{"id":3847,"date":"2025-01-31T20:00:06","date_gmt":"2025-02-01T01:00:06","guid":{"rendered":"https:\/\/sites.owu.edu\/geog-291\/?p=3847"},"modified":"2025-01-31T20:00:06","modified_gmt":"2025-02-01T01:00:06","slug":"naples-week-3","status":"publish","type":"post","link":"https:\/\/sites.owu.edu\/geog-291\/2025\/01\/31\/naples-week-3\/","title":{"rendered":"Naples – Week 3"},"content":{"rendered":"

Chapter 4:<\/b>\u00a0<\/span><\/p>\n

Chapter four opens discussing density mapping. The purpose of this style of mapping is to more accurately portray clustered information who\u2019s data would be hindered rather than elevated had it been individually mapped. It utilizes a standard unit of measurement, such as square miles, to provide a map with a clearer distribution. One of the main examples for when density mapping is useful is census tracts and counties. The book explains that due to their often arbitrary boundaries, these divisions of land can inaccurately represent data.<\/span><\/p>\n

The chapter discusses the different ways that density mapping can be carried out. Using a dot map or calculating a density map for each area are the two ways given on page 110. A dot map is exactly what it sounds like, a map that you add dots to to represent the data. Rather than one dot representing each individual data point it represents a \u2018specified number of features\u2019\u00a0 (e.g. 1 dot = 100 households). These dots are distributed randomly within each area meaning they don\u2019t represent the data\u2019s specific location either. The closer these dots are together the higher the density of the feature being represented is in that area. In order to calculate the density of an area, \u201cyou divide the total number of features, or total value of the features, by the polygon. Each area is then shaded based on its density value.\u201d There are many map comparisons throughout the explanations that show how density surface becomes more effective when comparing mapped individual features.<\/span><\/p>\n

The chapter also discusses the importance of your search radius. A larger search radius allows the GIS to consider more features when making calculations. While smaller search radiuses allow the GIS to represent more localized variation. It also makes an important emphasis on the point that the radius and density units do not have to be the same.\u00a0<\/span><\/p>\n

 <\/p>\n

Chapter 5:<\/b><\/p>\n

The explanation and example of why to map what’s inside of an area made it much more interesting to read about. It took me a moment to comprehend what was different about \u201cmapping inside\u201d of an area vs creating another kind of map. However, the way in which this chapter explains this to be used as an ever-evolving tool puts it into a much better perspective. Mapping inside of a specified area allows us to monitor what’s occurring inside it, or to compare features from inside several areas. This can often shift the need for action or not. Distinguishing whether or not you need to map inside one or multiple areas will determine how much work is required to create these maps. Mapping inside single areas provides you with a lot, such as; A service area, a buffer, an administrative or natural boundary, a boundary you create, and other features. When mapping instead several areas, you are able to compare the findings, thus comparing these multiple areas.\u00a0<\/span><\/p>\n

Discrete and continuous features were a topic that was very informative. Discrete features exist in well-defined boundaries. They represent things that occur at very specific locations. These would be things such as roads, buildings, etc. Continuous features exist over a broader scale. These are things that take large amounts of distance to change. These are things such as temperatures or elevation.\u00a0<\/span><\/p>\n

I really appreciated the <\/span>A summary of a numeric attribute<\/span><\/i> section. There is nothing better than a book that can be explanation-dense just giving the reader a list of definitions. Thankfully all of the most common ones listed are basic concepts from the Statistics class I am also taking this semester. One of the very helpful aspects that has been very strong in this chapter is the <\/span>What the GIS does<\/span><\/i> headings. As i\u2019m writing this I am going back over one of these sections that refers to overlaying areas with continuous values. Hearing the processes of the software helps me understand the actual uses for these actions.<\/span><\/p>\n

 <\/p>\n

Chapter 6:<\/b><\/p>\n

Chapter six opens with discussing mapping \u2018nearby.\u2019 This is something that I guess I have never really considered to be as extensive as the chapter lays it out being. One part of mapping \u2018nearby\u2019 that intrigues me is how you define what is \u2018nearby.\u2019 As my interests usually lie in urban planning, this often looks different depending on the type of location you\u2019re operating in. According to page 182, \u201cDeciding how to measure \u2018nearness\u2019 and what information you need from the analysis will help you decide which method to use.\u201d The term area of influence also caught my attention whilst reading. The idea of mapping a feature\u2019s impact and scaling it is an extremely useful and exciting feature. Mapping the distances between places like schools and corner stores that sell nicotine products cannot always immediately point out the issues.\u00a0<\/span><\/p>\n

I had a somewhat difficult time understanding what the text was referring to when the phrase costs were used to reference the measurement of something that’s nearby. For a minute I really thought we were talking about gas pricing or how much a subway card costs, when the discussion of time being a cost began on page 184 it started to make a lot more sense. This addresses one of my main concerns about \u2018mapping nearby.\u2019 As the United States has been bulldozed for vehicles, I was concerned that \u2018nearby\u2019 would be confined to a physical closeness.<\/span><\/p>\n



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

Chapter 4:\u00a0 Chapter four opens discussing density mapping. The purpose of this style of mapping is to more accurately portray clustered information who\u2019s data would be hindered rather than elevated had it been individually mapped. It utilizes a standard unit of measurement, such as square miles, to provide a map with a clearer distribution. One of the main examples for when density mapping is useful is census tracts and counties. The book explains that due to their often arbitrary boundaries, these divisions of land can inaccurately represent data. The chapter discusses the different ways that density mapping can be carried out. Using a dot map or calculating a density map for each area are the two ways given on page 110. A dot map is exactly what it sounds like, a map that you add dots to to represent the data. Rather than one dot representing each individual data point it represents a \u2018specified number of features\u2019\u00a0 (e.g. 1 dot = 100 households). These dots are distributed randomly within each area meaning they don\u2019t represent the data\u2019s specific location either. The closer these dots are together the higher the density of the feature being represented is in that area. In order to calculate the density of an area, \u201cyou divide the total number of features, or total value of the features, by the polygon. Each area is then shaded based on its density value.\u201d There are many map comparisons throughout the explanations that show how density surface becomes more effective when comparing mapped individual features. The chapter also discusses the importance of your search radius. A larger search radius allows the GIS to consider more features when making calculations. While smaller search radiuses allow the GIS to represent more localized variation. It also makes an important emphasis on the point that the radius and density units do not have to be the same.\u00a0   Chapter 5: The explanation and example of why to map what’s inside of an area made it much more interesting to read about. It took me a moment to comprehend what was different about \u201cmapping inside\u201d of an area vs creating another kind of map. However, the way in which this chapter explains this to be used as an ever-evolving tool puts it into a much better perspective. Mapping inside of a specified area allows us to monitor what’s occurring inside it, or to compare features from inside several areas. This can often shift the need for action or not. Distinguishing whether or not you need to map inside one or multiple areas will determine how much work is required to create these maps. Mapping inside single areas provides you with a lot, such as; A service area, a buffer, an administrative or natural boundary, a boundary you create, and other features. When mapping instead several areas, you are able to compare the findings, thus comparing these multiple areas.\u00a0 Discrete and continuous features were a topic that was very informative. Discrete features exist in well-defined boundaries. They represent things that occur at very specific locations. These would be things such as roads, buildings, etc. Continuous features exist over a broader scale. These are things that take large amounts of distance to change. These are things such as temperatures or elevation.\u00a0 I really appreciated the A summary of a numeric attribute section. There is nothing better than a book that can be explanation-dense just giving the reader a list of definitions. Thankfully all of the most common ones listed are basic concepts from the Statistics class I am also taking this semester. One of the very helpful aspects that has been very strong in this chapter is the What the GIS does headings. As i\u2019m writing this I am going back over one of these sections that refers to overlaying areas with continuous values. Hearing the processes of the software helps me understand the actual uses for these actions.   Chapter 6: Chapter six opens with discussing mapping \u2018nearby.\u2019 This is something that I guess I have never really considered to be as extensive as the chapter lays it out being. One part of mapping \u2018nearby\u2019 that intrigues me is how you define what is \u2018nearby.\u2019 As my interests usually lie in urban planning, this often looks different depending on the type of location you\u2019re operating in. According to page 182, \u201cDeciding how to measure \u2018nearness\u2019 and what information you need from the analysis will help you decide which method to use.\u201d The term area of influence also caught my attention whilst reading. The idea of mapping a feature\u2019s impact and scaling it is an extremely useful and exciting feature. Mapping the distances between places like schools and corner stores that sell nicotine products cannot always immediately point out the issues.\u00a0 I had a somewhat difficult time understanding what the text was referring to when the phrase costs were used to reference the measurement of something that’s nearby. For a minute I really thought we were talking about gas pricing or how much a subway card costs, when the discussion of time being a cost began on page 184 it started to make a lot more sense. This addresses one of my main concerns about \u2018mapping nearby.\u2019 As the United States has been bulldozed for vehicles, I was concerned that \u2018nearby\u2019 would be confined to a physical closeness.<\/p>\n","protected":false},"author":2292,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-3847","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\/3847","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\/2292"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/comments?post=3847"}],"version-history":[{"count":1,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/3847\/revisions"}],"predecessor-version":[{"id":3848,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/3847\/revisions\/3848"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=3847"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=3847"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=3847"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}