “Addresses” (Democrat) Plans are from the New York State Senate District plans submitted by the NYIRC.

The image above (from a web map) is a test of “roundness” (aka the Roeck test [1961]).   In general, rounder districts will have a higher Roeck score and oddly shaped or districts that are wider or longer (darker shaped districts) will receive lower scores. Although not a perfect assessment of gerrymandering, the Roeck test offers good comparison between district shapes. 

Another test done by Ward was comparing the average voter party tilt of a district to its neighboring districts (darker purple districts are more unlike their neighbors).  Metro NYC shows what could be considered large areas of voter homogeneity.

An map of an additional analysis is included with Ward’s paper (available here) summarizing this research work and findings.  The third review looked at identifying those proposed senate districts which had at least 30% minority group population (African-American, Asian, and Hispanic/Latino populations were used). Such districts are almost entirely located in New York City, with some inclusion of parts of Nassau county.  The research paper includes a comprehensive list of all data used in the gerrymandering analysis. A “live”  interactive version of the web mapping app can be accessed here.  The three different analysis for each plan (Names and Letters) can be viewed by clicking the layers button in the upper right hand corner of the viewing window.  (Designed for desktop viewers not smartphones.)

 Crime Modeling Across NYC

Ward’s graduate coursework also included developing a crime view modeling viewer (below) based on NYC OpenData reported crimes for 2019. Selected violent crime types were analyzed.  He developed an even square grid to cover the citywide footprint so as to render the spatio-temporal nature of the data.   Crimes that occur are tracked in space and time within those squares.  Data is associated with corresponding police precinct building addresses (how the data is made available through OpenData – not necessarily the exact crime location).

The data is for all of 2019 but each day of the model (365 days total) is rendering the three weeks of crime leading up to that day to predict ‘likelihood’ of crime going forward. Darker purple is an application “indicator” of where more violent crimes may occur based on the historical data.  Yellow grids meaning less likely than purple.  No color means no spatio-temporal connections in crimes or no crimes.  Click here to run the video.

Summary:

The Hunter College Masters in Geoinformatics (MSGEOi) focuses on the growing demand for emerging professionals  trained in the collection, organization, analysis, and dissemination of geospatial data.  Matthew Ward’s graduate work is illustrative of this as it intersects developing analytical methods and visualizations of large geospatial datasets. 

After school, Ward is interested in applying his work and research in the public or government space.  “I see the great power of combining open source technologies and open data portals for empowering local users with AR technology, he notes.  “For example, there are so many types of uses in helping the public visualize utilities and the public infrastructure or in environmental applications.”  

Augmented reality is no doubt a rapidly expanding and growing technology in the geospatial arena.

Contact:

Matthew Ward
Graduate Student
Hunter College, City University of New York (CUNY)
Department of Geography and Environmental Science
MATTHEW.WARD14@myhunter.cuny.edu

Dr. Sean Ahearn
Professor & Director, Center for Advanced Research of Spatial Information (CARSI)
Department of Geography and Environmental Science
sahearn@hunter.cuny.edu

Geospatial Student Spotlight: Caitlyn Jeri Linehan

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Academic Institution:

Lehman College, City University of New York (CUNY)                              Bronx, NY
Department of Earth, Environmental, and Geospatial Sciences
MSc Geographic Information Science  (June 2021)

Trinity College                                                                                            Hartford, CT
B.S. Environmental Science  (May 2019)

Research Focus:

Linehan’s graduate research focused on studying future urban sprawl in the metropolitan area of Nashville, Tennessee (Davidson County).  As part of this work, she applied the SLEUTH (Slope, Land cover, Exclusion, Urban growth, Transport and Hill shade) Model which utilizes several commonly accessible geospatial datasets including
USGS Shuttle Radar Topography Mission (SRTM), National Land Cover Database (NLCD), U.S. Census Bureau road files, and Digital Elevation Models.  Additional geospatial data was added from local sources.

Developed by Dr. Keith C. Clarke at UC-Santa Barbara, SLEUTH is categorized as a “cellular automata (CA) model” and is open source and available for download.   It models urban growth based on cells (5km x 5km, 30m x 30m, etc.) which support the geographic  unit of analysis.  The model suggests that changes in the geographic construct of any specific cell normally mimics and is a result of similar changes in neighboring cells.  Clarke was faculty at Hunter College in NYC 1982-1986.

Historical imagery and vector data of the datasets identified above of Davidson County for 2001, 2006, 2011, and 2016 were collected and used to calibrate the SLEUTH model to simulate urban growth for the period of 2017-2040. The urban area for 2016 was 119.81 square miles and from the SLEUTH model is said to increase to 121.90 square miles for 2040. The urbanization rate during the historical time period (2001-2016) is 11.11% but the SLEUTH model predicts a  much slower urbanization rate during the simulated time period (2017-2040) of 1.59%.  This study shows the SLEUTH model can be beneficial in modeling future urban growth but also suggests a need to model more accurately development within intra-urban areas as well as vertical urbanization within already densely urban regions.   Here is a great 10-minute YouTube presentation describing the study and for those interested in more detailed information, here is a copy of Linehan’s thesis.

While the population of Nashville and Davidson County is expected to grow over the next 20 years, most of the growth is anticipated in already developed areas minimizing urban sprawl.

Selected Additional Geospatial Projects:

New Yorkers for Parks

As an intern for the New Yorkers for Parks, Linehan created Open Space Profiles on parks and open space citywide, broken down by each NYC community board district. From open space quality and access to demographic and health information, the Profiles offer a summary for all 59 NYC community districts. This product enables elected officials, candidates, and community groups alike can use to better understand and convey open space issues in their communities.

Many interesting facts are presented in the profiles. For example, 33% of New Yorkers do not have a park within a five minute walk and 48 of 59 districts have less than 10 percent of city-owned parkland within their district. More data can be found here.

Best Places to Breath in New York City

That map for the land use regression having to do with PM2.5 (particulate matter) concentrations in NYC was done as a class assignment. The purpose of this map was that there is air monitors all over NYC. Using data air monitoring stations across New York City, Linehan used an ordinary least squares regression to test the associations between the dependent variable PM2.5 (particulate matter) measurements and the independent variables (major truck routes and population density). The result from this regression was estimated PM2.5 data which was used to interpolate a surface which shows the estimated PM2.5 values across NYC. The map in the lower right shows that the suggested and safe PM2.5 values is 12 micrograms per cubic meter according to the U.S. National Ambient Air Quality Standards. Only isolated locations in south Brooklyn and Queens and a few areas in Staten Island actually have an acceptable PM2.5 value according to this study.  Data used in the study was from U.S. EPA, New York State DOT and U.S.Census.

With the focus on using data from citywide air monitoring stations, Linehan’s project identified only a select number of areas across the city that met acceptable air quality standards.  This poster can be downloaded here.

Accessibility and Connectivity of Bike Paths in the Bronx, New York

Another one of Linehan’s class projects focused on applying a network analysis on the accessibility of a low stress bike network in the Bronx to access select facilities which promote well being such as parks, recreation centers, and libraries.  It is well established people that engage in physical activity can greatly reduce the risk of different diseases as well as reducing stress and anxiety.  To this end, biking is a great form of physical activity that provides these benefits though a deterrent to urban biking in areas such as the Bronx, is a lack of a connected bike path.

Her findings, which are included in the poster below, finds that though only a small proportion of Bronx residents do not have access to a park entrance via a bike portal, it is estimated a disproportional percent (89%) do not have access to recreational centers and 53% lacking bike access to libraries.  Central to the analysis is applying “Level of Traffic Stress” (LTS) coefficients to each road segment.  A copy of the poster below can be downloaded here.

Safe and readily available bike networks are important factors in providing access to urban cultural and community facilities.  This poster can be downloaded here.

Summary:

The Lehman GISc Program emphasizes “real-world” applications of geotechnologies and geospatial analysis to solve problems and improve conditions focusing on New York City as a “living laboratory.”  The program continues to grow and providing trained and well educated graduates to organizations and governments across the metropolitan area.

Linehan will be continuing her graduate education by beginning her doctoral work at the University of California – Santa Barbara which was one of the original three universities associated with the The National Center for Geographic Information and Analysis (NCGIA).   Formed in 1988,  NCGIA also includes the University of  Buffalo and the University of Maine.

 Contact:

Caitlyn Jeri Linehan
Graduate Student
Lehman College, City University of New York (CUNY)
Department of Earth, Environmental, and Geospatial Sciences
CAITLYN.LINEHAN@lc.cuny.edu
CaitlynLinehan@ucsb.edu

Dr. Yuri Gorokhovich
Associate Professor
Lehman College, City University of New York (CUNY)
Department of Earth, Environmental, and Geospatial Sciences
yuri.gorokhovich@lehman.cuny.edu

Geospatial Student Spotlight: Ge (Jeff) Pu

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Academic Institution:

SUNY College of Environmental Science and Forestry
Department of Environmental Resources Engineering
Ph.D Environmental Resource Engineering (Dec 2020)

Drexel University
Department of Civil and Environmental Engineering
B.S. & M.S. Environmental Engineering

Research Focus:

Ge’s doctoral research focused on monitoring riparian vegetation conditions overtime and quantifying correlations to selected water quality variables. In addition, he focused on quantifying the impact of riparian vegetation presence on river channel boundary changes. Such riparian vegetation environments have a significant role in filtering contamination and maintaining water quality in riverine and stream systems. His research involved the use of high spatial resolution aerial photos to detect and delineate riparian vegetation status. Riparian ecosystems across New York State remain under stress from climate change, agricultural practices and urbanization.

Study Area and Background:

Ge’s study areas included the Genesee River which originates in Gold, PA flowing north to Rochester, NY and ultimately into Lake Ontario.   Land cover in the study area is dominated predominately by agriculture and forest, with smaller amounts of developed land, including a mixture of residential, commercial, transportation, and other non-developed lands.  Various parts of the Genesee River are currently listed as impaired on Section 303 (d) of the Federal Clean Water Act based on the presence of various pollutants, which includes phosphorus, sedimentation, oxygen demand, and pathogens.  Riparian buffers along the Genesee River play a significant role in improving the overall condition of the river and help combat many of the water quality related problems through filtering various contaminations, trapping sedimentation and ultimately improving river water quality.

The Mount Morris gravity dam on the Genesee River was utilized as a separation point when comparing the results of riparian vegetation indices. This separation formed a logical break since upstream of the dam the channel follows its natural path, whereas flow downstream of the dam is regulated by the dam instead of natural flow regimes.  An interactive web map of the study area is here.

The Genesee River watershed stretches from Lake Ontario in the north to Northern Pennsylvania in the south.

Data and Software:

 Data:  Central to Pu’s research was the use of the Google Earth Engine (GEE) which includes two freely accessible image programs: (1) United States Department of Agriculture National Agriculture Imagery Program (NAIP) imagery, and (2) Landsat products.  The NAIP images are airborne color-infrared orthorectified images acquired at 1 meter ground sampling distance. This fine spatial resolution enables interpolation of detailed information on the boundaries of river channels and riparian vegetation. The NAIP program collects imagery on a regular basis, with images of the study area available from 2003–2015. GEE provides access to Landsat 5 and 8 8-day Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) composites.                                                     

Other geospatial datasets also utilized included USGS water quality data, and (2) National Oceanic and Atmospheric Administration (NOAA) weather data. Downstream water quality data was collected by the USGS in the Genesee River at the Ford Street Bridge in Rochester, NY and downloaded through the USGS Water Data for the Nation.  Parameters collected include water temperature, specific conductivity, pH, turbidity and dissolved oxygen.  Daily weather data is collected by the National Oceanic and Atmospheric Administration (NOAA) at three airports within the watershed: Greater Rochester International Airport, Cattaraugus County Olean Airport, and Dansville Municipal Airport. Weather parameters recorded include daily mean precipitation and daily cumulative air temperature. Weather parameters across the three stations were averaged to eliminate micro climate effects. Water flow/gauge data provided by the U.S. Army Corps of Engineers.

 Software:  Data was examined and processed in a variety of desktop software packages and cloud platform including, as previously identified, Google Earth Engine as well as  ArcMap, QGIS and R.

 Project Findings:

This project developed a new method to extract multi-temporal riparian vegetation indices directly from satellite image composites which included five major steps: (1) Identifying the channel boundary, (2) creating a buffer around the channel, (3) classifying land cover within the riparian buffer, (4) converting pixel-based vegetation buffers to polygons, and (5) generating final riparian buffer boundaries.

Pu’s Genesee River riparian mapping was based on GEE’s multi-petabyte catalog of satellite imagery and geospatial datasets which are available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth’s surface.

The project utilized GEE for both image processing and spatial analysis processes, along with some usage of QGIS.  GEE was found to provide an efficient means to perform environmental data monitoring because it eliminates the processing time and effort involved with downloading, sorting, and combining datasets in order to perform the calculations and other processes necessary to obtain time series vegetation index data calculations.

Findings indicate that downstream water temperature and dissolved oxygen have moderate to strong correlation to riparian vegetation index (magnitude of Spearman’s correlation coefficient generally above 0.5), while other water quality parameters do not. Water temperature was positively correlated with the vegetation index values, while dissolved oxygen was negatively correlated. There were no significant differences in terms of correlation between the two sections of the river.

Analyzing nearly a decade of imagery covering the Genesee River footprint, Pu’s research shows the meandering of both the main river channel and at major river bends both of which impact adjacent riparian habitats.

Overall, the study developed a new method to rapidly extract time series of riparian vegetation indices directly from satellite image composites.  In delineating the main channel of the Genesee River and mapping riparian vegetation within 90 meters of the channel the research observed the expected annual trends, where the index rises in the summer season while falling in the winter season.

Findings will be useful to riparian stakeholders and managers to conduct informed riparian vegetation management and restoration. Delineating riparian vegetation extent from higher resolution imagery allows one to identify riparian zones that have significant riparian removal or channel variations and to prioritize restoration sites considering both spatial and temporal scales. Results drawn from Pu’s research has been and will continue to be presented at local and international conferences for improving current riparian management approaches.

A more complete and detailed document on Pu’s research containing further discussion/analysis, statistics and graphs, and summary details can be downloaded here.

Google Earth Engine (GEE):

 Jeff’s extensive use of GEE as part of this doctoral work led to additional work which was primarily funded through AmericaView and SUNY-ESF.  Funded primarily through a grant from the U.S. Geological Survey, Ameriview is a consortium comprised of university-led, state-based consortia working together to sustain a network of state and local remote sensing scientists, educators, analysts, and technicians.   The state’s AmericanView affiliate is New York View   (NYView) which is administered through the Department of Environmental Resources Engineering (ERE) at SUNY-ESF.  The NYView academic consortium includes the State University of New York (SUNY) College of Environmental Science and Forestry (ESF), the Institute for Resource Information Sciences (IRIS) at Cornell University, SUNY Fredonia, and SUNY Plattsburgh.

Through this NYView project, Jeff developed a series of 16 free-to-access Google Earth Engine online training videos (available on YouTube) for the beginner to advanced which demonstrate applications to support teaching, research, and outreach in remote sensing and image processing.  GEE is a cloud-based platform that combines a catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. This platform is available for academic, non-profit, business, and government users to analyze and visualize the Earth’s surface.  He was able to also work closely with the Google Earth Engine development team which provided access for development and programming assistance.  A one-page summary of the NYView 2019-2020 Mini-Grant project can be downloaded here.

Summary:

Jeff is currently preparing for a postdoctoral research position. He hopes to become a professor in the future based on his of research and associated work in academia.  His research interests continue to evolve in the realm of water resource engineering with a focus of utilizing the latest technologies to help solve water resource challenges across both urban and rural environments.

Contact:

Ge (Jeff) Pu
State University of New York – College of Environmental Research and Forestry
Department of Environmental Resource Engineering
gpu100@syr.edu

Dr. Lindi Quackenbush
Associate Professor
State University of New York – College of Environmental Research and Forestry
Department of Environmental Resource Engineering
ljquack@esf.edu

 

Geospatial Student Spotlight: Christopher Plummer

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Academic Institution:

The University at Albany, SUNY
Department of Geography and Planning
Pursuing M.S. in Biodiversity, Conservation, and Policy

B.S The College at Brockport, SUNY, Environmental Science and Biology

Research Focus:

Using drones to assess white-tailed deer abundance and habitat preference in the Albany Pine Bush Preserve

Plummer’s overall research goal was to use thermal imaging drones to perform aerial surveys of white tailed deer and associated drone technology to produce an up-to-date habitat assessment of the study area to assess both white-tailed deer abundance and associated habitats. Plummer’s research proposes drones:

  • Offer a faster and lower cost of aerial data capture
  • Can be flown with lower detection from wildlife
  • Can be equipped with thermal sensors to aid in wildlife detection
  • Offer high accuracy population data with robust statistical strength

Study Area and Background:

Located northwest of Albany, the Albany Pine Bush Preserve (APBP) is approximately 3,350 acres supporting a broad variety of habitats highly fragmented by roads developed areas.  Hunting, fishing and trapping are recreational activities that are carefully regulated in the preserve following relevant APBP and New York State Dept. of Environmental Conservation (NYSDEC)  rules and regulations.

Historically, the monitoring of whitetail deer (WTD) has been difficult to establish due to obtaining reliable abundance estimates as traditional survey methods are normally very work intensive and/or expensive.  In the past such estimates have been done within the preserve with spotlight surveys and camera trap surveys.

(Historically, obtaining reliable abundance estimates of white-tailed deer has been difficult to establish, with more traditional methods being work intensive and/or expensive, and having issues with imperfect deer detection. In the past such estimates have been done within the preserve with spotlight surveys and camera trap surveys. Plummer plans to use the findings from the previous work to help understand how the use of drones compares to the more traditional survey methods.

Unmanned Aerial Vehicles (UAV) technology, more commonly called drones, was chosen as a means to facilitate WTD data collection and analysis in the Preserve.  Specifically, DJI Mavic 2 Enterprise Dual was used for the thermal deer survey while other DJI drones (Phantom 4 and  Insprie 2) were used to help create the orthophoto map.  A MicaSense RedEdge 3 multispectral camera which was attached to one of the drones helped pick up additional reflectance characteristics of varying vegetation types.  This camera has 5 bands, with the two additional bands sensing red edge (~715 nm) and near infrared (~825nm) wavelengths.

Universal Ground Control Station (UGCS) and DJI Pilot software was used to develop flight plans over three distinct focus areas in the Preserve.  Multiple flights were made to acquire enough images with sufficient overlap.  Plummer used Agisoft photogrammetry software to generate orthophotos of the study areas.

ENVI software was used to generate a supervised canopy cover classification of the study area.  Plummer was able to classify the entire orthophoto with >95% accuracy into herbaceous/open canopy, deciduous canopy, coniferous canopy, bare ground/no canopy, and developed canopy.  ArcMap 10.6 was used in a variety of ways as well as R-Studio and Microsoft Excel for data analysis.

Three flight plans in Blueberry Hill East and West and the Kaikout Kill Barrens

The research team flew a total of 34 surveys between March 9th and May 22nd 2020 with all flights occurring within 1 hour of sunrise collecting over 950GB of imagery files.  A total of 405 deer were spotted and identified in 143 unique locations.

Silhouettes of five deer as seen through Plummer’s use of thermal imaging photography

Project Findings

Plummer’s initial calculated observed average density of 13 deer per sq. km is unlikely to be uniform over the entire Preserve due to varying  landscape dynamics and various habitat types. The abundance within their study area varied widely over time, which Plummer suggests  is explained by daily movements made by individual groups into the nearby residential areas.  With regards to habitats, deer were more frequently found in the following areas of the Preserve:  (1) areas of recent timber harvest, (2) Pitch Pine cover and in the pitch-pine scrub oak barrens, (3) areas with significant dune activity, and (4) close to the edge of Preserve boundaries. He believes this new method for assessing white-tailed deer abundance has high potential in the wildlife monitoring space and hopes  his findings about deer within the Preserve will help generate well informed deer management decisions.

Plummer generated a habitat classification map based on the percent of each canopy type within a 50m x 50m grid covering the entire study area. The Pitch Pine Dominant Forest habitat along the southern border of the Preserve was one of the areas with the highest WTD counts.

Actual WTD observed during any given survey varied over time. Over the near three month data collection period in early 2020, WTD observation counts ranged from 1 to 30

Summary

Plummer’s SUNY Albany graduate work suggests that thermal drones are a viable tool to assess WTD population dynamics and can generate reliable abundance estimates.  Drone deployment provides the ability to survey large geographic areas in a small amount of time and a cost effective manner.  Results show that deer use a variety of habitat types in the Preserve and numbers at any one location varies temporally.  A detailed video presentation Chris made outlining his work to the members of the APBP is available here on YouTube.

Post-graduate work, Plummer would welcome the opportunity to work for an agency such as U.S. Fish & Wildlife Service or NYSDEC Division of Fish and Wildlife. He’s not opposed to going back to school for a PhD but not anything immediately.  He is also very interested in public policy related to climate change and sustainability.

Contact:

Chris Plummer
Graduate Student
University of Albany
Geography and Planning
ccplummer@albany.edu

Dr. Alexander Buyantuev
Associate Professor
University at Albany
Geography and Planning
http://www.albany.edu/gp/Buyantuev.php