2024 SCHEDULE

Landslides are major natural disasters that can result in a considerable amount of property damage, environmental impacts, and in the worst cases the loss of human life. Each year, landslides play a great role in destroying structures such as roads, farms, residential areas, as well as forests. These destructive events produce debris that is high in sediment volume, which results in rapid deposition and can choke off rivers and streams, damaging local ecosystems. To mitigate the loss of life, damages, and environmental impacts of landslides, it is important that hazard mitigation and risk assessors be provided with appropriate, detailed information regarding the past occurrence and future susceptibility of landslides for regions where they are a threat. Most landslide inventories are created by using manual detection methods, which require considerable amounts of time, money, and person-power to complete. An automated detection algorithm would significantly reduce the amount of time, money, and person-power required. Persistent homology can be used to create an automated landslide detection algorithm using ridge points as it can identify ‘hole’ structures that may delineate the perimeter of a landslide. One of the benefits of using persistent homology is its ability to work with high-dimensions of data, so beyond the positional and elevation information of each ridge point, other data that might better help identify a landslide can be added. Examples include the Normalized Differential Vegetation Index (NDVI), which may highlight vegetative health affected by slope instability, or the Apparent Thermal Inertia (ATI), which may highlight areas where landslide structural features allow for water seepage/percolation. This study presents an exploration of such datasets to an existing algorithm for detecting landslides, that initially only used positional and elevation data sourced from LiDAR, with a goal of increased accuracy.

The Nanticoke Creek, which is located southeast of Nanticoke, PA, disappears suddenly underground through a fracture, and travels through mine pools; however, the exact travel path of the creek underground is as of yet unknown. It is believed that the creek waters eventually resurface at the Askam borehole as Abandoned Mine Drainage (AMD). This is particularly concerning for the region, as the mine pools the Nanticoke Creek waters travel through can collapse, leading to an increased development of land deformation hazards such as sinkholes, disrupting the continued and possible future uses for the site. To better assess the risk of such land deformation hazards, and to better understand the possible pathways of the Nanticoke Creek waters through the mine pool system, we must determine what mine pools are still intact versus those that have collapsed. This research is part of a pilot study to determine if gravity geophysical methods can be used to highlight at-risk areas and identify locations of mine pool collapse. Such methods require forward modeling to account for gravitational differences between sites, which further requires digitization of historical mine maps and an updated elevation dataset. The latter is used to process gravity data to determine anomalies that may highlight collapsed mine pools, or developing sinkholes.

A poster showing the Pennsylvania State Plane Coordinate System (SPCS) zones of the current NSRS horizontal datum, NAD 83, as compared to the forthcoming new datum, NATRF2022. With this poster I intend to highlight the differences between the zones of SPCS83 and SPCS2022 in Pennsylvania, and to ensure that the geospatial community of the Commonwealth is aware of these upcoming changes.

The Philadelphia Police Department uses directed patrol to add visible police presence in areas with elevated risk for violent crime to act as a deterrent. For the safe and effective application of this tactic, the Department requires solutions to meet field communications, situational awareness, data collection and analysis, and real-time operations monitoring needs. ArcGIS Mission, an Esri product designed to be an all-inclusive tool for creating, managing, and reviewing field mission operations, can be configured to support these ongoing directed patrol efforts. To evaluate the suitability of ArcGIS Mission for this use case, the three main components of ArcGIS Mission were installed and configured in the PPD environment, and a test mission was designed to simulate a week of directed foot patrol operations in a target area.

Existing geospatial data related to this mission’s operations was pulled from police repositories and displayed in the mission basemap, and, using ArcGIS Mission REST API, ETL scripts were written to pull data collected during patrol and aggregate it in police data repositories for analysis and recordkeeping. To simulate law officers in the field, operatives used Responder, the ArcGIS Mission mobile application component, to communicate and collect spatial data from within the target area over the course of a week. Preliminary findings of this demonstration indicate that ArcGIS Mission can effectively address many operational requirements of ongoing directed patrol in law enforcement. Furthermore, although the out-of-box product primarily targets operations management of unique events, the functionality of ArcGIS Mission’s communication, situational awareness, and operations management tools provide avenues for additional use cases in a multitude of sectors, including long-term or repetitive operations.

This research is part of a larger project to better understand the geologic development of the Appalachian Mountains in the Cow Head region of Western Newfoundland, Canada. Specifically, this particular research is focused on producing a bedrock geologic map for the Cow Head Peninsula and analyzing structural data. Prior mapping on the Peninsula collected minimal structural data and focused only on exposures located around the shoreline, leaving a dearth of data in the center. In the interior of the Peninsula, there are some sparse exposures of very weathered bedrock, but most of the bedrock is overlain by dense vegetation and soil making it difficult to identify and interpret the mapping units. Since the majority of prior mapping on the Cow Head Peninsula was conducted in the 1950s, only coarse resolution elevation datasets and limited aerial imagery along with lower accuracy spatial data were available to aid map interpretations. Our presented work overcomes the existing challenge presented by low-resolution elevation models and limited aerial imagery by producing a photogrammetrically produced high-resolution orthophoto and digital terrain model (DTM)) as well as a thermal orthophoto. Imagery for processing were collected using a small unmanned aerial system mounted with visible and thermal band cameras. The thermal imagery can help identify bedrock expressions that are otherwise hidden by the dense vegetation in the central parts of the Peninsula, and the combined photogrammetric products help aid in developing our geologic map more accurately.

The Philadelphia Water Department has been mapping their Sewer assets in GIS since 2004. Part of this data includes brick sewers that can be up to 150 years old. We will present a map that shows how these sewers are meticulously mapped throughout the city as well as the supporting documents that they are based on. We will also include some statistics and information on how they are inspected and maintained.

This map of the United States and associated territories depicts federal recreation lands (e.g., national parks, scenic sites, preserves, etc.) and associated properties and points of interest to include the National Trails System, Wilderness, and natural features.

Malaria due to the Plasmodium falciparum parasite continues to be a major public health concern and is spread via mosquitoes within the Anopheles genus. While individual species of the Anopheles genus have specific environmental requirements, suitable habitat can be broadly identified based upon rainfall, temperature, and landcover requirements. Accordingly, a workflow and web application were developed that leverages Google Earth Engine to determine suitable habitat for four species of Anopheles mosquitoes. While this project is focused on the Greater Mekong Subregion, the techniques used can be applied to other regions and species with well-defined habitat envelopes.