From Fragmented Data to Actionable Intelligence: A GIS Case Study

 

Case Study: Building a Centralized GIS Platform for the Cleveland Museum of Natural History Natural Areas

Client: Cleveland Museum of Natural History — Natural Areas Department
Location: Northeast Ohio
Tools: Esri Experience Builder, ArcGIS Online, Survey123, Field Maps, QuickCapture, ArcGIS Pro
Project Type: GIS Systems Design & Implementation | Spatial Analysis | Data Management

The Problem

The Cleveland Museum of Natural History's Natural Areas (CMNH NA) department oversees more than 12,000 acres of ecologically significant preserves across Northeast Ohio — critical habitat for rare plant and animal species under constant threat from invasive species, deer overpopulation, and climate-driven habitat change.

But their data told a different story than their mission. Years of field work, species monitoring, and land management records were scattered across retired devices, local hard drives, hand-drawn notebooks, and inconsistently formatted digital files. Staff relied on institutional memory just to locate basic information. Onboarding new team members was slow. Cross-departmental collaboration was limited. And with over 200 datasets in play, there was no single place to see the full picture.

The department needed a centralized, accessible, and sustainable GIS solution — built on their existing Esri infrastructure — that could consolidate fragmented historical data, streamline ongoing field collection, and support data-driven conservation decision-making.

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My Approach

I worked directly with the Natural Areas GIS staff to design and implement a centralized geospatial data hub using Esri Experience Builder, integrated with ArcGIS Online, Survey123, Field Maps, and QuickCapture.

The project unfolded in three phases:

1. Data Audit and Standardization
The first challenge was bringing order to over 200 datasets spanning decades. I digitized and georeferenced historical aerial imagery, hand-drawn survey maps, and field notebooks. Attribute schemas were harmonized across multiple data collection platforms — resolving format inconsistencies and aligning records to international biodiversity standards (Darwin Core / GBIF) to ensure the data could contribute not just to internal workflows but to the broader scientific community.

2. Spatial Analysis
With clean, integrated data in hand, I conducted hot spot analysis using the Getis-Ord Gi* tool in ArcGIS Pro to identify statistically significant clusters of invasive species across the preserves. Results identified high-density Phragmites infestations at Mentor Marsh as the highest priority, with moderate Spotted Lanternfly concentrations at North Kingsville Sand Barrens and Hemlock Woolly Adelgid presence at Cathedral Woods. These outputs directly informed treatment prioritization and resource allocation decisions.

Getis-Ord Gi* hot spot analysis of Spotted Lanternfly observations across Northeast Ohio preserves

Deer population monitoring data was also integrated and visualized, revealing rising harvest counts at Mentor Marsh correlated with exclosure damage — flagging an area requiring adaptive management response.

3. Experience Builder Hub Development

CMNH Natural Areas Experience Builder hub — centralized access to dashboards, StoryMaps, and data tools

The centerpiece deliverable is a multi-page Esri Experience Builder site functioning as CMNH NA's digital operations hub. It includes:

• Dashboards for invasive species management, deer population monitoring, rare plant tracking, and preserve infrastructure
• StoryMaps presenting preserve-specific conservation narratives for Mentor Marsh, Cathedral Woods, and North Kingsville Sand Barrens
• Live field data integration via Survey123 and QuickCapture, enabling near-real-time dashboard updates directly from field staff
• Data, Tools, and Tutorials pages consolidating the geodatabase, collection forms, and training documentation for staff and volunteers

The platform was designed in compliance with CMNH's institutional branding and underwent multiple rounds of usability testing with Natural Areas staff to refine navigation, widget placement, and accessibility.

Results

• Consolidated over 200 fragmented datasets into a single, standardized, and accessible geodatabase
• Identified and ranked invasive species treatment priorities across three major preserves using spatial hot spot analysis
• Reduced manual data transfer through direct Survey123-to-dashboard integration
• Delivered an operational Experience Builder hub approved by both the Natural Areas department and museum administration
• Established a replicable data governance framework supporting long-term institutional knowledge and staff continuity

Mentor Marsh Preserve Infrastructure web map showing nest box locations, trails, and land management zones

What This Project Demonstrates

This engagement reflects the kind of GIS work I do: taking complex, real-world data challenges and building practical, sustainable systems that actually get used. The CMNH Natural Areas hub isn't a prototype — it's a live operational platform supporting daily conservation decisions across 12,000+ acres of Northeast Ohio.

If your organization is managing fragmented spatial data, building out environmental monitoring workflows, or needs GIS support on a project basis, I'd love to talk.

📧 Stephdigsbones@gmail.com
🔗 Portfolio

📍 Northeast Ohio | Available for remote contract work

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Stephanie Bechard-Smith is a GIS specialist with an MS in GIS Administration, focused on environmental applications, spatial analysis, and Esri platform implementation. Available for freelance and contract GIS projects.

GIS 6005 Lab 6

 In module 6 we covered Proportional Symbol and Bivariate Choropleth Mapping.

For proportional symbol mapping, we started by creating a proportional map using population data for cities in India. We also made a nested legend in our map layout to more efficiently display the proportional symbols.  Next, we were given data for U.S. Job increases and decreases. We separated the data in to increase and decrease layers and then had to use the field calculator to get the absolute value foe the data in the decrease category. This was done because, currently, ArcGIS cannot handle these negative numbers. Once this was accomplished, the data was able to be mapped correctly, and we were able to use color selection and labels to help indicate the increases and decreases.  We also used nested legends in this map for a more efficient, streamlined layout. Pictured below.

For the last part of this lab we a Counties Health layer with various statistics. We focused on Obesity and Physical Inactivity. We started by making a choropleth map for the % Obese field and one for the % Physically Inactive field. We added three new fields to the attribute table for classed data. We changed the symbology for each of the choropleth maps and used Quantile with 3 classes, From here we found the class breaks for both %Obese and % Physically Inactive. Using these breaks we went to the attribute table where we added our new fields and with the help of Select by Attribute and Calculate Field we classed our data in the Class Obese Field (1,2 1nd 3) and the Class Inactive field (A, B and C). Next, we combined the data from these tow fiends into the Class Final field using an Arcade formula. Once this was complete we could create a map layout using unique features symbology and selecting an appropriate color scheme to present the data in a meaningful way. With a bivariate choropleth map this means a color scheme that makes the data relationships easy to discern.

  

GIS 6006 Lab 5

 

In this lab we learned to export and clean data to import into ArcGIS Pro. Once we imported this data table into Pro we did a table join for our data and an existing counties layer. We exported this to a new layer and created two choropleth maps that we used to be the basis for an info graphic. Above is my ending info graphic.  

For my overall design I wanted to make sure that the two  maps were the focal point by positioning them in the center section of the layout. I chose an off-white color that would make the map stand out without being bright white making it easier on the eyes. I also added a dark grey boarder again to pull the viewers focus and set the maps apart. For the title area I went with a darker background and white letters to pull the viewers’ attention – creating good contract and readability. For the supplemental information I went with two different charts, the scatter plot and the bar chart, both are easy to read. The center text panel was taken from a reputable source and gives an overall of the main concepts behind the infographic. The creator, coordinate system and sources were placed in the lower section, centered because the data source is still relevant, and I went with a smaller but still readable text point.

 

GIS 6005 Module 4 Color Concepts & Choropleth Mapping

 The first part of this lab focused on color concepts. We explored the relationship between RGB and HSV color systems and compared the color ramps on ArcGIS Pro with those on ColorBrewer. We were asked to create three different color ramps to explore the differences and better understand the processes used to create them. We selected a color, then created a linear progression ramp, an adjusted progression ramp, and lastly, selected the closest match to the previous ramps on ColorBrewer. Below is a screenshot of each of the ramps, interval information, and notes.

The differences between my linear progression and my adjusted progression color ramps are subtle. The greatest difference being the increased step size between the darkest colors in the adjusted progression ramp which I used to help the map viewer (person) differentiate them. The Color Brewer ramp number were very different than either of my color ramps. The results generated a progression of colors that I felt more defined and easier to distinguish from each other. The RGB values show that Color Brewer varied the steps between each color selection much more than my linear progression or my adjusted progression ramps. The results reflected our text/readings and illustrated just how complicated the relationship is between RGB values when creating color ramps. This lab also showed how difficult choosing the best color ramp can be and how time-consuming custom creation is.

Choropleth Map

The final map we made was one to show the changes in population by county for the state of Colorado from 2010 to 2014.

For a coordinate system, I selected NAD 1983 (CORS96) State Plane Colorado Central FIPS 0502 (US Feet). The State plane coordinate systems are designed specifically for individual states or regions. They minimize distortion within that specific area, making them ideal for these maps where accuracy is important. Colorado Central is one of the three zones within the Colorado State Plane system, optimized for the central part of the state. As this map covers the entire state, I felt it was the best choice. I selected feet instead of meters because the average American is more familiar with feet than meters.

To normalize my data I used Arcade for my formula ((POP2014 – POP2010)/POP2010)*100. This calculation resulted in both positive and negative values. I elected to use a diverging color ramp, along with an odd number of classes (seven), which I felt was best for visualizing the divergent changes in population. For classification, I used a Natural Breaks (Jenks) because this method best depicted the relationship in range of values, as opposed to using the total county counts per class. 

On the map layout I elected to go with the symmetrical classification around zero. I felt communicated the most amount of information to the average view at a glance. I started with five classes but felt that was not sufficient and that seven gave a better picture of the divisions.

GIS 6005

 This lab was all about color. We started by creating self-generated color ramps, both linear and adjusted progression

                            Color Brew

The differences between my linear progression and my adjusted progression color ramps are subtle. The greatest difference being the increased step size between the darkest colors in the adjusted progression ramp which I used to help the map viewer (person) differentiate them. The Color Brewer ramp number were very different than either of my color ramps. The results generated a progression of colors that I felt more defined and easier to distinguish from each other. The RGB values show that Color Brewer varied the steps between each color selection much more than my linear progression or my adjusted progression ramps. The results reflected our text/readings and illustrated just how complicated the relationship is between RGB values when creating color ramps. This lab also showed how difficult choosing the best color ramp can be and how time-consuming custom creation is.

GIS 6005 Mod 3

 Terrain Visualization

In this lab we were provided with elevation and landcover raster datasets. We were given the choice to create either a traditional or multidirectional hillshade from our DEM. I chose multidirectional as it lightened up the entire area and I felt after comparing the two that it gave a better overall impression of the terrain. For the symbology of the landcover layer, I grouped the trees by type name, fir, pine, and so forth and selected shades of green. I used a blue for water areas and a brown for non-forested. This layer was placed on top of the elevation hillshade and given a transparency of 29.5 with a multiply layer blend. I selected these settings because I felt it was the right combination to be able to visually discern the different landcover classes and still be able to make out the different terrain underneath.  

GIS 6002 Module 2 Lab

 

My area of interest  for this lab was the state of Michigan. The state of Michigan has more than one State Plane available; it covers UTM zones 15, 16band, and 17. The reason other options are less appropriate is because Michigan has an unusual shape, extending diagonally from southeast to northwest. The Michigan GeoRef Coordinate System is based on an Oblique Mercator projection, which is specifically designed to minimize distortion for areas with this kind of shape. It covers the entire state, including its territorial waters, in a single zone.