Author: ecdahlstrom

Chapter 7

In this chapter, I learned all about the different methods of digitizing. I first was introduced to the process of moving, rotating, splitting, and adding more vertices to polygons on a map. Although this process was simple, I appreciated that it led to a cleaner look and allowed the polygons to stand as a more accurate representation of the locations on the map. The next tutorial focused on adding and deleting a polygon feature class. Although I did not have any problems with the guided tutorial, I struggled with the “your turn” section. After a while of retracing my steps, I eventually figured out that I forgot to change the feature class I was adding to point instead of polygon. This meant that I wasn’t allowed to use the correct symbology or data format. Sometimes I wish in situations like these the chapter would give us links for previous instructions or reminders if we forgot how to do a process. I then learned about the steps of how to smooth polygons. This process further contributed to a more professional looking map. The last tutorial taught me how to use the georeference and transformation tool on polygons. I found this section to be the hardest in this chapter because there were many steps that had to be performed correctly in order for the function to work. For example, I had trouble with the transform polygon feature. When setting the output coordinate system for one of the feature classes, I accidentally changed the wrong one. This meant that I could not link the layers to each other because they did not have corresponding coordinate systems. This mistake emphasized the importance of making sure to follow and pay attention to the little things when working with GIS.

Chapter 8

Chapter 8 focused on the definition and the process of geocoding. Geocoding takes data in the form of text, such as street addresses or zip codes, and plots them as geographic coordinates on a map. Although there are components that are applied in order to make this process as accurate as possible, there are still some problems that come with geocoding. One issue is that there are often misspellings, abbreviations, or omissions of names causing confusion when matching the sources to reference data. GIS combats this by calculating a percent accuracy of each source through the subtraction of points for each problem that occurred. Although there is no way to truly judge if matches are correct, this process is able to review some of the nonmatched sources and incorrect matches to improve the reliability of geocoding for the many organizations that rely on it today. 

The first tutorial introduced me to how to geocode data using zip codes. This included the process of building a zip code locator, establishing geocode addresses, and rematching data. This type of mapping would be extremely useful in seeing where the majority of an organization or event population resides. The second tutorial walked me through a similar process, but this time I geocoded data for street addresses. For me, this chapter was probably the simplest and quickest one so far. Although there were not that many tutorials to work on, the chapter introduced a large amount of necessary background information about geocoding.

Key Concepts/Definitions

Geocoding: GIS process that matches location fields in tabular data to corresponding fields in existing feature classes to map the tabular data.

Chapter 9

This chapter marked the beginning of the last section of the book: applying advanced GIS technologies. It focused on the idea that even when you have the right dataset for a subject, sometimes visualization of spatial data is needed to answer questions and solve problems. The chapter covered four different spatial analysis methods that included buffers, service areas, facility location models, and clustering. Buffers are generally used to find what’s near a feature or to see how many people are located within a specified distance of a feature. The buffer map was my favorite way to visualize data from this chapter. To me, this method provided the most accurate visual representation of the differing spatial data. Service areas are like buffers but instead of distance from a feature, it is based on travel over a network. The facility location model uses Network Analyst to find the best locations for new features such as facilities. This map was interesting to look at because the representation of the data was presented in a way I have not seen before. Lastly, the clustering method focuses on the ability to identify clusters of data points that are closer to each other but distant from other clusters. Although I was able to complete this section, my results were not exactly lining up with the tutorials. I was getting all of the same numbers for frequency and mean, but my cluster ids did not match. As a result, my symbology for the groups also did not match. I do not know if this is a problem that happened on my end or if it was just the fact that ArcGIS Pro is now on a slightly different version than the book I currently use.

Key Concepts/Definitions

Buffer: A polygon surrounding map features of a feature class.

Chapter 4

This chapter marked the introduction of working with spatial data. The tutorials first walked me through how to interact with a project’s geodatabase. This included the process of inputting outside information into the project, such as a table, how to use the database utilities in the catalog pane, and how to join attribute tables. Although this tutorial was simple, the method was extremely useful to learn since many future projects will likely require the addition of outside data. In addition to joining attribute tables, I was introduced to other functions related to attribute tables such as how to export a feature class, add a field, calculate the sum or percentage of fields, and how to extract substring fields. Although I did initially run into a few problems when working with the Python language, I was able to fix them by quickly retracing the steps. By the end of the chapter, I felt that I had a good understanding of how to use Python. The chapter then shifted to how to carry out attribute queries. Attribute queries are useful for finding geographic features based on their descriptive properties rather than their geographic properties. I found this function to be extremely useful because when you have a large dataset to work through, narrowing down the data based on attributes makes the process easier. Lastly, I learned how to aggregate data and create a point layer. Both of these processes allow for comparison between features and data to be displayed on the map.

Key Concepts/Definitions

Database: A container of the data for an organization, project, or other events for record keeping, decision-making, analysis, and research. A geodatabase has a file folder that contains .gdb at the end.

Chapter 5

Chapter 5 contained information all about the geographic coordinate and map projection systems in GIS. The first tutorials walked me through how to change the map projection, or the flatness or curvature of the map, for a variety of different setups. For example, I practiced changing the map projection on a world map and on a map of the United States. Although I appreciated the lesson on this, these tutorials did not seem as useful to me as some of the previous ones. Additionally, since we worked with larger scale maps, I wondered if these various map projections would also be beneficial when working with maps for specific locations. Other skills I learned throughout the tutorials include how to navigate and use the coordinate systems tab in properties, import a shapefile into the geodatabase to be added to the map, add data to the map from ArcGIS Living Atlas, and the different spatial reference systems. The use of different tools was also emphasized a lot in the chapter. After the tutorials, I feel much more comfortable with searching for and using the different functions of the tools. This chapter, however, was probably the first one that I struggled with a few of the tutorials. In one section, the chapter assumes that you have had some previous work with Microsoft Excel. Although I am somewhat familiar with the program, I struggled with this part because I do not know all of the proper functions of Microsoft Excel. I also had an issue with downloading the elevation contours in the sixth tutorial. The book was not up to date with the new layout of the website, so I had a hard time downloading the file in the proper format. Even with the difficulties however, I was able to complete all of the tutorials in the chapter correctly.

Chapter 6

This chapter was focused on geoprocessing and the tools it uses to process data in GIS. Throughout the tutorials, I learned how to use geoprocessing tools such as pairwise dissolve, clip, merge, append, pairwise intersect, union, and tabulate intersection. The pairwise dissolve tool can aggregate block group attributes to other feature levels using statistics such as sum, mean, and count. The clip tool allows you to select and remove features outside a specific boundary such as streets. Clip is often paired with the extraction of features to make a study area. A study area allows you to focus on one particular section of a larger map. This function will be useful when analyzing the statistics and structure of a subarea while comparing it to the entire map. By clipping the excess features, the tool gives the study area map a cleaner finish. The merge tool allows you to merge multiple separate feature classes together into one. Merge is useful when you want to display similar features, but they are not the main focus of your project. The append tool adds features to an existing feature class, considering that both have the same attributes. The pairwise intersect tool creates a feature class combining all of the features and attributes of two input or overlaying feature classes. The union tool overlies the attributes of two input polygon layers to generate a new output polygon layer. This results in the comparison of two attributes in the same area such as neighborhoods and land usage. Finally, the tabulate intersection makes apportionments proportional to the areas of split parts of polygons and assumes that the populations of interest are uniformly distributed by area within polygons. Overall, I appreciated the setup of this chapter since each tutorial served as an introduction to a new tool in geoprocessing. 

Key Concepts/Definitions

Geoprocessing: A framework and set of tools for processing geographic data. Builds study areas in a GIS and performs tasks.

Delaware Data Inventory

Address Point: This data set is a spatially accurate representation of all certified addresses within Delaware County, Ohio. The address point indicates the location of the building’s center. This data’s intended use is for appraisal mapping, 911 emergencies, accident reporting, and disaster management. The data is updated daily, but published to the site monthly.

Annexation: Contains Delaware County’s annexations and conforming boundaries from 1953 to present. The data is updated on an as-needed basis and is published monthly.

Building Outline 2024: Consists of building outlines for all structures in Delaware County, Ohio. The data was updated in 2024 and on an as-needed basis.

Building Outline 2023: Consists of building outlines for all structures in Delaware County, Ohio. The data was updated in 2023 and on an as-needed basis.

Condo: Consists of all condo polygons within Delaware County, Ohio that have been recorded. 

Delaware County E911 Data: This data set is a spatially accurate representation of all certified addresses within Delaware County, Ohio. It is used for the State of Ohio Location Based Response System (LBRS). The data is updated on a daily basis, but published monthly.

GPS: Identifies all GPS monuments that were established in 1991 and 1997. The coordinates are in Universal Transverse Mercator Northing and Easting. The data is updated on an as-needed basis and published monthly.

MSAG: The Master Street Address Guide (MSAG) is a polygon feature set of the 28 different political jurisdictions such as townships, cities, and villages that make up Delaware County. The data set was created to facilitate the process of locating the boundaries of the different political jurisdictions. The data is updated on an as-needed basis and published monthly.

Municipality: This data set consists of all of the municipalities in Delaware County, Ohio. 

Parcel: The data set consists of polygons that represent all cadastral parcel lines within Delaware County, Ohio. All changes to this data set are represented by recorded documents stored at the Delaware County Recorder’s Office. The data is updated on a daily basis and published monthly.

Precincts: This data set consists of voting precincts in Delaware County, Ohio. Precincts are polygons that determine each voting precinct boundary. This data is The data is updated on an as-needed basis and published as-needed by the Delaware County Board of Elections.

Recorded Document: This dataset consists of points that represent recorded documents such as vacations, subdivisions, centerline surveys, surveys, annexations, and miscellaneous documents within Delaware County, Ohio. Created to facilitate the process of locating miscellaneous documents. The data is updated on a weekly basis and published monthly.

School District: This data set consists of all school district counties within Delaware County, Ohio. The data is updated on an as-needed basis and published monthly.

Street Centerline: The State of Ohio Location Based Response System depicts the center of pavement of public and private roads within Delaware County, Ohio. Address range data was collected by field observation of existing address locations and by adding addresses using building permit locations. This data set is a spatially accurate representation of the road system. The data is intended to support appraisal mapping. 911 emergencies, accident reporting, disaster management, and roadway inventory. This layer is updated on a daily basis while the 3D layer is updated on an annual basis. The data is published monthly.

Subdivision: This data set consists of all subdivisions and condos within Delaware County, Ohio. The data is updated on a daily basis and published monthly.

Survey: This data set is a shapefile of point coverage that represents surveys of land within Delaware County, Ohio. Points represent the location of the survey platform. The data is updated on a daily basis and published monthly.

Tax District: This data set consists of all tax districts within Delaware County, Ohio. Data is dissolved on the Tax District code. The data is updated on an as-needed basis and published monthly.

Zip Code: This data set contains all zip codes within Delaware County, Ohio. The zip code layer was created in 2025 by dissolving all Delaware county parcels by their property addresses. This data set was also used to populate the zip right and zip left attributes for Delaware County’s road centerline. The data is updated on an as-needed basis and published monthly through the United States Postal Office.

Map Sheet: This data set consists of all map sheets whiting Delaware County, Ohio.

PLSS: This data set consists of all Public Land Survey System (PLSS) polygons in both the US Military and the Virginia Military Survey Districts of Delaware County, Ohio. Was created to facilitate in identifying all of the PLSS and their boundaries. The data is updated on an as-needed basis and published monthly.

Farm Lot: This data set consists of all of the farm lots in Delaware County, Ohio. Was created to facilitate in identifying all of the farm lots and their boundaries. The data is updated on an as-needed basis where surveys are recorded.

Township: This data set consists of 19 different townships that make up Delaware County, Ohio. The data is updated on an as-needed basis and published monthly.

Dedicated ROW: This data set consists of all of the lines that are designated Right-Of-Way within Delaware County, Ohio. The line data was created through Delaware County’s Parcel data. The data is updated on an as-needed basis and published monthly.

Railroads: This data set allows a user to view the locations of railroads in Delaware County, Ohio.

Building Outline 2021: Consists of building outlines for all structures in Delaware County, Ohio. The data was updated in 2021 and on an as-needed basis.

Hydrology: This data set consists of all the major waterways within Delaware County, Ohio. The data is updated on an as-needed basis and published monthly.

Delaware County Contours: 2018 Two Foot Contours for Delaware County, Ohio in a file geodatabase format.

Original Township: This data set consists of the original boundaries of townships in Delaware County, Ohio. Data was before tax district changes affected their shape.

Preface

Based on the preface, it seems like this book will walk us through how to use ArcGIS Pro step by step. Additionally, we will also learn methods in ArcGIS online, ArcGIS StoryMaps, and other ArcGIS software. Specifically with ArcGIS Pro, it will be teaching us how to use the GIS application to create, use, and analyze professional 2D and 3D maps. The goal of reading this book is to learn how to effectively use GIS to integrate it into and address real-world problems. I appreciate the layout of the chapters since it first explains the concepts and then allows you to do the tutorial hands on. After reading this book, I hope to have enough knowledge of GIS to use it in potential research and possibly my future career.

Chapter 1

Chapter 1 served as an introductory course on how to use a variety of basic navigations and tools in GIS. One of the most fundamental tools introduced in this chapter was the use of the contents page. The contents page allows you to add and remove different features or layer classes and is, as I learned later, an important hub for editing for features. Some map making features that were introduced in this chapter include how to add basemaps, how to change symbols, and how to add, remove, and label feature classes. I also learned all about map viewing and zooming. Knowing how to zoom in and out, change the viewing zone, zoom into specific features, create bookmarks, zoom to full extent, and search for specific features will all be useful tools for analyzing maps in the future. I also learned how to export a layout and turn the map into an image with a photo viewer. This skill is useful when I need to share my map and make it accessible to other people. Overall, I liked how the chapter gives you straight forward instructions on how to find and do things so it is easy to navigate. Throughout the chapter, I definitely started to realize that I remembered how to work and locate different features in GIS. However, I found the tutorials a little bit repetitive, as you often do a skill and then delete the work to reset.

Key Concepts/Definitions

Feature Class: Basic building block for displaying geographic features on a map. A homogeneous layer on the map. Vector data that have corresponding attributes for each feature.

Raster: Type of spatial data. An image made up of small pixels. A raster encoded with geographic information can be used as a layer in a map.

Chapter 2

In this chapter, I learned all about how to create, design, and symbolize thematic maps. I was first introduced to symbology, or the visual elements to represent geographic features or attributes. From this, I was able to learn how to change the colors of polygons, symbolize ground features, and make graduated and proportional symbols. Symbology was a very important learning concept throughout this chapter. After these tutorials, I now feel comfortable with implementing symbol changes and where to go to do so. I was also introduced to the process of making two different types of thematic maps: choropleth and dot density. I enjoyed these types of maps because it was easy to see how the values were distributed. I also think that while the 3D choropleth map is not a necessity, it adds another element that helps display features. Additionally, while making these maps, I learned how GIS creates histograms based on the data and how they can be altered. This skill will be useful to know when analyzing the map through statistical analysis. Other important skills I learned in this chapter included how to label features, remove duplicate layers, remove pop ups for features, set visibility ranges, and how to create a definition query.

Key Concepts/Definitions

Thematic Map: Strives to solve or investigate a problem. Consists of a subject layer or layers placed in spatial context with other layers. To make this map you must answer: What layer or layers are needed to represent a subject? What spatial context layers are needed to orient map users to recognize locations and patterns of the subject feature?

Definition Query: Used to filter the features of a layer rather than select a temporary subset of features to work with.

Choropleth Map: Uses colors in polygons to represent numeric attribute values. Uses classification methods, that depend on the data and intent of the map, to display the data.

Map Scale: Ratio between distance A and B (one inch) on your computer screen divided by the distance between the two same points in inches on the ground.

Chapter 3

This chapter was all about making suitable maps to present and how to share your maps with people beyond ArcGIS Pro. The first section of this chapter was how to make a layout. Layouts are mostly used to share information in formal settings such as presentations or reports. The layout contained the map, a title, and a legend. When making the layout, I especially appreciated the ruler and guideline features of GIS. These features ensured the maps were even and gave them a professional look. From these layouts, I learned how to create different charts from the maps and how to highlight certain subsets of features from the charts. For example, in one of the tutorials I highlighted the top ten states with the highest arts employment. 

The rest of the chapter focuses on the online sections of ArcGIS. I first learned how to publish a map I made on ArcGIS Pro to ArcGIS Online. Publishing to ArcGIS Online is extremely useful in sharing your map with a wider audience since it is more accessible than a desktop and ArcGIS Pro. I then received a tutorial on how to create a story through StoryMaps that showcases the data and reason behind a map. To me, StoryMaps would be the most useful for sharing with the general public because the story provides context and instructions on the meaning of the map. I particularly enjoyed the inclusion of accessibility features and sidecar blocks in StoryMaps. These features made the story easy to navigate and further expanded the range of audience. I then learned how to create a briefing. The information was similar to the story section, but formatted the information in a way that would be useful for a presentation. Lastly, I was introduced to the concept of creating a dashboard. Dashboards are useful tools for allocating resources in response to changing goods or services over time.

Chapter 4

In this chapter, I learned about another way to map data through GIS called mapping by density. You should map by density when you are looking for patterns of individual features or mapping with areas of different sizes because it allows you to see where features are concentrated. There are two ways of mapping density: by defined area and by density surface. 

You should map by defined area if you want to compare areas with defined borders. Although GIS can calculate the density of each area for you, it is important to ensure all feature units match. Shaded fill maps or dot maps are common ways to display density maps defined by area. If you want to see the concentration of points or line features, however, you should map by density surface. When mapping by density surface, there are several parameters that affect how GIS calculates density surface. Cell size determines how coarse or fine the patterns will appear and search radius affects how generalized the patterns in surface density will be. The two ways GIS can calculate the density are the simple method and the weighted method. Overall, I learned how to efficiently create an effective density map through the use of GIS.

Key Concepts/Definitions

Shaded fill map: Uses a range of colors to display density. Density is treated as a ratio. Density value applies for the entire polygon, the actual density at a specific location may vary.

Dot Density Map: Each dot represents a specified number of locations. The dots are randomly distributed, so they do not represent the actual feature locations. The closer together the dots are, the higher density of features in that area. 

Simple Calculation Method: Counts only the features within the search radius of each cell. Results in a series of rings that overlap each other.

Weighted Calculation Method: Gives more mathematical weight to the features closer to the center of the cell. Every cell in the layer is counted and assigned a value. Results in a smoother, more generalized density surface.

Chapter 5

This chapter introduced me to all of the information I needed to know about mapping what’s inside. This type of mapping is used to monitor what is occurring inside of an area or to compare several areas based on what’s inside each. GIS can find out whether an individual feature is inside an area, list all the features inside an area, find out the number of features in an area, or get a summary of what’s inside a boundary based on a feature attribute.

The three ways of mapping what’s inside include drawing areas and features, selecting features inside of an area, and overlaying the areas and features. When creating these maps, it is important to use symbols, boundaries, labels, and colors to help distinguish or emphasize visual aspects of the map. I have found this detail to be emphasized throughout the book. Although the first two methods seemed pretty straight forward to me, overlaying the areas and features proved to be more complicated. When you are overlaying and have discrete features, you can use the same analysis as in geographical selection or you can summarize by area. When you are overlaying and have continuous features, however, you use the vector or raster model. Additionally, when overlaying you may end up with slivers. To offset them, you should merge them into one of the larger adjacent areas. When analyzing the results of these maps, you should use the summary statistics such as counts, frequency, sum, average, median, or standard deviation.

Key Concepts/Definitions

Drawing Areas and Features: Creates a map showing the boundaries and features. Good for the visual approach of seeing whether one or more features are inside or outside a singular area.

Selecting Features Inside of an Area: Specifies the area and layer containing features. GIS selects a subset of features inside the area. Good for getting a list or summary of features inside a single area and finding what’s in a given distance of a feature.

Overlaying the Areas and Features: GIS combines the area and the features to create a new layer with attributes of both or compares two layers to calculate the summary statistics of each. Good for finding which features are in several areas or how much of something is in one or more areas.

Slivers: Borders that are slightly offset.

Chapter 6

In this chapter, I was introduced to the concept of mapping what’s nearby. This type of mapping was particularly interesting to me because I recognized its use in many different fields. Mapping what’s nearby identifies the area and the features inside that are affected by a certain event and determines if an area is suitable for a specific use. Data in mapping what’s nearby is measured using distance or cost. Cost, also known as travel costs, could be the amount of time, money, or energy expended. Before mapping, you should decide whether the map would be suitable for the planar or geodesic method and if you should use inclusive rings or distinct bands. To map what’s nearby, you can measure a straight line distance, distance or cost over a network, or cost over a surface. When measuring distance with a straight line, there are several methods that can be used such as creating a buffer, selecting features within a distance, distance between feature to feature, and creating a distance surface. Distance or cost over a network consists of the measurement of segments in geographic networks within the travel parameters. Lastly, calculating cost over a geographic surface shows the rate of change in distance or cost from the feature. The method you use depends on the data and how you intend to portray the map. Overall, throughout this book, I learned that many of these different maps have the same principles behind them. Knowing how to properly differentiate each type of map, utilize coloring, identify features or categories, define boundaries, and analyze summary statistics are all factors in creating effective maps in GIS.

Key Concepts/Definitions

Straight-line Distance: Use for defining an area of influence around a feature, creating a boundary, or selecting features within a distance. Measures distance. 

Distance or Cost Over a Network: Use for measuring travel over a fixed infrastructure. Measures distance or travel costs.

Cost Over a Surface: Use for measuring overland travel and calculating how much area is within the travel range. Measures travel costs.

Planar Method: When you are assuming the earth’s surface is flat. Area of interest is relatively small.

Geodesic method: When you take the curvature of earth into account. Area of interest encompasses a large region.

Inclusive rings: Show how the total amount of features increases as the distance increases. Distinct bands: Show the differences between feature amounts and different distances.

Chapter 1

GIS analysis is the process of looking at geographic patterns in your data and at relationships between features. Understanding GIS analysis is important in making accurate decisions about what to expect and how to prepare for future conditions. Throughout this chapter, I was able to learn the steps of performing a GIS analysis and the necessary geographic features and attributes. To start an analysis, you need to form a specific question based on the information you need and how it will be used. Based on the chosen question, you then must choose an analysis method that best fits your data and features. There are three types of features used in GIS: discrete, continuous phenomena, or summarized by the area. Each geographic feature has one or more attributes that help identify it. These types of attribute values include categories, ranks, counts, amounts, and ratios. Features can be represented by two models: vector or raster. After choosing the method, you then must process the data and analyze the results. Analysis is done through summary statistics. The three most common types of summary statistics used in GIS include selecting, calculating, and summarizing. Finally, after the analysis, it is important to decide whether your information is valid and useful or if it is necessary to rerun the analysis. 

Key Concepts/Definitions:

Discrete features: Features with specific locations that are either present or absent at any given point. 

Continuous phenomena: Can be found or measured everywhere. 

Summarized by the area: Represents the counts or density of individual features within area boundaries. 

Vector Model: Each feature is a row in a table and feature shapes are defined by x,y locations. Areas in the vector model are defined by borders and are represented as closed polygons. Discrete, summarized by area, and continuous categories.

Raster Model: Features are represented as a matrix of cells in continuous space. Each layer represents one attribute and most analysis occurs by combining the layers to create new layers with different values. Continuous numeric values.

Selecting Statistics: Select features to work with a subset or assign a new attribute value to just those features.

Calculating Statistics: Calculate the attribute values to assign new values to features such as rank or ratios.

Summarizing Statistics: Summarize the values for specific attributes to get the statistics such as mean or frequency.

Chapter 2

Throughout this course, I learned that GIS’s ability to map where things are is an important visual in solving real world problems. In order to do this however, one must have a deep understanding of GIS analysis and the ability to create a suitable map. In this chapter, I was introduced to a variety of steps and suggestions to convey an appropriate analysis of data through mapping.

When deciding what to map, the information must be appropriate for the audience and the issue being addressed. To prepare your data, each feature in your map needs geographic coordinates. You can also map by type, categorize similar features, or map by subset categories. I was then intrigued to ask when it is most beneficial to divide major categories into subtypes? The chapter later explains that the general rule of mapping is no more than seven categories. However, if the features are dispersed or the map is smaller, your number of categories can vary. Later, the chapter explains when making your map there are several different ways you can display data including single type, subset feature, or by categories. Mainly, what I have gathered about the mapping process is that there is a delicate balance between being too informative and including as much data into the map as possible. The chapter, however, gives several map making tips on grouping categories, choosing appropriate symbols, and mapping reference features to make the process easier. The chapter concluded by introducing several patterns to look for in analysis such as clustered, uniformly spaced, and random distribution.

Key Concepts/Definitions:

Single Type Map: To map features of a single type. Same symbol used for all features. Basic map to reveal patterns. May suggest differences in the features to further explore.

Subset Feature Map: A map of all features in a data layer or subset based on category value. Can reveal patterns that aren’t apparent when mapping all features. Commonly done for individual locations.

Category Map: Maps features by category. Features represented by different symbols for each category value. Provides understanding of how a place functions.

Chapter 3

In this chapter, I was introduced to the features and process of accurately mapping the most and least features. Mapping where the most and least occur is extremely important in visualizing the relationships between places. This type of mapping is based on the quantity associated with each feature. When the data is discrete or continuous, you should map using counts or amounts. When summarizing by area, however, using counts or amounts can skew the patterns so it is useful to use ratios or ranks instead. Since there can be many different values in mapping, mapping by class allows the reader to compare the data more efficiently. The four most common classification schemes are natural breaks, quantile, equal interval, and standard deviation. When choosing a classification scheme, you need to know how the data values are distributed across the range. Creating a bar chart is a helpful way to see that data. If there is an outlier, you need to pay close attention to it as it can heavily skew your data on the map. One of the most useful things I learned throughout the chapter, however, was how to appropriately use graduated symbols, graduated colors, charts, contour lines, and 3D perspective views to map effectively. Having this knowledge on how to map the most and least is crucial in creating an informative and respectable map through GIS analysis.

Key Concepts/Definitions

Counts: Actual number of features on the map.

Amounts: Any measurable quantity associated with a feature.

Ratios: The relationship between two quantities and are created by dividing one quantity by another for each feature. Can display the average, proportion, or density of certain features.

Ranks: Feature in order from high to low. Show relative values rather than measured values.

Class: Features with similar values represented by the same symbol. 

Natural Breaks: Set where there is a jump in values so block groups having similar values are placed into the same class. Unevenly distributed data.

Quantile: Each class contains an equal number of features. Evenly distributed and emphasis on the relative difference between features.

Equal Interval: The difference between high and low values is the same for every class. Evenly distributed and emphasis on the difference between features.

Standard Deviation: Features are placed in classes based on how much their values vary from the mean. Evenly distributed and emphasis on the difference between features.

Part 1

*I reviewed the course syllabus and schedule to complete the GEOG 291 Quiz.*

Part 2

Hello! My name is Emma Dahlstrom and I am from Lexington, Kentucky. I am a sophomore this year at Ohio Wesleyan University. Currently, I am a declared Environmental Science Major and intend on eventually double majoring in biology or a related field. Although I do not know exactly what I would like to pursue after school, I have always been interested in working in the field of conservation. I am taking GEOG 291 not only for my major, but also because I have heard so much about GIS and was intrigued to learn more about it. Outside of my classes, I am on the university’s softball team and a member of Women In Stem.

Part 3

Originally I thought that GIS was just used in geography related fields and as a form of mapping. However, upon reading this chapter, I learned that its technology was used for so much more. One area that stood out to me particularly was how heavily it impacts agriculture. Coming from an agricultural based community, it was interesting to learn about all the ways GIS impacts its businesses. I was also surprised to learn that GIS was used in medical settings, planning cities, regulating commerce, e-governance, and so much more.

I also found it interesting the GIS had an identity problem. I was unaware that there are two sides to GIS: the “where”  spatial entities are and the “how” we encode spatial entities and the repercussions of different methods of analysis on answers to geographic questions. This identity problem allowed them to differentiate the important difference of spatial analysis and mapping to me. Spatial analysis is extracting the information from spatial data while mapping represents geographical data in a visual form.

Although I was somewhat familiar with GISystems, I was unaware that there was a second face to GIS called GIScience. I learned that GIS is now widely considered a “black box” system because it has become so widely established that it is simply assumed to be true and justifications are no longer required. However, GIScience was established to research and question the accuracy and underlying assumptions of these systems. This established the point that when using GIS, you must responsibly analyze the data to reach an accurate conclusion.  

Lastly, I found that the ability of GIS to visualize spatial relationships and objects makes interpreting the analytical pattern more accessible. This not only applies to the general public, but also to researchers as well. For example, the chapter details how the visual map of the cholera outbreak in London allowed epidemiologist John Snow to identify the location of the outbreak’s origin. 

Overall, this chapter introduced me to the history of GIS and the vast amount of fields and problems that it impacts in our everyday lives.

Part 4

Source 1:

Owusu‐Sekyere, Adriana, and George Ashiagbor. “Mapping the Paths of Giants: A GIS‐Based Habitat Connectivity Model for Forest Elephant Conservation in a West African Forest Block.” African Journal of Ecology [HOBOKEN], vol. 63, no. 2, no. 70028, Mar. 2025, https://doi.org/10.1111/aje.70028.

The Bia Goaso Forest Block in Ghana is home to a vital population of African forest elephants. Historically, the elephants had more extensive ranges and moved across a broader landscape. However, in recent years the population has been isolated which could cause several biological issues for the species. Through the use of a GIS-based habitat connectivity model, scientists were able to find a strong solution that enables the integration of landscape and ecological data on elephant habitat selection and movement. This map shows the variables influencing the choice of movement for the forest elephants based on proximity to water, land use, elevation, proximity to roads, slope, proximity to community, and terrain ruggedness. In addition, it details a variety of locations in the forest block that are suitable and unsuitable for forest elephants. By taking all of the variables into account and eliminating the unsuitable habitats for elephants, scientists were able to identify core channels and areas for elephant movement. This allows for the protection and maintenance of those pivotal areas for the conservation of the elephant species.

Source 2:

Kucsicsa, Gheorghe, and Cristina Dumitrică. “Spatial Modelling of Deforestation in Romanian Carpathian Mountains Using GIS and Logistic Regression.” Journal of Mountain Science [Heidelberg], vol. 16, no. 5, May 2019, pp. 1005–22, https://doi.org/10.1007/s11629-018-5053-8.

This study focused on the Carpathian Mountains which is an area of land that is heavily affected by forest loss. The goal of the study was to examine and analyze the various variables of deforestation in the area and to model the probability of deforestation through the use of GIS. This map displays the difference in forest coverage between 1990 and 2012. In the combined map, the persistent forest coverage stayed green, while the areas affected by deforestation were represented by red. This provides a clear visual of the affected areas. The map also sections off the area into four parts. This displays which parts of the mountains were being most affected by deforestation. By doing this, the scientists were able to identify the variables and their significance in the area. Understanding the driving forces of deforestation in affected areas allows conservationists to adopt appropriate policies and decisions in forest management and conservation.