Jennifer's GIS Blog

The goal of the accuracy assessment is to provide an easy comparison of the completeness and accuracy of the two different road networks. Clip - remove data outside of the grids Calculate Geometry Attribute - length in KM Summarize within - to calculate the length of the road within each grid square Join- join two grids created by summarize within to create a comparable datatable & make it possible to do the required calculations compare_networks(!Grid_Centerlines_Summary.sum_Length_KILOMETERS!, !Grid_TigerSummary.sum_Length_KILOMETERS!) def compare_networks(x, y): if x > y: return "Centerlines" elif y > x: return "Tiger" else: # This will handle the case where x and y are equal return "Equal" !Grid_Centerlines_Summary.sum_Length_KILOMETERS! - !Grid_TigerSummary.sum_Length_KILOMETERS! ((!Grid_Centerlines_Summary.sum_KM_Length!-!Grid_TigerSummary.sum_KM_Length! )/ !Grid_Centerlines_Summary.sum_KM_Length!)*100

  To complete this exercise, I first created three shape files. I then digitized the intersection of 60 test points, 20 for ABQ Streets, 20 for Street Map USA, and 20 for reference derived from the provided aerial imagery. I used a batch process to add XY coordinates to all three shapefiles simultaneously. Rather than exporting to DBF, I selected the columns as needed and copied and pasted them into the correct spot in the workbook I set up in Google Sheets. You can access a template workbook with the formulas added here Street Map- Tested 211.41 ft horizontal accuracy at 95% confidence level ABQ Streets- Tested 26.11 ft horizontal accuracy at 95% confidence level

 Medium-sized image of your map layout (Deliverable 2)  The numerical results for horizontal accuracy and precision (Deliverable 4)  Report your results for horizontal accuracy.  3.3m How does this compare to your results for horizontal precision?  This result is less than the anticipated 68th percentile precision of 4.5m Report your results for vertical accuracy.  6.0m How does this compare to your results for vertical precision?  This is almost 2 more meters above the vertical precision measurement of 4m In general, is there any evidence of bias in your results? If so, where?  There is evidence of bias in the vertical results because the vertical accuracy of 6m is significantly worse than the calculated vertical precision.  A very brief statement on how horizontal accuracy and precision are measured (so readers know the difference between the two) Horizontal accuracy and precision are measured by comparing a set of measurements to a known t...

Modeling Black Bear Connectivity: A Least-Cost Corridor Analysis This project focused on identifying optimal connectivity pathways for Black Bear (Ursus americanus) movement between two designated park areas. Utilizing a least-cost path analysis framework, we modeled a potential travel corridor by integrating ecological principles with advanced spatial analysis techniques in ArcGIS Pro. While some traditional tools are being superseded by newer, more efficient geoprocessing functions in modern GIS software, the core methodology remains robust. The analysis workflow, conceptualized below, systematically progressed through the following stages: 1. Habitat Suitability Assessment: The foundational step involved creating a habitat suitability model , quantifying the relative "goodness" of the landscape for Black Bear movement. This was achieved by: Reclassifying key environmental variables: We transformed raw data for factors like land cover, elevation, and proximity to human in...

This exercise involved a comprehensive geospatial analysis to identify areas most suitable for development. We integrated five critical environmental and proximity criteria, transforming raw data into a composite suitability map. Methodology: For each of the following five spatial datasets, a reclassification model was applied. In this model, a suitability rating scale of 1 to 5 was used, where 5 represents the highest suitability for development and 1-2 indicates the least suitable land. These five reclassified suitability layers were then combined using a Weighted Overlay technique. Each criterion was assigned an equal influence (20%) to produce a final, integrated suitability map. This resulting map visually highlights a gradient of development suitability across the study area, with higher values (closer to 5) indicating prime locations. An Alternate Weighted overlay was also conducted where Land Cover and soils were weighted at 20%, Slope at 40%, and distance to streams and roads...

 For this week's exercise we were tasked with conducting a structural damage assessment of a few block area impacted by Hurricane Sandy. We were asked to create a map of the path of Hurricane Sandy We were then given a very large raster data set with a very small project area to assess structural damage. Knowing something about Hurricane Sandy and the amount of damage, I immediately ruled out the possibility that there were any structures with no damage or only affected or sustained minor damage. I then narrowed in on determining which structures were destroyed and which sustained major damage. To complete the analysis of damage within a distance of the shoreline I used the created shoreline to create buffers of 0-100 m, 100-200m, and 200-300m. I used a basic join to derive counts for the following result

 For this week's exercise, we spent a considerable amount of time evaluating out-of-date elevation data, looking at the potential impact of storm surge data. This included comparing potential inundation models using LIDAR and USGS DEM-derived data. For the first exercise we were asked to compare Lidar data from New Jersey pre and post Hurricane Sandy. We were given several areas to asses and evaluate what has changed as well as looking at how the area has redeveloped based on a more recently derived buildings shapefile. We then turned our attention to the Tampa Bay Area where we looked at different methods of modeling storm surge and evaluated the predictive ability of each model. This included comparing counts of the types of buildings each building would see as flooded or not As well as comparing which models predicted a percentage of each type of building would be flooded to compare the accuracy of the different models Finally we created a map showing the agreement between the d...