{"id":7362,"date":"2026-09-09T19:26:53","date_gmt":"2026-09-10T00:26:53","guid":{"rendered":"https:\/\/sites.owu.edu\/geog-291\/?p=7362"},"modified":"2026-09-09T19:27:09","modified_gmt":"2026-09-10T00:27:09","slug":"mabailey-week-3","status":"publish","type":"post","link":"https:\/\/sites.owu.edu\/geog-291\/2026\/09\/09\/mabailey-week-3\/","title":{"rendered":"MaBailey Week 3"},"content":{"rendered":"

Chapter 4<\/strong> demonstrates mapping density, which helps understand where things are concentrated. A density map shows where there are higher and lower concentrations of the features being searched for. This is especially useful when a dataset contains so many points that looking at individual locations becomes confusing. For example, a regular map could show the locations of hundreds of businesses, but a density map makes it easier to recognize which parts of a city have the greatest concentration of businesses. Density also makes it easier to compare areas of different sizes because values can be expressed using a consistent unit, such as businesses per square mile.
\nThere are two main approaches to mapping density. 1.) Is defined areas, EX.) counties, ZIP codes, census tracts, or watersheds. How to calculate a density value. Density = number or amount of features \u00f7 area
\nThen there is a dot density map, each dot represents a certain number or amount of something. Instead of locating each actual person, one dot might represent 100 people.
\n2.) Density surface. A density surface is generally created as a raster layer made of cells. GIS looks at the features within a specified neighborhood around each cell and calculates a density value. This creates a continuous looking surface showing areas of high and low concentration.
\nThe search radius is important because it affects how the final pattern looks. A smaller radius reveals more local variation, while a larger radius produces a smoother and more generalized pattern. The density surface can then be displayed with graduated colors or contours.
\nDensity maps allow you to see patterns of concentration. The method you choose should depend on your data and the question you are trying to answer. Mapping by defined areas is useful for comparing established geographic units, while a density surface provides a more detailed picture of where concentrations actually occur.<\/p>\n

Density- The number or amount of something within a specified amount of area.
\nDensity map- A map showing where features or values are highly or lightly concentrated.
\nDefined area- An area with established boundaries, such as a county, census tract, watershed, or ZIP code.
\nDot density map- A map where each dot represents a certain number or amount of something.
\nDensity surface- A continuous-looking representation showing concentrations across an area.
\nRaster- Geographic data represented as a grid of cells.
\nCell- An individual square within a raster dataset that stores a value.
\nSearch radius- The distance around a location that GIS examines when calculating density.
\nConcentration- The degree to which features are grouped within an area.
\nAreal unit- A standard unit of area used to calculate density, such as acres or square miles.
\nCentroid- A point representing the center of a geographic area.
\nContour- A line connecting locations with the same value.
\nGeneralization- Simplifying geographic information to show broader patterns.<\/p>\n

Chapter 5<\/strong> focuses on what is inside a particular area. people need to know which geographic features are located within a boundary. Data about what’s inside can be used either to monitor or to compare multiple areas based on the features that are being searched. EX- Environmental scientists could determine which streams, wetlands, forests, or animal habitats occur inside a protected area. Emergency managers could determine which neighborhoods fall within a flood zone. Police departments could compare the number of crimes occurring within different districts.
\nThe chapter demonstrates three ways of finding what’s inside. 1- Drawing\/selecting an area boundary over geographic features. This allows you to visually see what is located within the area. It typically does not provide detailed calculations.2- Selecting features within the boundary. GIS can identify those features and allow you to examine them. EX. You could select all businesses within a developed district and calculate how many employees might work at the various businesses within that district and boundary. .3- Overlaying areas and features. Overlay combines layers so GIS can calculate more detailed information about their spatial layouts. This is especially useful when features cross the boundary. EX. a forest might only be partly inside a protected area. Overlaying the layers allows GIS to determine how much of that forest actually falls within the boundary. Mitchell gives a similar example of using overlay to calculate different land cover types inside protected areas. GIS can identify, summarize, and compare features based on whether they occur within a particular geographic area.<\/p>\n

Inside- A spatial relationship in which a feature occurs within the boundary of another feature.
\nBoundary- The line defining the limits of a geographic area.
\nSelection- Identifying specific features that meet geographic or attribute requirements.
\nOverlay- Combining geographic layers to examine how their features overlap or relate.
\nSpatial relationship- The geographic relationship between two or more features.
\nLayer- A collection of similar geographic information displayed together in GIS.
\nPolygon- A closed geographic shape representing an area.
\nBuffer- An area created around a geographic feature at a specified distance.
\nAdministrative boundary- A human-created boundary, such as a county, state, school district, or police district.
\nClip- A GIS operation that removes features or portions of features outside a boundary.
\nSummarize- To calculate information about a group of features, such as their number, total area, or average value.<\/p>\n

Chapter 6<\/strong> is about finding what is nearby a geographic feature. EX. Environmental managers could identify land within a certain distance of streams to protect water quality. Emergency services could determine which streets a fire department can reach within a certain amount of time. Businesses could also determine how many potential customers live within a certain driving time of a store. There are three major ways of measuring nearness, straight-line distance, distance or cost over a network, and cost over a geographic surface.
\nStraight-line distance is the simplest method. It measures the direct distance between features without considering roads, barriers, terrain, or other factors. Buffers are commonly used with this type of analysis. Straight-line distance is not realistic. EX). If you’re studying how quickly an ambulance can reach a house, the ambulance cannot simply travel in a straight line across buildings and rivers. It has to follow roads. That is where network analysis becomes useful. A network represents connected paths such as streets. Cost does not only mean money. A travel cost can be time, distance, money, or effort. For example, traffic increases the time required to reach a location even if the physical distance stays the same.
\nCost over a geographic surface. Instead of only roads, GIS assigns different travel costs to different areas of the landscape. This is useful for things such as wildlife movement or determining the easiest route across terrain.Two locations may be physically close together but difficult or time consuming to travel between. Choosing the appropriate measure of nearness makes the analysis much more meaningful.
\nNearby- Features occurring within a specified distance or travel range of another feature.
\nProximity- How close one geographic feature is to another.
\nStraight line distance- The shortest direct distance between two locations.
\nBuffer- An area created at a specified distance around a point, line, or polygon.
\nSource feature- The feature from which distance or travel is measured.
\nTravel range- The area that can be reached within a specified distance, time, or cost.
\nNetwork- A connected system of paths, such as streets, railroads, or pipelines.
\nNetwork analysis- GIS analysis involving movement along connected routes.
\nTravel cost- The amount of time, distance, money, or effort needed to move between locations.
\nCost surface- A geographic surface representing how difficult or expensive it is to move through different locations.
\nService area- The geographic area that can be reached or served by a facility.
\nArea of influence- The surrounding area that may be affected by a geographic feature or activity.
\nDistance-The amount of space separating two geographic locations.
\nGeographic surface- A representation of values that vary continuously across an area..<\/p>\n","protected":false},"excerpt":{"rendered":"

Chapter 4 demonstrates mapping density, which helps understand where things are concentrated. A density map shows where there are higher and lower concentrations of the features being searched for. This is especially useful when a dataset contains so many points that looking at individual locations becomes confusing. For example, a regular map could show the locations of hundreds of businesses, but a density map makes it easier to recognize which parts of a city have the greatest concentration of businesses. Density also makes it easier to compare areas of different sizes because values can be expressed using a consistent unit, such as businesses per square mile. There are two main approaches to mapping density. 1.) Is defined areas, EX.) counties, ZIP codes, census tracts, or watersheds. How to calculate a density value. Density = number or amount of features \u00f7 area Then there is a dot density map, each dot represents a certain number or amount of something. Instead of locating each actual person, one dot might represent 100 people. 2.) Density surface. A density surface is generally created as a raster layer made of cells. GIS looks at the features within a specified neighborhood around each cell and calculates a density value. This creates a continuous looking surface showing areas of high and low concentration. The search radius is important because it affects how the final pattern looks. A smaller radius reveals more local variation, while a larger radius produces a smoother and more generalized pattern. The density surface can then be displayed with graduated colors or contours. Density maps allow you to see patterns of concentration. The method you choose should depend on your data and the question you are trying to answer. Mapping by defined areas is useful for comparing established geographic units, while a density surface provides a more detailed picture of where concentrations actually occur. Density- The number or amount of something within a specified amount of area. Density map- A map showing where features or values are highly or lightly concentrated. Defined area- An area with established boundaries, such as a county, census tract, watershed, or ZIP code. Dot density map- A map where each dot represents a certain number or amount of something. Density surface- A continuous-looking representation showing concentrations across an area. Raster- Geographic data represented as a grid of cells. Cell- An individual square within a raster dataset that stores a value. Search radius- The distance around a location that GIS examines when calculating density. Concentration- The degree to which features are grouped within an area. Areal unit- A standard unit of area used to calculate density, such as acres or square miles. Centroid- A point representing the center of a geographic area. Contour- A line connecting locations with the same value. Generalization- Simplifying geographic information to show broader patterns. Chapter 5 focuses on what is inside a particular area. people need to know which geographic features are located within a boundary. Data about what’s inside can be used either to monitor or to compare multiple areas based on the features that are being searched. EX- Environmental scientists could determine which streams, wetlands, forests, or animal habitats occur inside a protected area. Emergency managers could determine which neighborhoods fall within a flood zone. Police departments could compare the number of crimes occurring within different districts. The chapter demonstrates three ways of finding what’s inside. 1- Drawing\/selecting an area boundary over geographic features. This allows you to visually see what is located within the area. It typically does not provide detailed calculations.2- Selecting features within the boundary. GIS can identify those features and allow you to examine them. EX. You could select all businesses within a developed district and calculate how many employees might work at the various businesses within that district and boundary. .3- Overlaying areas and features. Overlay combines layers so GIS can calculate more detailed information about their spatial layouts. This is especially useful when features cross the boundary. EX. a forest might only be partly inside a protected area. Overlaying the layers allows GIS to determine how much of that forest actually falls within the boundary. Mitchell gives a similar example of using overlay to calculate different land cover types inside protected areas. GIS can identify, summarize, and compare features based on whether they occur within a particular geographic area. Inside- A spatial relationship in which a feature occurs within the boundary of another feature. Boundary- The line defining the limits of a geographic area. Selection- Identifying specific features that meet geographic or attribute requirements. Overlay- Combining geographic layers to examine how their features overlap or relate. Spatial relationship- The geographic relationship between two or more features. Layer- A collection of similar geographic information displayed together in GIS. Polygon- A closed geographic shape representing an area. Buffer- An area created around a geographic feature at a specified distance. Administrative boundary- A human-created boundary, such as a county, state, school district, or police district. Clip- A GIS operation that removes features or portions of features outside a boundary. Summarize- To calculate information about a group of features, such as their number, total area, or average value. Chapter 6 is about finding what is nearby a geographic feature. EX. Environmental managers could identify land within a certain distance of streams to protect water quality. Emergency services could determine which streets a fire department can reach within a certain amount of time. Businesses could also determine how many potential customers live within a certain driving time of a store. There are three major ways of measuring nearness, straight-line distance, distance or cost over a network, and cost over a geographic surface. Straight-line distance is the simplest method. It measures the direct distance between features without considering roads, barriers, terrain, or other factors. Buffers are commonly used with this type of analysis. Straight-line distance is not realistic. EX). If you’re studying how quickly an ambulance can reach a house, the ambulance cannot simply travel in a straight line across buildings and rivers. It has to follow roads. That is where network analysis becomes useful. A network represents connected paths such as streets. Cost does not only mean money. A travel cost can be time, distance, money, or effort. For example, traffic increases the time required to reach a location even if the physical distance stays the same. Cost over a geographic surface. Instead of only roads, GIS assigns different travel costs to different areas of the landscape. This is useful for things such as wildlife movement or determining the easiest route across terrain.Two locations may be physically close together but difficult or time consuming to travel between. Choosing the appropriate measure of nearness makes the analysis much more meaningful. Nearby- Features occurring within a specified distance or travel range of another feature. Proximity- How close one geographic feature is to another. Straight line distance- The shortest direct distance between two locations. Buffer- An area created at a specified distance around a point, line, or polygon. Source feature- The feature from which distance or travel is measured. Travel range- The area that can be reached within a specified distance, time, or cost. Network- A connected system of paths, such as streets, railroads, or pipelines. Network analysis- GIS analysis involving movement along connected routes. Travel cost- The amount of time, distance, money, or effort needed to move between locations. Cost surface- A geographic surface representing how difficult or expensive it is to move through different locations. Service area- The geographic area that can be reached or served by a facility. Area of influence- The surrounding area that may be affected by a geographic feature or activity. Distance-The amount of space separating two geographic locations. Geographic surface- A representation of values that vary continuously across an area..<\/p>\n","protected":false},"author":2426,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-7362","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\/7362","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\/2426"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/comments?post=7362"}],"version-history":[{"count":1,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7362\/revisions"}],"predecessor-version":[{"id":7363,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7362\/revisions\/7363"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=7362"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=7362"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=7362"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}