Author: gmgodsey

For the final in GIS 291: Geospatial Analysis with Desktop GIS, I began by downloading datasets (Parcel, Street Centerline, and Hydrology) from the Delaware County Ohio GIS Data Hub and opened up the maps in the GIS program. The first application I decided to do was Selecting and Classifying Land Uses. The first step was to open up the attribution table for the Parcel layer; this displays all of the data regarding land use in the format of Delaware County Land Use Codes. These codes include agricultural (100-199), industrial (300-399), commercial (400-499), residential (500-599), and exempt (600-699). Mineral (200-299) is usually a land use code, but the Delaware County area does not have any mineral resource parcels. The next step was to take all 5 of these land use codes and separate them into new layers to highlight the different land uses within Delaware County. I did this within the Parcel attribution table with the tool Select by Attributes; within this popup, I created the query “Class is greater than or equal to 100 and Class is less than or equal to 199” (this example is for agricultural land use). The query highlights all data points with a class code within 100-199, which in turn selects all of the land being used for agriculture on the map. Then, I right-clicked on the Parcel layer, and then under selection, I clicked Create a new layer from selection, which made a new layer in the contents column, which I then renamed to agriculture and selected an appropriate color for. Next, I repeated that process for the 4 other land use codes. The following colors match each of the 5 land uses; green (agricultural), yellow (industrial), teal (commerical), orange (residential), and red (exempt). For the second half of this application, I used the same process of selecting the data and creating new layers to highlight the different agricultural land uses within Delaware County. The 9 other land uses and their corresponding colors include; red (vacant land), orange (cash grain/gen farm), yellow (livestock), green (dairy farms), teal (poultry farms), blue (fruit/nut farm), purple (nurseries), brown (timber), pink (other). The agricultural land uses not included because they do not exist within Delaware County are vegetable, tobacco, and greenhouse farms. 

Map of Land Uses:

Map of Agricultural Uses:

The second application I decided to do was Mapping Change. I began by downloading the Subdivision file from the Delaware County Ohio GIS Data Hub. Then, in the GIS program, I opened the following layers on my map: Parcel, Street Centerline, Hydrology, and Subdivision. First, I opened the attribution table for the Subdivision layer and located the column with the dates of the subdivision’s establishment; these dates range from 18080311 (3/11/1808) to 20240917 (9/17/2024). The next step was to separate these establishment dates into new layers to highlight the changes in urban development within Delaware County. I did this within the Subdivision attribution table with the tool Select by Attributes; within this popup, I created the query “Date is greater than or equal to 18080311 and Date is less than or equal to 18501231” (this example ranges from 3/11/1808 to 12/31/1850). The query highlights all subdivision establishments within 1808-1850, which in turn selects all of the subdivisions established during that time period on the map. Then, I right-clicked on the Subdivision layer, and then under selection, I clicked Create a new layer from selection, which made a new layer in the contents column, which I then renamed to 1808-1850 and selected an appropriate color for. I repeated that process for the dates 1851-1900, 1901-1930, 1931-1960, 1961-1990, 1991-2010, and 2011-2024. The following colors match each of the date ranges on the map: red (1808-1850), orange (1851-1900), yellow (1901-1930), green (1931-1960), blue (1961-1990), purple (1991-2010), pink (2011-2024). I also included a close-up of downtown Delaware because I think the changes in the establishment have been more noticeable throughout the decades (also, it’s cool!).

Map of Establishment: 

Downtown:

Chapter 9:

Chapter 9 went relatively smoothly; I encountered a couple of issues. One was in tutorial 9; when trying to select block centroids within the buffers and find the sum of the number of youths, I couldn’t figure out how to locate and open the PittsburghBlockCe_Statistics table. The other issue I discovered was trying to create the scatterplot. I also had issues with the desktop freezing, so I lost my screenshots from this chapter, but I will make up for it with more pictures of chapters 10 and 11!

 

Chapter 10:

Chapter 10 went smoother than Chapter 9. The only issue I encountered was in Tutorial 3 when trying to apply the ZFHHChld expression to the PittsburghBlkGroup attribution table. 

Chapter 11:

Chapter 11 went successfully! I ran into some minor issues; in tutorial 4, the line of sight between Observer 1 and Obsever 2 did not show up, and in tutorial 5, I could not find the Range tab in Properties to select FloorNumber. I enjoyed the animation portion of this chapter!

Chapter 4:

I had multiple complications with this chapter. About halfway through, I got stuck on the first tutorial and ended up redoing my work, but I reached the same outcome while trying to create the MaricopaTracts geodatabase. I had to skip the first two tutorials and continued with tutorial three without any more issues. 

Chapter 5:

Similar to Chapter 4, I had a few issues with this chapter. The first problem I encountered was in tutorial 3 when trying to add data in the layer group, browsing Chapter5.gdb I was unable to locate the Municipalities dataset. Similarly, in tutorial 6, I could not find the items from the National Land Cover Database when searching Living Atlas.

Chapter 6:

Thankfully, Chapter 6 went relatively smoothly, with only one issue. During tutorial 6 I couldn’t figure out how to use the NTAName field to join the BrooklynNeighborhoodsResidentialLandUse table to BrooklynNeighborhoods. 

Chapter 7:

Chapter 7 went smoothly; the only thing I couldn’t figure out was how to use the Split tool.

Chapter 8: 

Chapter 8 was pretty simple, but I did encounter one issue in tutorial 2. I couldn’t get the street locator tool to create the Streets_CreateLocator even though the tool said it was successful. 

 

Chapter 1:

The first tutorial chapter went slowly but smoothly. I didn’t run into any issues or complications, but it took me a while to get through all four tutorials as I adjusted to the program. 

Chapter 2:

The second tutorial chapter went much quicker. In tutorial four, I encountered a problem with the database not being imported correctly, but I reloaded the tutorial and manually imported the data to fix this issue.

Chapter 3:

The third tutorial chapter went slower than chapter two, but I encountered a few issues. The first issue was in tutorial one; when creating the chart, I could not see it (pictured below). I went through the steps again but ran into the same issue and decided to move on. The second issue was during tutorial four; when creating the table, it would not appear on the same tab as the map as the bar chart did. 

 

Chapter 4: Mapping Density

Mapping density allows the user to see where the highest concentration of a feature is located and highlights patterns in areas of different sizes. There are two methods when mapping density: shading an area based on density value or creating a density surface. The method should be based on the data type; the GIS program uses a density surface to map features, and map data is usually already summarized by a defined area (counties, forest districts, etc.). Mapping density by defined area is commonly created using a dot map, which represents the density of individual locations summarized by defined areas (each dot represents a specific number of features and is not based on the features’ actual location). To calculate the density value for the area, the user can divide the total number of features/total value of features by the area. The density surface is created in GIS as a raster layer, with each cell in the layer getting a density value based on the number of features within the cell’s radius. Users should map by defined area if their data is already summarized by area or map by density surface if they want to see the concentration of point or line features. Density by defined area is calculated based on the areal extent of each polygon and is usually displayed as a shaded map. In a dot density map, the user maps each area based on a total count/amount and a specific value of how much each dot represents. Then, GIS divides the value of the polygon by the amount represented to figure out how many dots to draw in one area. A dot map represents density graphically, and the individual dots represent total numbers/values in each area rather than a calculated density value. GIS creates density surfaces as raster layers with a specific calculated density value for each cell in the layer, which is good for showing where/how point/line features are concentrated. 

 

Chapter 5: Finding What’s Inside

Users map the inside of an area to monitor and understand what is occurring inside a given parameter and compare it to several other places; this provides the user with an idea of what is happening and where to take action. There are three ways users can define their analysis to find what is inside a given parameter, including drawing an area boundary on top of the features, using an area boundary to select the features’ insides and list or summarize them, or combining the area boundary and features to create summary data. Finding what is inside a single area allows the user to monitor activity/summarize information about the area (e.g., an administrative/natural boundary such as a watershed). Finding what is inside several areas allows the user to compare the areas (e.g., a group of zip codes). The features inside a given parameter can be discrete, unique identifiable features, or continuous, seamless geographic phenomena. By drawing areas and features, the user can show an area/feature’s boundary and then see which falls inside/outside the boundary. When selecting the features inside the area, the user specifies the area and the layer containing the features, then GIS chooses a subset of the features inside the given area. When overlaying the areas/features, GIS combines the areas/features to create a new layer with the attributes of both or compares the two layers to calculate the summary statistics. When choosing the best method for the user’s data/results, they should follow the guidelines to select the most appropriate method. First, the user should draw the area/features if they have a single area and only need to see the features within that selected area. Select the features inside if you have a single area and make a list/summary of discrete features that are fully or partially inside. Overlay the areas and features if there are multiple areas with a summary of what’s inside each, there is a single area with a summary of discrete features, including the portion of features, or there is a single area with a summary of continuous values. 

 

Chapter 6: Finding What’s Nearby

User map change to gain insight into how features/factors behave to anticipe what future conditions may be like, decide on a course of action, or elevate the results of an action or policy. Users can demonstrate change in an area by showing the location/condition of features at numerous dates or calculating and mapping the difference in specific values for each feature between two/more dates. Geographic features can show change in two ways; either through change in location or change in character/magnitude. Mapping a change in location allows the user to see how features will behave in the future allowing them to predict where future movements may take place (e.g., mapping the patterns of hurricanes throughout the months). Discrete features can be tracked as they move through space over time, these can be individual features (an animal), linear features (a river), or an area feature (boundary lines). Events represent geographic phenomena that can be tracked and occur at different locations over time (movement of crimes in a given area over time). Mapping a change in character/magnitude shows how the same condition in a given location has changed over time (e.g., changes in categories of land cover in a watershed now vs 20 years ago). Discrete features can change in character/quantity of an attribute associated with them (e.g., changes in traffic volume over a 24-hour period). Data summarized by area are totals, percentages, or other quantities that are associated with features within a defined geographic area (e.g., population in each county for each year). Continuous categories demonstrate the type of features in a given area, represented by boundaries or as a surface. Continuous values are measurements that are monitores at fixed points and are always available, such as air pollution. The time pattern being used to measure can be mapped in three ways; as a trend (change between two dates/times), as a before and after (conditions before and following an event), or as a cycle (change over a recurring time period). Change can be mapped in three ways, through a time series (one map for each time/date showing the location or characteristics of the features over time), a tracking map (a single map showing the location of the features over time), or measuring change (the amount, percentage, or rate of change in a specific place). 

Chapter 1: Introducing GIS Analysis

The first step in the GIS analysis process is framing a specific question, which will help decide how to approach the analysis, what methods to use, and how to display the results. The next step is to understand the data and features related to the question to determine the specific method, which is usually narrowed down based on what the results need to look like (either a quick process with limited results or a detailed analysis with precise results). Once selecting a method, the next step is to perform the necessary actions in GIS and analyze the results, whether they are displayed as a map, table, or chart. The geographical features include discrete and continuous phenomena, summarized by area. Discrete locations and lines can be pinpointed, and the feature can be present (e.g., Bodies of water, such as streams, are linear features). Continuous phenomena, such as precipitation or temperature, can be measured and recorded anywhere over the mapped area (e.g., annual precipitation/average monthly temperature values can be determined at any location). Summarized data represents the specific features within an area’s boundaries (e.g., the number of businesses in each zip code), which applies to the entire area, not a particular location. GIS uses two models, vector and raster, to represent geographical features. In the vector model, the features can be discrete locations or events, lines, and areas defined by x,y locations in space. A series of coordinate pairs represent lines demonstrating roads, streams, or pipelines. Areas (parcels of land, counties, or watersheds) are defined by borders and are represented by closed polygons. In the raster model, different features are represented by a matrix of cells (each layer representing one attribute) within continuous space. Cell size should be based on the original map scale/the minimum mapping unit to avoid using too large/too small a cell size (both of which can impact the precision of the map). The geographic attributes include categories, ranks, counts, amounts, and ratios. Categories are groups of similar characteristics (e.g., roads can be categorized as highways, freeways, or local roads). Ranks put features in order from high to low and are based on another feature attribute, such as a type or category. Counts and amounts demonstrate total numbers, with a count being the actual number of features on a map and amounts being any measurable quantity associated with a specific feature. Ratios illustrate the relationship between two quantities and show the differences between large and small areas. 

 

Chapter 2: Mapping Where Things Are

GIS uses mapping to demonstrate the distribution of features on a map rather than at individual features, which helps the user better understand the patterns of the area they are viewing. Mapping can help explain causes for patterns and allow the user to focus their efforts on specific distributions of features. When deciding what to map, the user needs to look for geographic patterns in the data, then use different layers and symbols to represent various features based on the information and results they seek. The map used should be appropriate for the audience and the issue that is trying to be solved; smaller maps should only have the information needed to demonstrate patterns, whereas larger maps will need to present more detailed data/information while remaining readable. To create a map, the user must prepare their data by assigning each feature a location in geographic coordinates and category values. Then, the user will tell GIS if they want their features displayed in a layer as a single type or by category values. When mapping a single type, all of the features demonstrated on the map use the same symbols; although these are basic maps, they can still reveal patterns. Mapping features by category involves using a different symbol for each category value; this gives an idea of how an area functions. The user can also display a subset of categories to uncover patterns and relations between various features (if a map has more than seven categories, it can make the area clustered, so grouping some will help). When choosing symbols to display categories, using different colors for each feature will help distinguish patterns better than other shapes. Including recognizable landmarks (roads, highways, buildings) in a map is beneficial to help people connect meaning to the patterns/results found. Patterns can be seen by looking at the map, or hidden patterns need statistics to measure and quantify the relationship between features. 

 

Chapter 3: Mapping the Most and Least

Mapping the most and least allows users to understand what areas meet their criteria, require action, or highlight relationships between places. Including features based on quantities adds another level of information beyond simple location features and brings a more in-depth understanding of the patterns/information seen. Users can map the features based on three quantities: discrete features, continuous phenomena, and data summarized by area. Discrete features are locations, linear features, or regions (e.g., line thickness determines river fish habitat). Continuous phenomena are areas/surfaces of continuous values using graduated colors, contours, or 3D views (e.g., soil fertility in an area is measured by a color gradient). Data summarized by area is demonstrated by separating different areas/features with various shading (e.g., the number of businesses in each zip code is represented by lighter/darker shading). After determining the quantities, the user must assign a symbol or group of values to each individual value into classes. Mapping individual values allows the user to see an accurate picture of the data and search for patterns within the raw data. Classes are features with similar values assigned the same symbol; users should make the differences in values between classes as great as possible to make the results as straightforward as possible. Users should create classes manually to ensure that their features meet specific requirements/compare values to meaningful values (they should specify upper and lower limits and symbols for each class). A standard classification scheme should be used if the user wants to group similar values to look for patterns in the data; the four most common schemes include natural breaks, quantile, equal interval, and standard deviation. In natural breaks or Jenks, the classes are based on natural groups of the data values. In quantile, each class has an equal number of features. In equal intervals, the difference between the high and low values is the same for each class. Standard deviation features are placed into classes based on the value variance from the mean. When making a map in GIS, it is easy to add more information than needed; remember to keep the information simple, clear, and concise.

Hi, my name is Gwendolyn Godsey, or Gwen for short, and I’m a senior majoring in Environmental Science with a minor in Nutrition.

While reading Chapter One of Nadine Schuurman’s GIS: A Short Introduction, I found it intriguing that the essential components of GIS, such as the concept of various layers of landscape (forests, streets, valleys), were mapped out using tracing paper and a light table. The tedious task of creating these layers and spatial analysis by hand can now be organized and separated quickly by computers, highlighting the advancement of the GIS process. GIS relies on a visual display of data and information, so it could be considered an unreliable or unscientific approach to quantifying data. However, as a visual learner, the illustrated display of information is easier to understand than viewing data on a table or chart. Before reading this chapter, I was unaware of the difference between GISystems, which focuses on the hardware and software that can capture and represent geographic information, and GIScience, which focuses on the abstract ideas behind the data and systems. As I understand it, GIScience is a foundation of ideas for how GISystems are built and operated, and the term GIS is used to describe both the system and the science behind it. Since the GIS process depends on spatial data, crisp and defined lines are favored in GIS to represent differences between various landscapes. I can understand why the classification process between landscapes and boundaries can become fuzzy, making this a challenge for users to overcome. The power of the GIS system is impressive, as the visualization of spatial relationships and objects allows the users to easily explore both intuitive and structured cofactors that may relate to an overarching pattern. Initially, I believed that GIS was only used by scientists and the government to understand environmental patterns or issues. But now I realize the true diversity of the industries that utilize GIS, including universities, hospitals, community groups, and public and private businesses. 

One area of GIS application is in Feminist Geographic Information Sciences (FGIS). Where conventional GIS uses precise locational measurements to quantify space, feminist GIS uses location and social processes with concerns about equality, wealth distribution, and power allocation. Through GIS, feminists can study how spatial problems involving women and other marginalized groups can contribute to social issues they are experiencing. (https://vtechworks.lib.vt.edu/server/api/core/bitstreams/90726a4c-9ae5-4f66-be6b-ec3f0b9b933c/content)

Another area of GIS application is in agriculture and farming practices. Farmers use spatial analysis to compare the differences in soil type and rainfall patterns within their agricultural land to assist with managing crops and drainage planning to prevent floods and droughts, leading to optimal crop yield. Farmers also use remote sensors through satellites to improve cultivation planning and decision-making to maximize crop yields. (https://smallfarms.cornell.edu/2017/04/use-of-gis/)