Lyme disease is the most common vector-borne disease in the United States. The disease is caused by the bacteria Borrelia burgdorferi and is largely spread by the black-legged tick, Ixodes scapularis. The black-legged tick, also known as the deer tick, is most common in the Northeast, mid-Atlantic, and upper Midwest. A weighted habitat suitability analysis submodel based on environmental factors was conducted to determine locations in the state of Michigan where deer ticks may exist or could become established if introduced. The results from this submodel were then used as part of a second weighted suitability analysis which also incorporated certain human activities that increase the likelihood of encountering deer ticks. A risk map was generated from the second suitability analysis to display areas that are both highly suitable for tick survival and human activity. The risk map showed a majority of the upper peninsula and northern lower peninsula, along with the west coast and portions of the southeast region, posing a risk to humans should a tick population become established if not established currently. Further studies should be conducted that incorporate tick surveillance programs, Lyme disease incidence rates, and human behavior in and around these higher risk areas to limit the spread of future tick populations and bring awareness to Michigan residents.

Data collection is often the most time-consuming part of a GIS research project. For a viewshed analysis this involves identifying the coverage areas and selecting the exact grid squares required by the study area. The goal of this tool is to save the end-user time by automating the LiDAR download and viewshed calculation. This tool is run via the ArcGIS Pro geoprocessing GUI through a custom python tool in a custom toolbox. This tool is specifically designed to retrieve LiDAR data from Pennsylvania Lidar Navigator hosted by Pennsylvania Spatial Data Access, or PASDA. The tool generates a polygon around the target site which is used to select LAS grid squares that were imported via REST URL. These LAS grid squares are then used to download their linked LAS datasets, merged into one universal LAS dataset, and then used to run a geodesic viewshed. The completion of this script produces a viewshed layer with automatic symbolization of green: land visible up to eighty feet above ground level, blue: land visible from eighty feet to one hundred twenty feet above ground level, and no fill color for anything above one hundred twenty feet above ground level. Testing with this tool has resulted in successful viewshed calculations with distances between observer and target features ranging from three statute miles to twenty-six statute miles. With the tool successfully downloading and generating viewsheds this tool allows end-users to multitask while this tool runs in the background, effectively saving the end-user time.

Maximum entropy, presence-only, species distribution modeling of the current and future habitat of Cantharellus formosus across North America is modeled under a range of climate change scenarios. C. formosus is a culturally and economically important ectomycorrhizal Basidiomycetes mushroom species which is highly prized by foragers for its gourmet flavor. It is symbiotic with Pseudotsuga menziesii (Douglas Fir) and widespread along the US west coast, particularly in heavily forested areas of Washington and Oregon, west of the Cascades. C. formosus has been observed as far south as Berkeley, California, and as far north as southern Alaska, as well as in limited areas of the northern Rockies, near the Canada - Idaho border. Using 663 research-grade, crowed-sourced presence observations obtained from the Global Biodiversity Information Facility and 23 ecological variables, the ecological-niche and species distribution of C. formosus was modeled using a maximum entropy, machine learning algorithm. Further, the future distribution of C. formosus was forecast using a range of climate projections, out to the year 2100. Projections indicate that highly suitable habitat is likely to decline, by 8% to 94%, particularly in California where multiple projections show a complete loss of highly suitable habitat. Conversely, suitable and somewhat suitable habitat may increase by upwards of 100%, as the projected habitat migrates to the north. Importantly, due to the ecology and symbiotic nature of C. formosus, while loss of habitat may occur relatively quickly under changing climatic conditions, establishment and/or expansion into new habitat is likely to be slower by comparison.  

The U.S. Department of Housing and Urban Development estimates that over 650,000 people were unhoused in 2023. The U.S. has an affordable housing crisis, and advocates and activists across the country are struggling to fix the causes and catalysts for this issue. Prevention, preferable to correction, highlights the importance of keeping people housed as the most desirable intervention. The difficulty with being proactive is in achieving the ability to see the when and where of potential problems before they arise. Geographic Information Systems (GIS) allow problems that are firmly spatially bound, such these, to be investigated in various ways. These systems, however, need training and expertise to be utilized successfully. This project aims to demonstrate a methodology for connecting the spatial and GIS tool expertise of GIST professionals with contextual knowledge and the capacity to operationalize information of advocates and activists on the ground by using a well-established and highly-developed model and toolset for distributed development and collaboration found in the field of open-source software development.

This study uses demographic data from the United States Census Bureau and Geographic Information Systems tools and methodologies to identify bus stops and routes for a public transportation plan for Cattaraugus County, New York. Located in rural Western New York, Cattaraugus County has an aging population and an average poverty rate of 14%, the fourth highest in the state overall and the highest for rural counties. With only two small urban areas located along the county’s Southern border, most of the county lacks easy access to essential services. Population, poverty, senior citizen, and housing demographic data from the U.S. Census Bureau’s 2020 decennial census and the American Community Survey 2022 5-year estimate data at the census block, group, and tract levels were used to identify the best bus stop locations. ArcGIS Pro and Suitability analysis are used to rank and score census data at the block level based on weighted criteria to determine the best area/location for a bus stop. Utilizing the bus stop locations, bus routes are identified using Network Analysis while considering winter driving conditions, rural road conditions, distances, and time. Secondary bus stops within a mile and a half of the direct route are identified to allow for deviated routes, where passengers can call in before a pre-determined time and request a pick-up. Carefully thought-out bus stops and routes place public transportation where they are needed most and will be utilized.

Studies have demonstrated that access to safe public green spaces and other protective community resources is crucial for childhood development between the ages of 0 and 4. By providing every child with the chance to form a meaningful connection with nature, communities can work to ensure a bright and resilient future for their youth. For equitable nature access to be achieved, several factors must be considered that impact the relationship between young children and public parks, including socially vulnerable demographics, proximity to parks, transportation networks, park quality, and public safety. The Children’s Equity Nature Index (CENI) is a composite index designed to map these relationships within the metropolitan areas of Pima County, Arizona. The CENI is made up of two main sub-indices: the Equity Priority Sub-Index, which considers demographics specific to the region that face a higher degree of vulnerability, and the Children & Nature Sub-Index, which examines green space quality, transportation access to green spaces, and the population density of children 0-4 years old throughout the study area. The final map shows the urban census block groups within Pima County that contain more children living in areas with higher social vulnerability and lower access to quality green spaces, which the CENI identified as predominantly highly developed block groups within Tucson’s city limits. With this information, community investment through funding, programs, partnerships, and policies can be designed specifically for the areas within the study area that need them the most.

During Stafford Act events, which authorize the President to issue declarations for federal assistance in response to incidents, rapid decision-making using the best available data is crucial. In the ESF #2 (Communications), Emergency Managers rely on VISA’s data to determine the availability of electronic payment processing and assess disruptions related to grocery stores providing food to survivors (Commercial Food Distribution Subcomponent). This project aims to create a template that transforms daily data, received every morning at 10:00 AM, into actionable information. The problem at hand is the need for faster data analysis to generate timely information about disruptions to a lifeline (A lifeline enables the continuous operation of critical government and business functions and is essential to human health and safety or economic security). Currently, manual data analysis is time-consuming and diverts resources from other critical tasks. To address this, we will use ARCGIS and PyCharm to plan to set up the supporting data and Service Area tools to understand the grocery stores available within a 15-minute drive time. Then, we will utilize the Tabulate Intersection (Analysis) tool to determine the population served by grocery stores before and after an incident. This approach reveals one primary result: it assesses the extent of disruption to the electronic payment processes and Commercial Food Distribution lifeline subcomponents using the supplied data. This project aims to improve the efficiency and accuracy of our response during critical events, ensuring better resource allocation and support for affected populations.

Conservation practitioners face complex decisions when considering species conservation planning, particularly with rare species and landscape-level habitat management. The California State Parks, Santa Cruz District manages 72,290 acres within 44 parks in Santa Cruz and San Mateo Counties. Rare plant species pose specific conservation challenges in these areas, and a broad and comprehensive baseline approach to their conservation has not been conducted. This study focused on five rare species: San Francisco collinsia (Collinsia multicolor), Mash microseris (Microseris paludosa), San Francisco popcorn flower (Plagiobothrys diffusus), Santa Cruz microseris (Stebbinsoseris decipiens), and Pacific grove clover (Trifolium polyodon). Using the habitat suitability modeling tool in ArcGIS Pro, I developed five unique habitat suitability models tailored to each species ecological niche. These models integrated criteria including digital elevation models, detailed vegetation data, soil descriptions, and known population distributions. Within each criteria, categories were ranked using a suitability score from 1 (low) to 10 (high). Suitability scores from each criteria were then added, resulting in a final suitability score. These scores were then exported to a raster and symbolized based on those high to low suitability scores. Preliminary findings provide general insights for California State Parks staff, particularly when assessing wide scale management techniques. These models offer predictions of unknown populations within Parks sites and identify suitable habitat for targeted restoration efforts. Each species model predicted suitable habitat within State Parks boundaries, including areas without current occurrence records. Known occurrence records were used to adjust each model to reflect optimal habitat for current populations. This research underscores the importance of integrating advanced spatial analysis techniques with field data to support conservation and biodiversity management practices. Finally, these findings can inform broader conservation goals and guide future management practices within the Santa Cruz District of California State Parks system and beyond.

Metro Transit is the main public transportation network in Minneapolis and St. Paul. The transportation network is comprised of light rail, buses, and commuter trains. The light rail network is comprised of two lines: the METRO Green Line and METRO Blue Line. The Southwest Light Rail Transit project is going to extend the METRO Green Line 14.5 miles to connect downtown St. Paul to downtown Minneapolis and surrounding locations. Residents have voiced concerns that the extension will bring crime to their neighborhoods because there is a perception that there is a higher amount of crime near light rail stations and platforms. The study examines crimes in Minneapolis and St. Paul from the years 2019 through 2023. Geographic Information System (GIS) analysis is utilized to produce a kernel density map of total crimes throughout each city. Visually, the kernel density map shows that there are crime hotspots in downtown St. Paul and downtown Minneapolis. A buffer analysis was also conducted by creating a buffer around each light rail station or stop along both Blue and Green Lines to calculate the number of crimes per 0.25 mile. The stations with the highest crimes per 0.25 mile were all located in downtown St. Paul and Minneapolis. Population density for each neighborhood was used to help determine the rate of future crime at the five new stations in Minneapolis. Light rail stations and stops are found to have higher crime at their downtown locations compared to other locations in the cities.

Since 2000, Iowa has had over $10 billion in crop loss insurance payouts, in which droughts were the largest weather-related factor. The aim of this research is to use remote sensing to demonstrate how much more drought resistant corn, soybeans, and oats have become. The three crops being observed are corn, soybeans, and oats from 2000-2023 between May 15th and October 15th, which is considered Iowa’s growing season. Currently both genetic modification, in corn and soybeans, and non-genetic modification, in oats, are being used to increase these crops’ ability to resist drought. The U.S. Drought Monitor labels areas of drought and classifies them by intensity using many different factors, with some being precipitation, soil moisture, vegetation health, etc. From this, an average drought intensity number for each year’s growing season is calculated. Using remote sensing data from the Landsat 5, 7, and 8 satellites, an index is calculated showing how drought affected vegetation is. CropScape is a tool maintained by George Mason University that shows crop-specific land cover, this is used to separate and display each crop with the previously mentioned index. Each year’s crop yield totals (in bushels/acre), from the National Agricultural Statistics Service, are presented to see if they correlate with recorded and calculated drought conditions. Using these methods and indices, it is shown that drought conditions are having less of an effect on crops in Iowa over time, meaning that corn, soybeans, and to a lesser degree oats, have become more drought resistant.