Author: jmmarzulli

For my final project, I used ArcGIS to look at how easy it is for people in Delaware County, Ohio to get to a public park. I wanted to find out how many houses are close enough to a park to walk there, using a half-mile distance. I used data from the Delaware County GIS website and tools from Chapters 1 to 4 to do this.

Data I Used:

• A map of public parks in Delaware County
• A map of roads in the county
• A list of address points (homes)
• Zoning or land use maps if needed

Steps I Took and Tools I Used:

1. Adding the Data and Styling It:
   • I downloaded the data from the Delaware County GIS Hub and added it to ArcGIS Pro.
   • I colored the parks green and added roads and house points on top so everything was easy to see.
2. Making Buffers Around Parks:
   • I made a buffer around each park that was 0.5 miles wide to show where people could walk to.
3. Selecting Homes Inside the Buffers:
   • I used the Select by Location tool to find out which homes were inside the buffer areas.
4. Using Attribute Queries and Joins:
   • I filtered the data to look at just residential homes.
   • Then I used a spatial join to count how many homes were within walking distance of a park.
5. Making the Final Map:
   • I created a final map that included a title, legend, north arrow, and scale bar.
   • I made sure the colors and labels were clear and easy to read.

What I Found Out: I found that a lot of homes in central and southern Delaware County are within walking distance to a park. But some of the rural areas up north don’t have as many nearby parks. This could be helpful for planning where new parks should go.

Conclusion: This project helped me understand how GIS can be used to solve real-world problems. By using buffers, joins, and queries, I was able to find out who has access to parks in the county. In the future, it would be cool to add more info like population or public transport to see how that changes the results.

 

Building upon the insights gained from Chapters 5–7 , I developed the Delaware Environmental Justice Explorer, an application designed to identify and visualize areas in Delaware where environmental burdens intersect with vulnerable populations. This tool aims to assist policymakers, researchers, and community members in understanding and addressing environmental justice concerns within the state.

 Application Features

• Interactive Mapping Interface: Users can explore various layers, including locations of industrial facilities, waste sites, air and water quality metrics, and demographic data such as income levels and racial/ethnic composition.

• Analytical Tools: The application provides tools to identify areas where high pollution levels coincide with vulnerable populations, enabling targeted interventions.

• Community Engagement: Users can submit observations or concerns about environmental issues in their communities, fostering community involvement and data accuracy.

 Technical Implementation

• Data Sources: The application utilizes data from the Delaware Department of Natural Resources and Environmental Control (DNREC), the U.S. Census Bureau, and the Environmental Protection Agency’s EJScreen tool.

• ArcGIS Online Capabilities:
  • Smart Mapping: To dynamically symbolize data based on attribute values, enhancing visual interpretation.

  • Web AppBuilder: For creating a user-friendly interface with customizable widgets.

  • Analysis Tools: Spatial analysis to identify overlap between environmental hazards and vulnerable populations.

  • Story Maps: To provide narratives explaining the significance of the data and findings.

 Use Case Scenario

A policymaker interested in addressing environmental justice can use this application to identify communities in Delaware that are disproportionately affected by environmental hazards. By analyzing the overlap between pollution sources and socioeconomic data, the policymaker can prioritize areas for intervention and allocate resources effectively.

This week  It focused on using smart mapping and how to make your web maps more interactive and useful. I learned how you can use different styles and settings to better show your data, like changing symbols based on a number field, using color ramps, and adjusting pop-ups so they show only what you need.

One thing I liked about this chapter was how easy it made the maps look once you knew what tools to use. It showed me how much better a map looks when you adjust the symbology and make it more visual for the user. I also liked learning how to make pop-ups cleaner and more helpful so people only see the important info.

One thing I found tricky was deciding which type of symbol or color ramp to use. Sometimes it looked cluttered if I picked the wrong one. I also had to play around with the labels to make sure they didn’t overlap. 

For my application, I used the Delaware County data from Geog 291 to create a smart map that shows where public parks are and how many houses are nearby each park. I made the parks stand out by symbolizing them based on the number of homes within a half-mile. I used different shades of green; darker green meant more nearby homes. I also changed the pop-ups so they only showed the park name and number of houses close by. This map could help city planners see which parks are busy and where new ones might be needed.

This week was all about spatiotemporal data and real-time GIS. I learned how time can be added as another layer of information on maps and how tile layers help maps load faster and run smoother. It also introduced Web GIS setups that can run on private servers (on-premises), which could be useful for organizations that don’t want to use cloud-based services.

One thing I found interesting was how tile layers work like pre-drawn map pieces that get loaded quickly. It reminded me of how Google Maps doesn’t lag when you zoom in and out. That’s because it’s using tile layers. I also thought it was cool how real-time maps can be made with data that changes constantly, like traffic or weather.

Something that confused me a little was how to work with time-enabled data. I got the idea, but I’d like to see more examples of how to add time sliders and show change over time on a web map.

Using the Delaware County data from Geog 291, I created a map that shows road construction projects over time. Each project has a start and end date, and I used time-enabled symbology so you can slide through the timeline and see which roads were under construction in each month. I used tile layers to make sure the basemap loads fast even when the time slider is moving. This could help people avoid traffic or plan better routes based on construction schedules.

Chapter 4 is about mapping density, which is useful when analyzing areas of different sizes. Density maps help show patterns rather than individual points or connections. There are two main ways to create a density map. The first method is by using defined areas. This is a quick and easy way to display data that has already been summarized. However, it’s not the most detailed method since it doesn’t come directly from raw data. If extra detail isn’t necessary, this method is a great way to visualize patterns. The second method is by using a density surface. This approach is more detailed but requires a lot more data input since it doesn’t use pre-summarized data. It looks similar to raster models because it uses layers and cells. It’s also possible to switch between the two methods by assigning values to summarized maps. Factors like cell size, search radius, calculation methods, and units impact how the final map looks.

Chapter 5 focuses on taking a closer look at maps to understand how different features, values, and layers work together. It also revisits the idea of discrete versus continuous values. Discrete values are unique and identifiable, like locations or addresses. Continuous values can be numerical or categorical, but they vary across an area.

This chapter also explains different ways to study areas and features. One way is by looking at the overall areas and features, which gives a quick visual representation but doesn’t provide specific data points. Another way is by selecting inside an area, which gives precise information about that space but doesn’t help with anything outside of it. Lastly, overlaying methods combine multiple layers of data to create a more detailed view. This method is useful but requires a lot of data input.

Chapter 6 begins by discussing the difference between mapping by distance versus cost. Distance mapping is usually enough, but it’s not always the most detailed option. Cost mapping considers travel expenses and effort, making it more precise but also more complex. This fits with a common theme in the book: more detailed methods require more data and effort.

The chapter also introduces planar and geodesic mapping. Planar mapping assumes the Earth is flat, which works for small areas. However, for larger areas, geodesic mapping is needed to account for the Earth’s curvature.

Different methods can be used to analyze distance within a map. District bands help compare distance with other characteristics, while inclusive rings show how totals increase as distance grows

Creating buffers is another important concept. Buffers define boundaries around values, helping to highlight edges and centers. The rest of the chapter focuses on how to apply these methods in real-world mapping. I’m curious to see how all of this will come together when we start working through tutorials and applying what we’ve learned.

Chapter 1- This chapter introduced me to using ArcOnline, which was a different experience compared to what I had learned in Geog 291. At first, I was able to follow along easily, but as I got further into the chapter, I ran into challenges when working with data layers. One of the biggest issues was figuring out how to properly format my data so that it would display correctly on the map. I had to go back and double-check my work multiple times before it finally looked right.

Another part that I found difficult was understanding how to adjust the symbology settings to better represent the data. I wanted to make the map more visually clear, but I struggled to find the right colors and symbols that would best display the information. After experimenting with different options, I started to get the hang of it. I realized how important these small details are in making a map both informative and easy to read.

Chapter 2- Going into this chapter, I was feeling more confident, and overall, things went more smoothly. One of the first tasks was working with attribute tables, which I found really helpful in organizing and understanding the data. Being able to filter and sort information within the table made it much easier to see patterns in the dataset.

A challenge I faced in this chapter was trying to properly configure labels for the map. I wanted certain features to stand out, but some of the labels were either too small or overlapping in a way that made the map look cluttered. After adjusting the settings multiple times, I was finally able to make the labels clear and readable.

By the end of the chapter, I felt a lot more comfortable with these tools, and I started to see how all the different elements—layers, symbols, labels, and attribute tables—come together to create an effective map. I’m looking forward to applying what I learned to more complex projects in the future.