Objective: Assisted the Pittsburgh city planning department in ensuring that new downtown developments do not visually or physically conflict with existing historic sites.

Viewshed Mapping: Created a viewshed analysis map to identify and display the specific areas of the terrain that are visible from a key historic landmark ("MARKET STREET--100 BLOCK").

3D Visualization: Developed a local 3D scene to model the spatial relationships and structural heights of buildings situated both within and outside the historic site boundaries.

Data Extraction & Spatial Selection: Used Export Features to isolate specific data points and deployed Select by Location to identify building footprints that intersected with historic site polygons.

Surface & Terrain Analysis: Utilized Extract by Mask to clip National Elevation Dataset (NED) imagery to the Pittsburgh city boundary. Generated a Hillshade raster (customized with a 315° Azimuth and 30° Altitude) to increase terrain contrast and highlight the downtown topography.

Viewshed Geoprocessing: Deployed the Viewshed spatial analyst tool using the historic point as the observer feature and the clipped elevation raster as the input, symbolizing the binary output to clearly map visible versus non-visible areas.

3D Surface Generation: Built a Triangulated Irregular Network (TIN) from 2D topographic contour lines to establish an accurate, localized 3D ground surface for the scene.

3D Extrusion via Arcade Expressions: Extruded flat building polygons into 3D volumetric models by calculating their minimum height. Leveraged an Arcade expression ($feature.STORIES * 10) to dynamically calculate and render building heights based on attribute table data.

Objective: Visualized the spatial relationship between terrain elevation and land use patterns to support localized urban planning and redevelopment initiatives.

Data Synthesis: Successfully integrated and mapped distinct raster datasets—a Digital Elevation Model (DEM) and categorical land use data—within a specific municipal boundary.

Cartographic Design: Developed a clear, professional map layout that effectively communicates overlapping geographic variables by balancing color ramps, category labels, and transparency.

Raster Analysis & Environment Customization: Standardized geoprocessing workflows by configuring application-level Environment Settings. This ensured that outputs from disparate raster datasets shared a unified Coordinate System, Cell Size, and Mask.

Data Extraction & Masking: Executed the Extract by Mask tool to precisely clip large-scale (statewide/regional) National Elevation Dataset (NED) and land use rasters to the specific vector boundary of Champaign County.

Spatial Selection: Utilized Select by Location with custom parameters (such as search distances and spatial inversions) to identify specific suburban buffer zones and exclude central city boundaries.

Advanced Symbology & Visualization: Applied predefined layer files (.lyr) to translate raw pixel values into meaningful land use categories. Manipulated layer Transparency (e.g., setting the elevation raster to 30%) and utilized the Swipe tool to allow underlying land use data to remain visible beneath the terrain shading.

Objective: Visualized the 2020 tract-level population distribution across Cook County while tracking geographic shifts in census boundaries over a five-year period.

Boundary Change Detection: Conducted a comparative spatial analysis to detect and explicitly highlight significant geometric changes (modifications larger than one acre) in census tract boundaries between 2015 and 2020.

Data Processing & Tabular Joins: Extracted demographic tables and TIGER/Line shapefiles from the U.S. Census Bureau. Standardized complex Geographic Identifiers (GEOIDs) using an Arcade string expression (Right($feature.GEOID, 11)) to create matching primary keys, successfully joining tabular population counts to spatial polygons.

Spatial Overlay Analysis: Utilized the Intersect geoprocessing tool to overlay historical (2015) and current (2020) tract geometries to identify where boundary shifts occurred.

Geometric Calculations: Deployed Calculate Geometry to dynamically compute the size of overlapping tract regions in US Survey Acres. Applied Select by Attribute to filter out minor discrepancies and isolate significant geometric changes (areas > 1 acre).

Cartographic Design: Designed a multi-layered choropleth map utilizing graduated colors with natural breaks to display total population density, layered beneath a bright red highlight to distinctly emphasize the modified census tracts.

Objective: Identified the optimal growing habitats for a specific oak tree species within Pittsburgh by evaluating multiple environmental and municipal constraints.

Workflow Automation: Replaced manual spatial operations by designing a custom, automated macro model that evaluates overlapping suitability criteria (proximity to rivers, distance from parks, floodplain avoidance, and city limits).

ModelBuilder Configuration: Developed a visual programming workflow in ArcGIS Pro to chain together sequential geoprocessing tasks, streamlining the transformation of raw vector data into a single, cohesive suitability polygon.

Proximity & Overlay Operations: Deployed the Buffer tool to create inclusion zones (areas within 5,000 feet of rivers) and exclusion zones (areas within 0.1 miles of parks).

• Utilized the Clip tool to restrict the analysis strictly to the Pittsburgh municipal boundary.

• Applied the Erase tool sequentially to carve out park buffers and FEMA floodplains from the viable growing areas.

Custom Tool Parameterization: Converted the static model into a dynamic, parameterized geoprocessing tool. This strategic setup allowed the suitability model to be instantly re-executed using newly updated data (specifically, swapping in updated 2021 park boundaries) without needing to rebuild the underlying spatial logic.

Objective: Evaluated zoned development capacity data for the City of Seattle to identify specific commercial parcels requiring municipal food service business permit renewals.

Zoning Aggregation: Transformed highly granular, parcel-level data into generalized zoning districts to provide a clearer, macro-level view of the city's land use distribution.

Targeted Identification: Isolated auto-oriented, retail/service commercial areas to streamline the planning commission's permit management workflow.

Data Extraction & Spatial Selection: Executed a Select by Attribute query (ZONELUT = 'C1') to isolate the specific commercial parcels targeted for permit renewal, exporting them as an independent feature layer for emphasis.

Geoprocessing & Statistical Aggregation: Deployed the Dissolve data management tool to aggregate thousands of individual property parcels into nine unified zoning classes (e.g., Commercial, Highrise Family, Industrial).

Dynamic Attribute Calculation: Configured the Dissolve tool's statistical parameters to simultaneously calculate the Mean land value (LAND_AV) for each newly aggregated zoning category.

Cartographic Design & Symbology: Applied Unique Values symbology to differentiate the nine broad zoning categories with distinct colors, ensuring the critical permit renewal parcels were styled in bright red and prioritized at the top of the drawing order. Customized legend properties (adjusting columns and renaming raw field codes to full descriptive labels) to optimize readability.

Objective: Assigned a real-world spatial coordinate system to a historical, unreferenced aerial photograph (NAPP 1988) of Champaign, Illinois, allowing it to accurately align with modern geographic data.

Spatial Alignment: Matched the unreferenced raster image to an established vector reference layer (street centerlines) by identifying common geographic intersections and landmarks across both datasets.

Georeferencing & Transformation: Utilized the Georeference toolset to apply a 1st Order Polynomial (Affine) transformation, mathematically shifting, scaling, and rotating the historical raster into its correct spatial position.

Control Point Creation: Strategically generated multiple pairs of control points (minimum of four), linking distinct, static ground features on the unreferenced image to their exact geographic coordinates on the reference shapefile.

Error Assessment: Actively monitored the Control Point Table to evaluate transformation accuracy, ensuring the Total Root Mean Square (RMS) Error remained below 1 to validate the precision of the alignment.

Raster Export: Executed the Save as New function to permanently lock the spatial modifications, exporting the aligned map as a new, fully georeferenced TIFF file.

Objective: Digitized key university buildings and transit stops to assist the campus Facilities & Services department in evaluating and improving local bus routes.

Feature Creation: Transitioned a static, georeferenced aerial photograph into functional vector data by manually delineating custom shapefiles for specific campus infrastructure.

Shapefile & Schema Creation: Generated new, empty polygon (buildings) and point (bus stops) shapefiles from scratch. Defined a precise local coordinate system (NAD 1983 StatePlane Illinois East) and established custom attribute fields to store textual feature data.

Vector Digitizing: Utilized the Create Features toolset within an active editing session. Employed the Polygon and Right Angle Polygon tools to accurately trace building footprints, and the Point tool to mark exact coordinates for bus shelters based on the underlying aerial imagery.

Attribute Management: Systematically populated the attribute table during the editing process, linking accurate names (e.g., "Armory," "Illini Union") to the newly created spatial geometries to ensure the data was fully queryable.

Cartographic Labeling & Symbology: Applied hollow fills with bright red outlines for buildings to maintain the visibility of the underlying raster basemap. Configured dynamic text labels utilizing a Halo effect and custom placement parameters (e.g., "Horizontal around polygon") to ensure legibility against the complex photographic background.

Objective: Analyzed citywide health and support services for older adults by mapping the spatial relationship between senior population density, free flu clinics, and community senior centers.

Spatial Data Conversion: Transformed raw tabular data and standard street addresses into actionable, geographically accurate vector point features to visualize the distribution of localized healthcare resources.

Coordinate Mapping: Converted flat CSV data for 262 flu clinics into spatial point features using the XY Table To Point tool, plotting them directly via absolute latitude and longitude fields.

Custom Geocoding Workflow: Built a local address locator from scratch using U.S. Census Bureau TIGER/Line street reference data. Executed the Geocode Table tool to translate the physical street addresses of 21 senior centers into spatial points.

Address Rematching & QA/QC: Conducted quality control on geocoding outputs using the Rematch Addresses pane. Manually corrected low-scoring or unmatched addresses (e.g., cross-referencing with external web maps and using the Pick from Map function) to ensure 100% spatial accuracy.

Thematic Cartography: Layered distinct point symbols (medical cross icons and labeled blue squares) over a choropleth base map. Utilized graduated colors with a Natural Breaks classification method to clearly represent the percentage of residents over the age of 65 at the census tract level.

Objective: Mapped the 2007 population density (persons per square mile) at the granular block-group level to analyze historical demographic concentrations within the city limits.

Data Integration: Combined quantitative vector data with a continuous raster basemap to provide real-world geographic context to the demographic patterns.

Granular Data Visualization: Configured a choropleth map at the census block-group level, applying Graduated Colors and Natural Breaks to the POP07_SQMI field to highlight high-density urban corridors.

Data Formatting & Cleanup: Refined the legend properties to restrict raw density calculations to a single decimal place, ensuring the map remains easily readable for a non-technical audience.

Basemap & Boundary Overlay: Integrated an underlying topographic basemap for spatial reference and superimposed the Pittsburgh municipal boundary as a hollow outline to strictly delineate the city's jurisdiction over the density data.

Objective: Highlighted local commercial distribution by mapping specific business locations alongside the major road network within the Point Breeze neighborhood.

Localized Focus: Shifted from a macro-regional view to a micro-neighborhood analysis, detailing the spatial relationship between urban infrastructure and commercial points.

Spatial Navigation: Utilized the attribute table to query and dynamically Zoom To the specific Point Breeze neighborhood record, isolating the view to the target study area.

Predefined Symbology Integration: Applied standardized ESRI symbol sets to point and line features, utilizing "Star 2" for commercial businesses and the "Major Road" style for the street network to establish an intuitive visual language.

Hierarchical Typography: Established a clear visual hierarchy through custom labeling rules, utilizing a larger font size for the neighborhood boundary to act as an anchor, while setting a smaller, secondary font size for individual business names to prevent map clutter.

Objective: Visualized county-level economic data to display the percentage of the population aged 16 and over who are currently employed across the state of Illinois.

Thematic Mapping: Created a choropleth map to clearly communicate demographic and economic trends across broad geographic regions, providing a high-level overview of employment distribution.

Data Classification & Symbology: Applied Graduated Colors symbology using the Natural Breaks (Jenks) classification method (divided into 3 classes) to group the employment percentages based on natural groupings inherent in the data.

Dynamic Labeling: Configured automated feature labeling using the County_Nam attribute field. Enhanced text readability against the varied choropleth background by applying custom typography (Calibri, 8pt) and a 2-point white Halo effect.

Cartographic Layout: Designed a formal map layout utilizing standard map elements, including a title, a kilometer-based scale bar, a north arrow, and a cleanly formatted legend (constrained to one decimal place for professional presentation).

Objective: Established the base geographic context for the City of Pittsburgh relative to its wider county boundary and major hydrological features.

Spatial Hierarchy: Designed a clear, minimalist reference map that prioritizes the visibility of the regional river system cutting through the municipal and county borders.

Layer Management & Drawing Order: Strategically organized the table of contents hierarchy (placing Water Bodies at the top, followed by Pittsburgh, and Allegheny County at the bottom) to ensure smaller or intersecting features were not obscured by larger regional polygons.

Symbology & Transparency: Customized the Allegheny County layer to display strictly as a black outline with a hollow fill, allowing the underlying base data and the full extent of the water bodies to remain visible.

Extent Configuration: Manually adjusted and locked the map frame layout to precisely encompass the full geometric extent of Allegheny County.