{"id":7766,"date":"2026-10-02T11:11:02","date_gmt":"2026-10-02T16:11:02","guid":{"rendered":"https:\/\/sites.owu.edu\/geog-291\/?p=7766"},"modified":"2026-10-02T11:11:02","modified_gmt":"2026-10-02T16:11:02","slug":"sisler-week-6","status":"publish","type":"post","link":"https:\/\/sites.owu.edu\/geog-291\/2026\/10\/02\/sisler-week-6\/","title":{"rendered":"Sisler Week 6"},"content":{"rendered":"

Chapter 7- Digitizing\u00a0<\/span><\/p>\n

The learning goals of this chapter include creating, editing, and deleting polygon features; creating and digitizing point features; using cartography tools to smooth features; working with CAD drawings; and spatially adjusting features. The first tutorial in this chapter introduces moving and rotating existing buildings, adding vertex points, and splitting polygons to edit them to match buildings. When two buildings are drawn as one polygon, the split tool will separate the polygons. The second tutorial is about creating and deleting polygon features. This is important because planners need the open polygons to see the surface and for transportation engineering studies. I created a polygon and deleted 4 polygons. The third tutorial taught me how to use cartography tools. These tools are useful to improve the cartographic quality of polygons. The tool that helps with this is called the smooth polygon tool. Some data has features digitized with a few lines that don\u2019t match the true geography. The smooth polygon tool can help smooth features, which will fix the problem. When smoothing, the smoothing tolerance is important; a lower number gives a more detailed path but will take longer to process. The last tutorial taught me how to transform features. When dealing with computer-aided designs (CAD), the drawings or models aren\u2019t usually in the same units as the GIS software, so this is important to remember.\u00a0<\/span><\/p>\n

\"\" \"\"<\/p>\n

Chapter 8: Geocoding<\/span><\/p>\n

Geocoding is a GIS process that matches locations in data to corresponding fields in existing feature classes. When matching sources to reference data, it is not possible to make exact matches. So there is a fuzzy-matching algorithm that does its best to match the source address to the actual address. The system attempts to use the thought processes and rules that an expert would use to complete the task. This algorithm starts each source with 100 points; each problem encountered when trying to match the source would remove points from the score. If there is more than one candidate, then the candidate with the higher score is chosen for that estimated location. On the off chance that there was a tie between two sources, the GIS software picks one, or the user can choose to have the GIS leave ties alone. The first tutorial in this chapter taught me about geocoding data using ZIP codes, which is a good way to geocode because people normally put the correct ZIP code compared to everything else. I learned how to change the geocode match scores through the geocoding options. I have learned how to rematch addresses and add my own addresses from a point on the map. The last thing I learned in this tutorial was how to use the collect events tool. This tool counted the attendees in each ZIP code and applied a symbol to each ZIP code based on the number. The second tutorial is about geocoding with street addresses. I used the create locator tool again in this tutorial to geocode attendee data by street addresses. I reorganized the fields in my attribute table to compare specific values in the data.\u00a0<\/span><\/p>\n

\"\"\"\"<\/p>\n

Chapter 9: Spatial Analysis<\/span><\/p>\n

This chapter focuses on spatial analysis. Sometimes the map won\u2019t have all the data for users to answer questions and solve problems. There are different types of analytical methods that may be needed to answer the questions and solve the problems. These methods are buffers, service areas, facility location models, and clustering. This chapter introduced a new spatial data type, the network dataset. A network dataset is used for estimating travel distance or time on a street network. Tutorial 1 explained what a buffer is and how to specify a buffer’s radius using the buffer tool. This tutorial had me find the buffer zones around swimming pools; this was to determine how many kids are most likely to use the pool. The second tutorial was about creating multiple ring buffers. The third tutorial was about creating multiple ring service areas, calibrated with a gravity model. I followed along with the instructions to create a buffer zone around each open pool on how long it would take to get there. I was able to create the attribute table with the estimated number of pool tags in each area. The fourth tutorial was about using the network analysis tool to locate facilities and determine which ones to keep open. This was determined by maximizing attendance between the pools and creating a map that showed the relationship between pools and block centroids. The fifth tutorial taught me how to perform data cluster analysis. The tool that was used in this tutorial was the multivariate clustering tool. This tool helped create clusters of the data based on gender, age, and the crime committed. From the data, I was able to see a table of information to see the data more easily compared to the map.\u00a0<\/span><\/p>\n

\"\"\"\"<\/p>\n","protected":false},"excerpt":{"rendered":"

Chapter 7- Digitizing\u00a0 The learning goals of this chapter include creating, editing, and deleting polygon features; creating and digitizing point features; using cartography tools to smooth features; working with CAD drawings; and spatially adjusting features. The first tutorial in this chapter introduces moving and rotating existing buildings, adding vertex points, and splitting polygons to edit them to match buildings. When two buildings are drawn as one polygon, the split tool will separate the polygons. The second tutorial is about creating and deleting polygon features. This is important because planners need the open polygons to see the surface and for transportation engineering studies. I created a polygon and deleted 4 polygons. The third tutorial taught me how to use cartography tools. These tools are useful to improve the cartographic quality of polygons. The tool that helps with this is called the smooth polygon tool. Some data has features digitized with a few lines that don\u2019t match the true geography. The smooth polygon tool can help smooth features, which will fix the problem. When smoothing, the smoothing tolerance is important; a lower number gives a more detailed path but will take longer to process. The last tutorial taught me how to transform features. When dealing with computer-aided designs (CAD), the drawings or models aren\u2019t usually in the same units as the GIS software, so this is important to remember.\u00a0 Chapter 8: Geocoding Geocoding is a GIS process that matches locations in data to corresponding fields in existing feature classes. When matching sources to reference data, it is not possible to make exact matches. So there is a fuzzy-matching algorithm that does its best to match the source address to the actual address. The system attempts to use the thought processes and rules that an expert would use to complete the task. This algorithm starts each source with 100 points; each problem encountered when trying to match the source would remove points from the score. If there is more than one candidate, then the candidate with the higher score is chosen for that estimated location. On the off chance that there was a tie between two sources, the GIS software picks one, or the user can choose to have the GIS leave ties alone. The first tutorial in this chapter taught me about geocoding data using ZIP codes, which is a good way to geocode because people normally put the correct ZIP code compared to everything else. I learned how to change the geocode match scores through the geocoding options. I have learned how to rematch addresses and add my own addresses from a point on the map. The last thing I learned in this tutorial was how to use the collect events tool. This tool counted the attendees in each ZIP code and applied a symbol to each ZIP code based on the number. The second tutorial is about geocoding with street addresses. I used the create locator tool again in this tutorial to geocode attendee data by street addresses. I reorganized the fields in my attribute table to compare specific values in the data.\u00a0 Chapter 9: Spatial Analysis This chapter focuses on spatial analysis. Sometimes the map won\u2019t have all the data for users to answer questions and solve problems. There are different types of analytical methods that may be needed to answer the questions and solve the problems. These methods are buffers, service areas, facility location models, and clustering. This chapter introduced a new spatial data type, the network dataset. A network dataset is used for estimating travel distance or time on a street network. Tutorial 1 explained what a buffer is and how to specify a buffer’s radius using the buffer tool. This tutorial had me find the buffer zones around swimming pools; this was to determine how many kids are most likely to use the pool. The second tutorial was about creating multiple ring buffers. The third tutorial was about creating multiple ring service areas, calibrated with a gravity model. I followed along with the instructions to create a buffer zone around each open pool on how long it would take to get there. I was able to create the attribute table with the estimated number of pool tags in each area. The fourth tutorial was about using the network analysis tool to locate facilities and determine which ones to keep open. This was determined by maximizing attendance between the pools and creating a map that showed the relationship between pools and block centroids. The fifth tutorial taught me how to perform data cluster analysis. The tool that was used in this tutorial was the multivariate clustering tool. This tool helped create clusters of the data based on gender, age, and the crime committed. From the data, I was able to see a table of information to see the data more easily compared to the map.\u00a0<\/p>\n","protected":false},"author":2415,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-7766","post","type-post","status-publish","format-standard","hentry","category-course-student-work"],"_links":{"self":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7766","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/users\/2415"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/comments?post=7766"}],"version-history":[{"count":1,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7766\/revisions"}],"predecessor-version":[{"id":7773,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7766\/revisions\/7773"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=7766"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=7766"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=7766"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}