I am going to participate in the challenge this year and will try to do as many days as time allows
Day 1: Points. This is a map of the distribution of city bicycle racks that were installed either in or prior to 2011 and those that were installed from 2015-2021. New installs have been concentrated in the Northside of the city. Datasets are from the Chicago Data Portal. The map was made using ArcGIS Pro
Day 2: Lines. This is a map of the trails within the Breaks Interstate Park displayed on 3D topography. The map was created using ArcGIS Pro
Day 3: Polygons. This is a map of the Great Lakes system, between Canada and the United States in North America, created with QGIS.
Day 4: A bad map. This is a map showing the neighborhoods of Pittsburgh, PA, USA; however, the city beneath is not Pittsburgh. Thanks to an incorrect projection file assigned to the layer the boundaries are positioned over Madrid, ES instead. It is unclear which direction is north since there are conflicting north arrows. There is a scalebar for size reference, but one will need to decode to wingdings 3 in order to understand the numbers. It may also be slightly difficult to determine the names of each neighborhood with a font size that large.
Web Mapping
HTML, Java, Python for Web-based maps using Leaflet and Mapbox libraries. QGIS was used for data prep and maps were publishing online using Github pages.
This project focused on using the Mapbox Isochrone API to determine travel distance within Chicago. It can be combined with multiple origins to determine where parties can meet, delivery boundaries, etc. More examples can be found in this tutorial I created as a class project.
An automated tool used to batch-clip layers to a specified boundary. The tool projects all layers in a folder to the same projected coordinate system before clipping them to the extent of another specified layer and saving them to a new geodatabase.
The tool will save the intermitent, projected layers in a separate folder than the original or final outputs. The parameters provide helpful guidance and error feedback.
The tool is available for download from my github where you may also view the script.
If GIF is too scaled down, you may view it in a new window by clicking here
Inverted Polygon symbology tutorial in QGIS
Tutorial on inverted polygon symbology in QGIS
Tutorial showing how to apply inverted polygon symbology in QGIS
Pansharpening
Landsat imagery in ERDAS Imagine
Comparison of orginial 30 meter resolution Landsat 8 color imagery and pansharpened 15 meter resolution. Shown in false color (7,6,4 band combination)
Downtown Pittsburgh, PA, USA. 30 meter multispectral bands from Landsat 8.
Downtown Pittsburgh, PA, USA. 15 meter panchromatic band from Landsat 8.
Downtown Pittsburgh, PA, USA. Pansharpened multispectral image.
Nighttime Light imagery from Landsat stitched as a Mosaic in ERDAS Imagine
Location of Pittsburgh Metropolitan Statistical Area in Pennsylvania, USA
Median NTL Value for the Study Period: To Visualize the Spread of Nighttime Lights Across the Study
Area.
Results of Linear Regression Analysis: Slope Values as the Decreasing or Increasing Trend in Observed
NTL Radiance for Each Feature in the Study Area from 2012-2019
Results of Linear Regression Analysis, Focused on Pittsburgh: Slope Values as the Decreasing or
Increasing Trend in Observed NTL Radiance for Each Feature in the Study Area from 2012-2019
-->
Layer Blending
symbology in QGIS for visual analysis
This map was produced in QGIS. Inverted polygon symbology was used to highlight the city boundary and layer blending was applied to allow visual examination to the patterns of arrests for possession to the distribution of poverty
Digital Elevation Model
DEM produced in QGIS
DEM produced and symbolized in QGIS using data available from Cook County data portal and USGS.
Remote Sensing
Image classification and analysis in Erdas Imagine using remotely-sensed, satellite imagery.
The multispectral bands from Landsat 8 were stacked and mosaiced to cover the area of interest, Pittsburgh, PA.
Subset of mosaic image using Pittsburgh MSA boundary as area-of-interest.
Unsupervised image classification to reclassify the image into urban intensity classes and natural land covers.
From the National Lancover Cover Database
Reclassified land cover types to highlight urbanized versus natural surfaces
Change analysis of land usage within the Chicagoland area
Multi-criterion evaluation
to find suitable locations
Multi-criterion evaluation using Network Analayst for distance from school and work locations along with crime density, tree canopy per block, distance to train station, etc. The project presentation is below for more detailed information.
Multi-criterion evaluation of suitable locations to place hypothetical tourist lodges based on land use and land cover raster layers along with proximity to waterways and elevation.
Network Analayst Extension
In ArcMap Desktop
Comparison of Euclidean distance and actual street network driving distance from Northeastern Illinois University's main campus within the city of Chicago, IL.
Network Analyst outputs, from a multi-criterion evaluation, within the street grid of the city of Chicago, IL.
Tree canopy - LiDAR
Canopy measurments from LiDAR point cloud
Tree canopy measurment of LaBagh Woods, Chicago, IL using Lidar point clouds and producing DEM and DSM layers.
Watershed delineation
Flow Accumulator in ArcGIS
Wetland delineation using Flow Accumlator function in ArcGIS. Displayed with hillshade for terrain texture.
Model Builder
Customizing geoprocessing tools and iterative processes in ArcGIS
Basic model builder example of how to setup a model part for selecting features from a layer and exporting results to a new table.
Basic model builder setup for automating a process with a geoprocessing tool while allowing user-inputted parameters.
Agent Analyst
tutorial and presentation for extension in ArcMap.
Surface Interpolation - Bathymetry
Produced from a subset (430 points) of a larger database of depth samples in the Apalachicola bay. The original database was created by the Apalachicola Bay National Estuarine Research Reserve (ABNERR)
and the NOAA Coastal Services Center. They surveyed the bay and the lower portions of four distributaries on 11-22 October 1999 using three benthic sampling techniques.
Georeferencing
Physical ground points were taken using a handheld GPS unit and used to georeference a high-resolution photo of the NEIU campus.
Density Mapping
.
Density of 2010 domestic crime records from Chicago Data Portal
Side-by-side visual of plotted crime data points and density surface
Crime density using burglary records in Chicago, IL using data from the Chicago Data Portal
Comparison of density surface calculations of burglary records in Chicago, IL using data from the Chicago Data Portal
3D display of the Crime Density surface
Timelapse accumulation of Covid-related deaths reported in Chicago, IL during early 2020
ESRI Instructor-lead Courses
Map of the percentage of Impervious surfaces in the Chicagoland area using data from the 2011 National Land Cover Database
Surface Interpolation - Bathymetry
Produced from a subset (430 points) of a larger database of depth samples in the Apalachicola bay. The original database was created by the Apalachicola Bay National Estuarine Research Reserve (ABNERR)
and the NOAA Coastal Services Center. They surveyed the bay and the lower portions of four distributaries on 11-22 October 1999 using three benthic sampling techniques.
Georeferencing
Physical ground points were taken using a handheld GPS unit and used to georeference a high-resolution photo of the NEIU campus.
Demographic-Data Maps
.
Changes in Population Density During the Study Period: Based on Decennial Census Values from 2010
and 2020
Changes in Population Density During the Study Period, Focused on Pittsburgh: Based on Decennial
Census Values from 2010 and 2020
Population density in Chicago, IL using 2010 Census Data
Average population density by neighborhood in Chicago, IL
Poverty rates in Chicago, IL using 2010 Census Data
Average poverty rate by neighborhood in Chicago, IL