Author: rpobenauf

Final Project Data Summary:

• Zip Code
  • Contains all zip codes within Delaware County, Ohio. The layer was created in 2005 after re-evaluation of the zip codes in 2003 by dissolving all Delaware County parcels by their property addresses. This dataset is published monthly and is updated on an as-needed basis through coordination with the USPS. 
• School District
  • This data set consists of all School Districts within Delaware County. Originally created via the Delaware County Auditor’s parcel records, it is updated as-needed and is published monthly. 
• Building Outline 2023
  • This dataset has an outline of all buildings and structures in Delaware County in 2023. Delaware County. 
• PLSS
  • This dataset consists of all the Public Land Survey System (PLSS) polygons in both the US Military and the Virginia Military Survey Districts of Delaware County. It was created to facilitate the identification of all of the PLSS and their boundaries. It is updated as-needed and is published monthly. 
• Township 
  • This data set consists of 19 different townships that make up Delaware County, OH. It is updated as-needed and is published monthly. 
• 2024 Aerial Imagery 
  • 2024 3in Aerial Imagery. Flown in Spring of 2024. Collection of maps and datasets. Published September 25, 2024. 
• 2021 Imagery (SID file) 
  • Collection of images. Published March 11, 2022. 
• Recorded Document 
  • Dataset consists of points that represent recorded documents in the Delaware County Recorder’s Plat books, cabinet/slides and instrument records which are not represented by active subdivision plats. This dataset is updated on a weekly basis and is published monthly. 
• Dedicated ROW
  • This dataset consists of all lines that are designated right-of-way within Delaware County. Created through the daily updates of Delaware County’s Parcel data. This dataset is updated on a daily basis and is published monthly. 
• Delaware County E911 Data
  • The State of Ohio Location Based Response System (LBRS) Address_Points data set is a spatially accurate representation of all certified addresses within Delaware County. Data is maintained by Delaware County Auditor’s GIS Office. The Address_Points layer is intended to support appraisal mapping, 911 Emergency Response, accident reporting, geocoding, and disaster management.  The dataset is updated on a daily basis, and is published monthly.
• Building Outline 2021
  • This dataset consists of building outlines for all structures in Delaware County, Ohio. The layer was updated in 2021. The dataset is updated on an as-needed basis.
• Original Township 
  • This dataset consists of the original boundaries of the townships in Delaware County, Ohio before tax district changes affected their shapes.
• Precincts 
  • This dataset consists of Voting Precincts within Delaware County, Ohio. This dataset is maintained by the Delaware County Auditor’s GIS Office under the direction of the Delaware County Board of Elections. This dataset is updated on an as needed basis and is published as needed by the Delaware County Board of Elections.
• Delaware County Contours
  • 2018 Two Foot Contours for Delaware County Ohio in File Geodatabase format. Data was published April 9, 2020.
• Building Outlines – DXF
  • Data consists of an image of specific building outlines in Delaware County using CAD drawings. Created on March 26, 2020, Last updated on May 15, 2023.

• Address Points – DXF

• The State of Ohio Location Based Response System (LBRS) Address Points data provides for a spatially accurate placement of addresses within a given parcel in Delaware County. The data was created through a partnership between the State of Ohio and Delaware County. The data is maintained by the Delaware County Auditor’s GIS Office. The Address Points indicate the location of the building centroid as best as possible. 

• Street Centerlines – DXF
  • The State of Ohio Location Based Response System (LBRS) Street_Centerlines depict center of pavement of public and private roads within Delaware County. Address Range data was developed from data collected by field observation of existing address locations and by adding addresses using building permit information.
• Parcel
  • This dataset consists of polygons that represent all cadastral parcel lines within Delaware County, Ohio. The cadastral geometries are maintained by the Delaware County Auditor’s GIS Office. This dataset is maintained on a daily basis, and is published monthly.
• Street Centerline 
  • The State of Ohio Location Based Response System (LBRS) Street_Centerlines depict center of pavement of public and private roads within Delaware County. Address Range data was developed from data collected by field observation of existing address locations and by adding addresses using building permit information. This layer is updated on a daily basis for all fields but the 3-D fields which are updated on an annual basis, and is published monthly.
• Condo 
  • This data set consists of all condominium polygons within Delaware County, Ohio that have been recorded with the Delaware County Recorders Office.
• Subdivision
  • This data set consists of all subdivisions and condos recorded in the Delaware County Recorder’s office. This dataset is updated on a daily basis and is published monthly.
• Tax District
  • This data set consists of all tax districts within Delaware County, Ohio. The data is defined by the Delaware County Auditor’s Real Estate Office. Data is dissolved on the Tax District code. The data is updated on an as-needed basis and is published monthly.
• Address Point
  • The State of Ohio Location Based Response System (LBRS) Address_Points data set is a spatially accurate representation of all certified addresses within Delaware County Ohio.   The dataset is updated on a daily basis, and is published monthly.
• Map Sheet
  • This dataset consists of all map sheets within Delaware County, Ohio
• Farm Lot
  • This data set consists of all the farmlots in both the US Military and the Virginia Military Survey Districts of Delaware County. This dataset is maintained on an as-needed basis where new surveys have been recorded.
• Annexation
  • This data set contains Delaware County’s annexations and conforming boundaries from 1853 to present. This dataset is updated on an as-needed basis once an annexation has been recorded with the Delaware County Recorders office. It is published monthly.
• Survey
  • Survey points is a shapefile of a point coverage that represents surveys of land within Delaware County, Ohio. These surveys are found in documents in the Recorder’s office and the Map Department. This dataset is updated on a daily basis and is published monthly.
• 2022 Leaf-On Imagery (SID file)
  • 2022 Imagery 12in Resolution. Information was published September 14, 2022.
• Hydrology
  • This dataset consists of all major waterways within Delaware County, Ohio. This data was enhanced in 2018 with LIDAR based data. This dataset is updated on an as-needed basis and is published monthly.
• GPS
  • This dataset identifes all GPS monuments that were established in 1991 and 1997. This dataset updated on an as-needed basis, and is published monthly.

Chapter 7

This chapter was easy for me and I actually figured out how to use certain features before being told to by the tutorial. This chapter included a lot of practical skills that I think will be useful to know how to do. This includes moving and rotating polygons as the software is not always 100% accurate and so I know this will be useful. It was fun to be able to create a new feature and is a useful skill.

Chapter 8

This chapter was focused on geocoding and was very short. We learned how to rematch addresses and use other various new tools. Geocoding is the process of converting text-based location descriptions into precise geographic coordinates to plot them as points on a map. This chapter was fairly simple for me. 

Chapter 9

This chapter showed us how to use buffers which is a zone around a feature. These are used for drug-free buffers around schools, food deserts, etc. We also learned how to use multiple-ring buffers which are used to create various buffer zones inside each other. We used service areas to estimate a gravity model of geography. 

Chapter 4

I had similar issues as last time with the instructions not being specific enough. For example the text said to “Search for and open the Export Table tool,” when I searched for it three different “Export Table” tools came up and the text did not specify which one was the correct one. I had to go through each tool to figure out which one was the right one. After trying the two tools that were still active I got error messages for both of them. There were times where I had to guess what the textbook wanted me to do. I had a really hard time with this chapter. It was very confusing. After meeting with Dr. Krygier I realized that it wasn’t as much the content in the “Your Turn” section that was confusing me but just the way they worded the instructions that I had no idea what to do with. 

Chapter 5

This chapter went a lot smoother but I did have some issues with instructions telling me to open something with no other instructions and I had no idea how to proceed. This chapter has been the easiest for me thus far with little snags. It is really interesting how many applications GIS has and the many different ways it can be applied. 

 

Chapter 6

We learned a lot of different tools in this chapter. We learned how to combine and dissolve data. The pairwise dissolve tool was easy to use. This chapter went over how to merge, extract and add features. 

 

Chapter 1:

This chapter was not very difficult as I have used most of these features before. Instructions were generally easy to understand but I did have some issues with buttons being greyed out (among other things) when I was told to click on them. I am sure this is due in part to user error. I had other issues like things not appearing that were supposed to. I like that they go through a lot of the features just to show us how to use them and turn them off so we know where they are and how to use them. I like that we can toggle different layers to get different mixes of information. 

Chapter 2: 

I had more issues with this chapter. For example, it says “In the Contents pane, drag Over Age 60 Receiving Food Stamps above the 3D Layers heading.”, which is impossible because Over Age 60 Receiving Food Stamps is a subheading of 2D (again, I was probably just doing this wrong). I like how many different ways there are to customize your map and make it accessible. 

Chapter 3: 

I could not get the text box to work for some reason, I couldn’t figure out how to write in it, I tried in multiple places. I also could not figure out how to get the ruler to work. I like how many options for map elements and surroundings we have. The instructions for all three chapters were confusing and at times difficult to follow. 

Mitchell Chapter 4

Mapping density shows where the highest concentration of features is. Density maps are most useful for looking at patterns and large collections of data. A density map lets you measure the number of features using a uniform aerial unit, such as hectares or square miles, so you can clearly see the distribution. Density maps are useful for mapping areas that vary in size, such as census tracts or counties. You can map the density of features or of feature values. You can create a density map based on features summarized by defined area or by creating a density surface. You can map density graphically, using a dot map, or calculate a density value for each area. To calculate a density value for each area, you divide the total number of features, or total value of the features, by the area of the polygon. A density surface is usually created in the GIS as a raster layer with each cell in the layer getting a density value. This approach provides the most detailed information but requires more effort. You can create a density surface from individual locations, or linear features. 

To create a density surface, the GIS defines a neighborhood around each cell center. It totals the number of features that fall within that neighborhood and divides that number by the area of the neighborhood. The GIS does this for every cell and creates a running average of features per area. Several parameters that you specify affect how the GIS calculates the density surface, and thus what the patterns will look like. These include cell size, search radius, calculation method, and units. The cell size determines how coarse or fine the patterns will appear. The smaller the cell size, the smoother the surface. A larger cell size will process faster but will result in a coarser surface. 

Mitchell Chapter 5

People map what’s inside an area to monitor what’s occurring inside it, or to compare several areas based on what’s inside each. By monitoring what’s going on in an area, people know whether to take action. Summarizing what’s inside each of several areas lets people compare areas to see where there’s more and less of something. To find what’s inside, you can draw an area boundary on top of the features, use an area boundary to select the features inside and list or summarize them, or combine the area boundary and features to create summary data. Finding what’s inside a single area, lets you monitor activity or summarize information about the area. Finding how much of something is inside each of several areas lets you compare the areas. 

Discrete features are unique, identifiable features. You can list or count them or summarize a numeric attribute associated with them. They are either locations or discrete areas such as parcels. Continuous features represent seamless geographic phenomena, you can summarize the features for each area. 

Drawing areas on top of features is a quick and easy way to see what’s inside. You create a map showing the boundary of the area and the features, you can then see which features are inside and outside the area. Selecting the features inside the area includes specifying the area and the layer containing the features, and the GIS selects a subset of the features inside the area. Another way is to overlay the areas and features. The GIS combines the area and the features to create a new layer with the attributes of both or compares the two layers to calculate summary statistics for each area. This approach is good for finding which features are in each of several areas or finding out how much of something is in one or more areas. 

Mitchell Chapter 6

Using GIS, you can find out what’s occurring within a set distance of a feature and what’s within traveling range. Finding what’s within a set distance identifies the area affected by an event or activity. It also lets you monitor activity in the area. Traveling range is measured using distance, time, or cost. Finding what’s within the traveling range of a feature can help define the area served by a facility. Knowing what’s within traveling range can also help delineate areas that are suitable for, or capable of supporting, a specific use. To do this, you can measure straight-line distance, measure distance, or cost over a network. For some analyses, you have the option of calculating distance assuming the surface of the Earth is flat (planar method) or taking into account the curvature of the Earth (geodesic method). The planar method is appropriate when your area of interest is relatively small, the results of your analysis will appear as the correct shape when displayed on a flat map. Geodesic method should be used when your area of interest covers a large region. 

Straight-line distance is the easiest way of finding out what’s nearby. With this, you specify the source feature and the distance, and the GIS finds the area or the surrounding features within the distance. This approach is good for creating a boundary or selecting features at a set distance around a source. With the distance or cost over a network approach, you specify the source locations and a distance or travel cost along each linear feature. The GIS finds which segments of the network are within the distance or cost. You can then use the area covered by these segments to find the surrounding features near each source. This approach is good for finding what’s within a travel distance or cost of a location over a fixed network. With the cost over a surface approach, you specify the location of the source features and a travel cost. The GIS creates a new layer showing the travel cost from each source feature. This approach is good for calculating overland travel cost. 

Mitchell 1

Spatial analysis and data is more abundant than ever and growing in acceptance as it experiences more advances and is shared more openly and widely. It is also growing in uses and accessibility and will continue to grow in the coming decades. GIS analysis is a process of identifying geographic patterns in data that range in complexity. In order to effectively perform an analysis you need to: frame the question by figuring out what information you need; understanding your data; choosing a method that works for your data collection and intended use of the results; process the data; and look at the results. 

The types of geographic features we’re working with affect all steps of the analysis process. Geographic features are discrete, continuous phenomena, or summarized by area. For discrete locations and lines, the actual location can be pinpointed. At any given spot, the feature is either present or not, examples include streams and parcels. Continuous phenomena can be found or measured anywhere and blanket the entire area you’re mapping. A value can be determined at any given location and includes precipitation and temperature. Summarized data  represents the counts or density of individual features within area boundaries. Examples of features summarized by area include the number of businesses in each zip code, the total length of streams in each watershed, or the number of households in each county. The data value applies to the entire area, but not to any specific location within it.

Geographic features can be represented in a GIS using two models of the world: vector and raster. With the vector model, each feature is a row in a table, and feature shapes are defined by x,y locations in space. Features can be discrete locations or events, lines, or areas. Locations are represented as points having a pair of geographic coordinates. When you analyze vector data, much of your analysis involves summarizing the attributes in the layer’s data table. With the 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 new cell values.

Mitchell 2

Patterns help understand the area you’re mapping, you can use a map to identify individual features or to look for patterns in the distribution of features. Looking at the location of features lets you explore causes for patterns. It is important to understand the purpose of your analysis and your audience to know what information is relevant and what to include. Every feature on a map needs geographic coordinates, if the data is coming from a GIS database it will already have coordinates assigned. Many categories are hierarchical, with major types divided into subtypes. 

When making a map, you can map all features in a layer as a single type or show them by category values. Mapping features as a single type might reveal differences between them. The GIS stores the location of each feature as a pair of geographic coordinates or, if the location is a line or area, as a set of coordinate pairs that define its shape. You can map features by category, by drawing features using a different symbol for each category value. Mapping features by category can provide an understanding of how a place functions. When mapping different categories you can use different symbols or different maps to express the different categories. If you’re showing several categories on a single map, you’ll want to display no more than seven categories. Because most people can distinguish up to seven colors or patterns on a map, displaying more categories than this makes the patterns difficult to see. The distribution of features and the scale of the map also affect the number of categories you can display. The way you group the categories can change the way readers perceive the information. 

Your map will be more meaningful for people if you display recognizable landmarks, such as major roads or highways, administrative or political boundaries, locations of towns or cities, or major rivers. You may also want to map reference features specific to your analysis so that you can look at geographic relationships.

Mitchell 3 

People map where the most and least are to find places that meet their criteria and take action, or to see the relationships between places. Mapping features based on quantities adds an additional level of information beyond simply mapping the locations of features. Knowing the type of features you’re mapping, as well as the purpose of your map, will help you decide how to best present the quantities to see the patterns on your map. 

You can map quantities associated with discrete features, continuous phenomena, or data summarized by area. Discrete features can be individual locations, linear features, or areas. Locations and linear features are usually represented with graduated symbols, while areas are often shaded to represent quantities. Continuous phenomena can be defined areas or a surface of continuous values. Areas are displayed using graduated colors while surfaces are displayed using graduated colors, contours, or a 3D perspective view. Data summarized by area is usually displayed by shading each area based on its value or using charts to show the amount of each category in each area. 

To map the most and least you assign symbols to features based on an attribute that contains a quantity. Quantities can be counts or amounts, ratios, or ranks. Knowing the type of quantities you’re mapping will help you decide the best way to present the data. Ratios show you the relationship between two quantities, and are created by dividing one quantity by another, for each feature. Using ratios evens out differences between large and small areas, or areas with many features and those with few, so the map more accurately shows the distribution of features. Because of this, ratios are particularly useful when summarizing by area. The most common ratios are averages, proportions, and densities. Averages are good for comparing places that have few features with those that have many. 

Intro: My name is Rio Obenauf, I am a sophomore majoring in Sociology and Environmental Studies. I work in the costume shop for the Theater department. I plan to go to grad school for Environmental Sociology after graduation and figure out what I want to do from there, likely studying social vulnerability or biodiversity conservation. I took GEOG 347 in the fall and we worked a lot with ArcGIS. I struggled with this class because I had no background knowledge or experience with GIS so I am excited to learn more about the software without the stress of trying to figure it out as I go. 

Schuurman Chapt

er 1: I had not heard of GIS before coming to OWU but it’s incredible just how many ways it can be utilize

d and how vast its reach is. I had no idea how much controversy surrounded GIS as far as how it’s regarded, whether as an extension of mapping or as a tool for quantitative analysis. I was previously unfamiliar with many of the terms used in this article including the terms GIScience and GISystems. Geographic Information Science is the science behind the GIS software and is the study of geographic information, focusing on theories, methods, and technologies. GISystems are the software and hardware used to collect, store, analyze, and display data. 

In GEOG 347 we used GIS to analyze rates of deforestation over an area in Guatemala. Our data went back to 1970 so it was cool to see the evolution in quality from then to 2025. Images from 1970 were nearly unusable because of how fuzzy they were. We also used GIS to analyze temperatures in central Ohio to study the Columbus urban heat island. I had no idea how wide the range of applications for this framework is and that it initially gained appreciation in the architectural community.  I greatly appreciate just how versatile GIS is and how many contexts it can be applied in. GIS has gained traction in so many disciplines and communities that I am sure I will have an opportunity to use these skills in my future career. I am grateful to have this opportunity to learn how to use this software as it is a great ability to possess for the field I plan to go into.

GIS Application 1: GIS can be used to analyze flood prone areas to aid in disaster mitigation and predict future flooding events. This study researched flood preparedness in Thailand, specifically in the elderly population. 

https://doi.org/10.3390/w14081268 

 

GIS Application 2: This study used GIS to monitor animal and habitat biodiversity. They used GIS because it accommodates large varieties of spatial and attribute data. Data on species and habitat distribution from different dates allow monitoring of the location and the extent of change.

https://doi.org/10.1006/jare.2001.0887