Author: arpichardo

Chapter 7: 

Chapter 7 was one of the most hands-on chapters so far, and I honestly liked that it felt practical instead of just procedural. Learning how to create, edit, and adjust polygon features made GIS feel more interactive. Moving vertices and reshaping buildings took some patience at first, especially when I accidentally selected entire features instead of individual points. Once I got comfortable with snapping and adjusting boundaries, it became much easier and actually kind of satisfying.

Working with CAD drawings and spatial adjustments showed me how GIS isn’t just about viewing data — it’s about improving and updating it. I could see how these tools would be useful in real-world campus planning or city development projects. If a building changes shape or a parking lot is added, these skills would make it possible to update the map accurately.

One thing I noticed, similar to earlier chapters, is that sometimes the wording in the book didn’t perfectly match what I saw in ArcGIS Pro. That caused a little confusion, but I’ve gotten more confident using the search tool to find what I need. Overall, this chapter helped me feel more independent in the software rather than just following instructions step-by-step.

Chapter 8:

Chapter 8 focused on geocoding, which at first seemed straightforward but actually required more attention than I expected. Learning how ArcGIS matches addresses or zip codes to spatial locations helped me understand how tools like Google Maps might work behind the scenes. The idea that there’s a scoring system for matched and unmatched addresses was really interesting.

Creating the locator and working through matched versus unmatched addresses was probably the most challenging part. At times, I had to go back and double-check fields because one small mismatch would cause errors. However, once I understood what the software was looking for, the process made much more sense.

I also liked seeing how the same data could look different depending on the basemap used. It made me think more critically about presentation and how the background layer affects interpretation. While I’m not sure how heavily I’ll use geocoding in my final project, I do think understanding this process is important because it connects tabular data to real-world spatial patterns.

This chapter definitely required careful reading, but it helped me feel more comfortable working with attribute tables and troubleshooting errors.

Chapter 9:

Chapter 9 was probably my favorite of the three. The buffer tools were really cool to visualize. Being able to create proximity zones and adjust the radius made the concept of spatial analysis feel very clear. Seeing the blue buffer circles expand or shrink depending on distance helped me understand how GIS can model real-world impact zones.

Using the Pairwise Buffer tool and creating multiple-ring buffers showed how planners or policymakers might analyze service areas. I started thinking about how this could apply to environmental science, like mapping wildlife hotspots or pollution impact zones. The Network Analyst tools were also interesting because they move beyond simple distance and consider travel routes and accessibility.

One thing I noticed was that changing units (like switching to U.S. Survey Miles) affected the output in ways I didn’t initially expect. That made me realize how important measurement units are in GIS analysis.

Overall, this chapter felt like it tied everything together. Instead of just editing data or matching addresses, we were actually analyzing patterns and relationships. It made GIS feel more powerful and applicable to real-world problem solving.

Chapter 4: File Geodatabases

Chapter 4 focused on working with file geodatabases and managing attribute data within ArcGIS Pro. I learned that a file geodatabase acts as a container that stores feature classes and tables in an organized way. It is more efficient than simply storing shapefiles in folders because it keeps related datasets structured together. This chapter felt more technical than earlier ones, but I can see how important it is for long-term data management.

One of the main skills I practiced was carrying out attribute queries using SQL. Writing expressions in the Select By Attributes tool required precision. If I missed parentheses or used the wrong operator, the query would not run correctly. It reminded me of coding because everything has to be exact. Once I understood the structure better, it became easier to filter specific crime incidents and visualize patterns on the map.

I found the crime data analysis especially interesting. It made me think about how GIS can be used in public safety and urban planning. However, I still want to strengthen my understanding of SQL beyond just following tutorial steps. Overall, this chapter helped me understand how spatial features connect to tabular data behind the scenes.

Chapter 5: Spatial Data

Chapter 5 focused on spatial data and coordinate systems. I learned the difference between geographic coordinate systems (latitude and longitude) and projected coordinate systems, which transform the earth onto a flat surface. Changing projections in ArcGIS Pro helped me see how maps can look very different depending on which system is used.

The world projection exercises were interesting because they showed that every projection has some level of distortion. There is no perfect projection — it depends on what you are trying to preserve, such as area or shape. I also learned more about shapefiles and how they are made up of multiple components (.shp, .dbf, .shx). Understanding this helped clarify why datasets sometimes fail to load properly.

Working with Census TIGER data was one of the more practical parts of the chapter. Downloading and importing external spatial data showed how GIS integrates multiple data sources. This chapter helped me better understand how spatial data is structured and why projections matter for analysis accuracy.

Chapter 6: Geoprocessing

Chapter 6 focused on geoprocessing tools and spatial analysis. This chapter felt more applied compared to the previous ones. I used tools such as Pairwise Dissolve, Intersect, Clip, and Union to manipulate spatial layers. Dissolving block groups into neighborhoods using summary statistics helped me understand how data can be aggregated meaningfully.

I found Select By Location particularly interesting because it selects features based on spatial relationships rather than attribute values. This reinforced that GIS analysis is both spatial and statistical. The exercise involving populations with disabilities and fire company boundaries stood out to me because it showed how GIS can support emergency response planning and equitable resource distribution.

At the beginning of the semester, I did not fully understand what geoprocessing meant. Now I feel much more comfortable navigating the toolbox and using different tools together. While I still rely on the search bar sometimes, my confidence with the software has definitely improved.

Chapter 1 Tutorial Reflection

Chapter 1 was my introduction to ArcGIS Pro, and at first it felt overwhelming due to the number of tools, panels, and data layers involved. I initially struggled with navigating the interface and locating the correct tutorial files, but after rereading the instructions and becoming more familiar with the project structure, the process became much clearer. Once I understood how projects, maps, and data are organized, the software felt far more manageable.

This chapter helped me understand that GIS is more than just map creation—it is a way to organize, analyze, and visualize spatial data. Learning about feature classes, raster datasets, file geodatabases, and projects gave me a better understanding of how environmental data is stored and accessed. Being able to view attribute tables alongside spatial data reinforced the idea that GIS links environmental information, such as land cover or population data, directly to geographic locations.

From an environmental science perspective, these skills are especially important because many environmental problems are spatial in nature. For example, understanding where pollution sources are located, how land use changes over time, or where vulnerable ecosystems exist requires accurate spatial organization of data. The ability to turn layers on and off, switch basemaps, and use bookmarks can help environmental scientists focus on specific regions or environmental factors.

By the end of this chapter, I felt much more confident using ArcGIS Pro and less intimidated by the software. This chapter provided a strong foundation that I can build on in future environmental science coursework and research.

Chapter 2 Tutorial Reflection

Chapter 2 focused on creating thematic maps and working with symbology, which made GIS feel more creative and analytical at the same time. Using zoning and land-use data helped demonstrate how GIS can display multiple variables simultaneously and reveal patterns that are not obvious in raw data. I found it interesting to see how different land-use categories were visually represented and how color choices affected interpretation.

This chapter was particularly relevant to environmental science because land use plays a major role in environmental health. Mapping residential, commercial, industrial, and park areas helped me understand how development patterns can impact ecosystems, air and water quality, and access to green spaces. Thematic maps like these could be used to study urban sprawl, habitat fragmentation, or areas at higher risk for environmental pollution.

Learning how to create choropleth maps and work with census data also has clear environmental applications, especially when analyzing environmental justice issues. For example, mapping households receiving food assistance alongside environmental risk factors could help identify communities that are more vulnerable to environmental hazards. Definition queries were especially useful because they allow data to be filtered without permanently altering datasets, which is important when comparing environmental conditions across regions.

Overall, Chapter 2 showed how GIS can be used as a powerful visualization and analysis tool in environmental science, helping connect human activity with environmental impacts.

Chapter 3 Tutorial Reflection

Chapter 3 emphasized sharing GIS results with broader audiences, which is a critical skill for environmental science. Environmental data is often used to inform policymakers, researchers, and the public, so learning how to present maps clearly and effectively is extremely important. This chapter focused on layouts, charts, and online sharing tools, which helped translate technical GIS work into accessible formats.

I found the layout and chart creation sections to be challenging at first, particularly when arranging map elements and legends. However, these skills are essential for presenting environmental data in reports, presentations, and publications. Being able to create static maps with map surrounds helps ensure that environmental information is communicated clearly and professionally.

Using ArcGIS Online and ArcGIS Story Maps was especially valuable from an environmental science perspective. Story Maps provide a way to combine maps, text, and visuals to explain complex environmental issues, such as climate change impacts or conservation efforts, to non-experts. This is important for public outreach and environmental advocacy.

Overall, Chapter 3 helped me understand how GIS can bridge the gap between data analysis and real-world environmental decision-making. It reinforced the importance of communication in environmental science and showed how GIS tools can be used to share environmental data in meaningful and accessible ways.

P.S. I wasn’t sure if the photos were supposed to be of the final product or just a part of the process so I just snapped photos when I remembered to.

Chapter 4: Mapping Density

This chapter focuses on density mapping and the different ways it can be used to show where features are most concentrated. Density mapping is important because it helps reveal patterns that are not always clear when looking at raw totals alone. Examples discussed in the chapter include mapping workers in a business district or households with children within a specific ZIP code, which helps highlight areas of higher intensity or activity.

The chapter describes two main approaches to mapping density: defined areas and density surfaces. Defined area maps, such as dot maps, are often used with structured data like census information because the boundaries and values are already established. Density surface maps use raster layers or contour maps to create a smoother and more detailed representation of density. Although density surface maps require more effort to create, they are especially useful when identifying subtle spatial patterns that might be missed with defined areas.

Later in the chapter, Mitchell explains how density maps are created by adjusting factors such as cell size, search radius, and units of measurement. The chapter also emphasizes the importance of color gradients and thoughtful design choices to prevent misleading interpretations. Overall, this chapter showed that density mapping is a powerful analytical tool, but its effectiveness depends heavily on the choices made by the analyst.

Key Concepts: Density mapping, defined areas, density surfaces, cell size, search radius, normalization

Questions: How do analysts decide which type of density map is most appropriate for a dataset? How can map design choices unintentionally influence how density patterns are interpreted?

Chapter 5: Finding What’s Inside

Chapter 5 focuses on determining what features are located within specific areas, which is a central function of spatial analysis. The chapter begins by explaining how this approach can be used to identify patterns such as crime hotspots or areas of high conservation value. By analyzing which features fall within defined boundaries, GIS allows for more meaningful comparisons between regions.

The chapter outlines several methods for finding what is inside an area, including drawing areas manually, selecting features within boundaries, and overlapping multiple layers. These techniques allow analysts to examine how different features relate to one another spatially. I found the conservation example especially effective in showing how GIS can be used to prioritize areas based on the features they contain.

Another important idea in this chapter is how areas are visually represented on maps. Showing only an area’s boundary emphasizes borders, while shading or screening an area highlights the space as a whole. These visual decisions can significantly affect interpretation and should match the goal of the analysis. This chapter emphasized how containment analysis supports real-world decision-making.

Key Concepts: Containment, overlay, selection, spatial analysis, boundaries

Questions: How precise do boundaries need to be for containment analysis to be reliable? How can analysts communicate uncertainty when boundaries affect real-world decisions?

Chapter 6: What’s Nearby

Chapter 6 explores how GIS can be used to analyze proximity and determine what is located near a specific feature. The chapter introduces three main methods for measuring proximity: straight-line distance, distance or cost over a network, and cost over a surface. Each method serves a different purpose depending on whether the focus is simple distance, travel time, or accessibility.

One concept that stood out to me was the use of distinct distance bands to show areas of influence around a feature. This reminded me of the Chicago School model of urban structure, which also uses distance to explain spatial organization. While the comparison is not exact, both approaches rely on distance as a way to interpret spatial patterns.

The chapter also discusses spider diagrams, which visually connect a single feature to multiple locations. This makes it easier to see whether a feature is within range of several important points. Toward the end of the chapter, Mitchell explains the importance of limiting the number of mapped features or using clear symbolization to avoid confusion. Overall, this chapter showed how proximity analysis helps turn spatial data into practical insights for planning and analysis.

Key Concepts: Proximity analysis, straight-line distance, network distance, cost surface, spider diagrams

Questions: When is straight-line distance insufficient for proximity analysis? How do analysts decide which proximity method best fits a real-world problem?

Chapter 1: Geographic Thinking and GIS Analysis

Chapter 1 really opened my eyes to the bigger picture of GIS. I had always thought of GIS as just software for making maps, but Mitchell emphasizes that it’s really a way of thinking about the world spatially. The chapter introduces geographic thinking, which is about considering location, proximity, and spatial relationships when analyzing any data. It made me realize that the “where” is often just as important as the “what.” GIS is not just a tool; it’s a framework for asking meaningful questions, and the software is just one way to explore the answers.

Another key point was the idea of scale and how it affects patterns. What looks like a cluster at one scale can appear completely different at another. This made me reflect on how careful we need to be when interpreting maps—seeing a pattern doesn’t automatically mean something significant is happening. I also appreciated the discussion on vector and raster models, even though raster still feels a little tricky to wrap my head around. Vector models, using points, lines, and polygons, felt more intuitive, especially for plotting discrete events like crime locations or schools.

Overall, this chapter helped me see GIS as more than just technical steps; it’s a mindset. Thinking geographically forces me to consider relationships I might otherwise ignore, like how environmental factors relate to population density or how distance influences access to resources. I’m curious to see how this perspective will shape the way we approach actual map creation in class, and I wonder how geographic thinking can help tackle complex problems when data is incomplete or messy.

Key Concepts: Geographic thinking, GIS analysis, spatial patterns, scale

Questions: How do analysts avoid bias in interpreting spatial patterns? How does scale influence the conclusions drawn from GIS data?

Chapter 2: Understanding Geographic Data

Chapter 2 shifted my focus from thinking about GIS conceptually to thinking about the actual data that feeds it. Mitchell explains that understanding data is just as important as knowing the software because poor data choices lead to misleading results. The distinction between vector and raster data was useful. Vector data feels more tangible—points, lines, and polygons that represent features like roads or buildings—while raster data is more abstract, representing continuous surfaces like elevation or temperature. I think I’ll need to practice with raster more to feel comfortable using it in analysis.

Attribute data also stood out to me because it shows that location alone isn’t enough. For example, plotting all the schools in a city is informative, but adding enrollment numbers or funding data allows for meaningful comparisons. I was surprised at how many factors affect data quality—accuracy, resolution, completeness—and how each one can influence the results. It made me appreciate how critical it is to assess the data before running any analysis.

I also liked the practical examples in this chapter about choosing the right data for a map’s purpose. A city council zoning map needs different detail than a map showing air pollution trends, and understanding these differences is key to making effective, useful maps. This chapter made me think more critically about the data we’ll use in GIS assignments and how important it is to know both the strengths and limitations of each dataset.

Key Concepts: Vector data, raster data, attribute data, data quality

Questions: How do analysts decide which data model works best for a project? How can low-quality or incomplete data be handled responsibly in analysis?

Chapter 3: Exploring Geographic Patterns

Chapter 3 felt the most practical and immediately applicable of the three. Mitchell dives into identifying and interpreting geographic patterns, like clustering, dispersion, and trends. What really stood out to me was the idea of “most and least”—using maps to show where the most or least of something occurs. This seems simple, but I can see how it would be incredibly powerful in fields like public health, urban planning, or environmental monitoring. I was also struck by how often statistics are intertwined with map-making, which reminded me of my high school stats class and the maps we used to analyze datasets.

A major takeaway was the difference between maps designed for analysis versus maps designed for communication. Analytical maps might include more detail for exploring data, while presentation maps should simplify the information to prevent overload. I thought this was a helpful reminder that GIS isn’t just about plotting data; it’s about thinking critically about your audience and how information is presented. The chapter also emphasized the importance of revising maps and being selective about what to include, which makes me realize how iterative the map-making process really is.

I found myself reflecting on the ethical implications of maps. Since patterns can suggest relationships that aren’t necessarily causal, it’s important to be honest about what a map can and cannot show. This chapter made me excited to start creating our own maps while keeping in mind both accuracy and clarity.

Key Concepts: Clustering, dispersion, trends, exploratory spatial analysis

Questions: How can uncertainty be effectively communicated on maps? What ethical responsibilities should map creators consider when visualizing sensitive data?

1. Introduction

Hello, my name is Andrea Pichardo, and I am an ENVS student interested in understanding how geography and technology intersect to explain real-world issues. I am especially interested in how spatial data can be used to analyze social and environmental patterns, such as access to resources, environmental impacts, and community-level change. I am new to Geographic Information Systems, but I am excited to learn how GIS tools are used across different fields and how they influence decision-making in everyday life.

2. Comments on Schuurman:

In Chapter 1, Schuurman explains that GIS is much more than a tool for making maps. One idea that stood out to me is that GIS does not have a single, fixed identity. Instead, its meaning changes depending on who is using it and for what purpose. For some people, GIS is mainly software that helps organize spatial data, while for others it represents a scientific way of thinking about space and spatial relationships. This made me realize that GIS is not just technical, but also conceptual and interpretive.

I also found the distinction between GIS as a system and GIS as a science especially interesting. GISystems focus on practical tasks such as urban planning, transportation routing, or managing infrastructure. GIScience, on the other hand, asks deeper questions about how spatial data is created, categorized, and analyzed. Schuurman emphasizes that decisions like where boundaries are drawn or which data is included can significantly affect results. This challenged my assumption that GIS outputs are always neutral or objective, and it made me more aware of the role human choices play in shaping geographic information.

Another important theme in the chapter is visualization. Schuurman explains that maps and spatial images allow people to see patterns that might not be obvious in tables or written descriptions. The example of John Snow’s cholera map shows how visualizing data spatially can lead to meaningful insights and real-world change. At the same time, she points out that visualizations can oversimplify complex situations if users do not critically examine the data behind them.

Overall, this chapter helped me see GIS as a powerful tool that influences how we understand space, make decisions, and interpret the world, rather than just a technical mapping skill.

3. GIS Application Areas

Application 1: Crime Mapping and Public Safety

GIS is widely used in crime analysis to map crime incidents, identify hotspots, and allocate police resources more effectively. Law enforcement agencies use GIS to analyze spatial patterns of crime over time, helping them predict where crimes are more likely to occur and develop targeted prevention strategies. Crime maps also allow communities and policymakers to better understand safety concerns and evaluate the effectiveness of interventions.

Map/Image:

Sources:

•National Institute of Justice, GIS and Crime Mapping

•Chainey & Ratcliffe (2005), GIS and Crime Mapping

Application 2: Environmental Monitoring and Conservation

GIS plays a crucial role in environmental science by helping researchers monitor ecosystems, track land-use change, and manage conservation efforts. For example, GIS is used to map wildlife habitats, analyze deforestation, and assess the impacts of climate change. By layering environmental data such as vegetation, elevation, and human activity, GIS allows scientists to make informed decisions about conservation planning and resource management.

Map/Image:

Sources:

•ESRI, GIS for Environmental Management

•Turner et al. (2001), Landscape Ecology in Theory and Practice

4. Was the quiz completed?

Yes.