Over the last 20 years, Lyme disease has grown to become the most common vector-borne disease affecting Americans. Spread in the eastern U.S. primarily by the bite of Ixodes scapularis, the black-legged tick, the disease affects an estimated 329,000 Americans per year. Originally confined to New England, it has since spread across much of the east coast and has become endemic in Virginia. Since 2010 the state has averaged 1200 cases per year, with 200 annually in the New River Health District (NRHD), the location of our study. Efforts to geographically model Lyme disease primarily focus on landscape and climatic variables. The disease depends highly on the survival of the tick vector, and white-footed mouse, the primary reservoir. Both depend on the existence of forest-herbaceous edge-habitats, as well as warm summer temperatures, mild winter lows, and summer wetness. While many studies have investigated the effect of forest fragmentation on Lyme, none have made use of high-resolution land cover data to do so at the peridomestic level. To fill this knowledge gap, we made use of the Virginia Geographic Information Network’s 1-meter land cover dataset and identified forest-herbaceous edge-habitats for the NRHD. We then calculated the density of these edge-habitats at 100, 200 and 300-meter radii, representing the peridomestic environment. We also calculated the density of <2-hectare forest patches at the same distance thresholds. To avoid confounding from climatic variation, we also calculated mean summer temperatures, total summer rainfall, and number of consecutive days below freezing of the prior winters. Adding to these data, elevation, terrain shape index, slope, and aspect, and including lags on each of our climatic variables, we created environmental niche models of Lyme in the NRHD. We did so using both Boosted Regression Trees (BRT) and Maximum Entropy (MaxEnt) modeling, the two most common niche modeling algorithms in the field today. We found that Lyme is strongly associated with higher density of developed-herbaceous edges within 100-meters from the home. Forest patch density was also significant at both 100-meter and 300-meter levels. This supports the notion that the fine scale peridomestic environment is significant to Lyme outcomes, and must be considered even if one were to account for fragmentation at a wider scale, as well as variations in climate and terrain. / M.S. / Lyme disease is the most common vector-borne disease in the United States today. Infecting about 330,000 Americans per year, the disease continues to spread geographically. Originally found only in New England, the disease is now common in Virginia. The New River Health District, where we did our study, sees over 200 cases per year. Lyme disease is mostly spread by the bite of the black-legged tick. As such we can predict where Lyme cases might be found if we understand the environmental needs of these ticks. The ticks themselves depend on warm summer temperatures, mild winter lows, and summer wetness. But they are also affected by forest fragmentation which drives up the population of white-footed mice, the tick’s primary host. The mice are particularly fond of the interface between forests and open fields. These edge habitats provide food and cover for the mice, and in turn support a large population of ticks. Many existing studies have demonstrated this link, but all have done so across broad scales such as counties or census tracts. To our knowledge, no such studies have investigated forest fragmentation near the home of known Lyme cases. To fill this gap in our knowledge, we made use of high-resolution forest cover data to identify forest-field edge habitats and small isolated forest patches. We then calculated the total density of both within 100, 200 and 300 meters of the homes of known Lyme cases, and compared these to values from non-cases using statistical modeling. We also included winter and summer temperatures, rainfall, elevation, slope, aspect, and terrain shape. We found that a large amount of forest-field edges within 100 meters of a home increases the risk of Lyme disease to residents of that home. The same can be said for isolated forest patches. Even after accounting for all other variables, this effect was still significant. This information can be used by health departments to predict which neighborhoods may be most at risk for Lyme. They can then increase surveillance in those areas, warn local doctors, or send out educational materials.