Category: Course Student Work

Preface

Based on the preface, it seems like this book will walk us through how to use ArcGIS Pro step by step. Additionally, we will also learn methods in ArcGIS online, ArcGIS StoryMaps, and other ArcGIS software. Specifically with ArcGIS Pro, it will be teaching us how to use the GIS application to create, use, and analyze professional 2D and 3D maps. The goal of reading this book is to learn how to effectively use GIS to integrate it into and address real-world problems. I appreciate the layout of the chapters since it first explains the concepts and then allows you to do the tutorial hands on. After reading this book, I hope to have enough knowledge of GIS to use it in potential research and possibly my future career.

Chapter 1

Chapter 1 served as an introductory course on how to use a variety of basic navigations and tools in GIS. One of the most fundamental tools introduced in this chapter was the use of the contents page. The contents page allows you to add and remove different features or layer classes and is, as I learned later, an important hub for editing for features. Some map making features that were introduced in this chapter include how to add basemaps, how to change symbols, and how to add, remove, and label feature classes. I also learned all about map viewing and zooming. Knowing how to zoom in and out, change the viewing zone, zoom into specific features, create bookmarks, zoom to full extent, and search for specific features will all be useful tools for analyzing maps in the future. I also learned how to export a layout and turn the map into an image with a photo viewer. This skill is useful when I need to share my map and make it accessible to other people. Overall, I liked how the chapter gives you straight forward instructions on how to find and do things so it is easy to navigate. Throughout the chapter, I definitely started to realize that I remembered how to work and locate different features in GIS. However, I found the tutorials a little bit repetitive, as you often do a skill and then delete the work to reset.

Key Concepts/Definitions

Feature Class: Basic building block for displaying geographic features on a map. A homogeneous layer on the map. Vector data that have corresponding attributes for each feature.

Raster: Type of spatial data. An image made up of small pixels. A raster encoded with geographic information can be used as a layer in a map.

Chapter 2

In this chapter, I learned all about how to create, design, and symbolize thematic maps. I was first introduced to symbology, or the visual elements to represent geographic features or attributes. From this, I was able to learn how to change the colors of polygons, symbolize ground features, and make graduated and proportional symbols. Symbology was a very important learning concept throughout this chapter. After these tutorials, I now feel comfortable with implementing symbol changes and where to go to do so. I was also introduced to the process of making two different types of thematic maps: choropleth and dot density. I enjoyed these types of maps because it was easy to see how the values were distributed. I also think that while the 3D choropleth map is not a necessity, it adds another element that helps display features. Additionally, while making these maps, I learned how GIS creates histograms based on the data and how they can be altered. This skill will be useful to know when analyzing the map through statistical analysis. Other important skills I learned in this chapter included how to label features, remove duplicate layers, remove pop ups for features, set visibility ranges, and how to create a definition query.

Key Concepts/Definitions

Thematic Map: Strives to solve or investigate a problem. Consists of a subject layer or layers placed in spatial context with other layers. To make this map you must answer: What layer or layers are needed to represent a subject? What spatial context layers are needed to orient map users to recognize locations and patterns of the subject feature?

Definition Query: Used to filter the features of a layer rather than select a temporary subset of features to work with.

Choropleth Map: Uses colors in polygons to represent numeric attribute values. Uses classification methods, that depend on the data and intent of the map, to display the data.

Map Scale: Ratio between distance A and B (one inch) on your computer screen divided by the distance between the two same points in inches on the ground.

Chapter 3

This chapter was all about making suitable maps to present and how to share your maps with people beyond ArcGIS Pro. The first section of this chapter was how to make a layout. Layouts are mostly used to share information in formal settings such as presentations or reports. The layout contained the map, a title, and a legend. When making the layout, I especially appreciated the ruler and guideline features of GIS. These features ensured the maps were even and gave them a professional look. From these layouts, I learned how to create different charts from the maps and how to highlight certain subsets of features from the charts. For example, in one of the tutorials I highlighted the top ten states with the highest arts employment. 

The rest of the chapter focuses on the online sections of ArcGIS. I first learned how to publish a map I made on ArcGIS Pro to ArcGIS Online. Publishing to ArcGIS Online is extremely useful in sharing your map with a wider audience since it is more accessible than a desktop and ArcGIS Pro. I then received a tutorial on how to create a story through StoryMaps that showcases the data and reason behind a map. To me, StoryMaps would be the most useful for sharing with the general public because the story provides context and instructions on the meaning of the map. I particularly enjoyed the inclusion of accessibility features and sidecar blocks in StoryMaps. These features made the story easy to navigate and further expanded the range of audience. I then learned how to create a briefing. The information was similar to the story section, but formatted the information in a way that would be useful for a presentation. Lastly, I was introduced to the concept of creating a dashboard. Dashboards are useful tools for allocating resources in response to changing goods or services over time.

Mapping Density

• Density mapping shows where things cluster instead of where each single feature sits hence it is good for patterns, bad for pinpointing.
• Matters most when your polygons are different sizes; a raw count map makes a big polygon look busy just because it is big.
• Two ways you can go about it:
  1. Area method: We divide features by polygon area, or use a dot map. The dots are placed randomly, so they are a picture of density, not real locations. That feels like a trap for anyone who does not read the fine print.

  2. Density method: A raster where every cell gets a value from the features inside a search radius.

• Small cells give a smoother surface but cost processing time. A bigger radius smears the pattern out. 
• The simple method just counts what is in the radius; the weighted method leans toward features near the cell centre and gives a cleaner map.
• Display with graduated colours or contour lines.

So we have these classificational schema; Natural Breaks, Quantile, Equal Interval, Standard Deviation which basically decides what the map tells, which is a lot of power for one dropdown. I keep wondering how often public data gets quietly skewed by someone picking an arbitrary search radius. Still, density fixing the unequalnpolygon problem is the real win here.

Finding What’s Inside

• Mainly for monitoring or comparing; some examples include; drug arrests near a school, or which zip code has more of something.
• Three methods:
  1. Area > Features: More visual. Fast glance, No data out of it.
  2. Select Features:  Gives us a subset we can actually use for lists and summary stats (count, frequency, sum, average). 
  3. Overlays: Merges boundaries and features into a new layer and permanently tags features with the area’s attributes. Vector is precise but leaves slivers; raster counts cells, faster but cell size drives everything.

To be honest Vector still wins for anything legal like parcel boundaries, because “close enough” does not hold up in a property dispute. However, for non high stakes thing the latter should be good enough.

Finding What’s Nearby

• In this chapter you ask some other different questions such as who is affected by an event, who is actually served by a facility and so on. 
• “Near” is not only physical distance. It can be time, money, or effort, which changes the decision process.
• Three methods:
  1. Straight line: Buffers, Select within distance, or a continuous distance surface. Planar for a flat plane, geodesic for a curved earth. Ranges can be inclusive rings (0–1, 0–2, 0–3 mi) or distinct bands (0–1, 1–2, 2–3 mi).
  2. Cost over a network: Streets and other fixed infrastructure. 
  3. Cost over a surface: Overland travel with no roads. 

The  RINGS vs BANDS distinction is the most useful thing in this chapter for me. Bands isolate each ring, so if I ever compare demographics by distance from a facility, that is the one I want. Building turntables from scratch sounds genuinely tedious, but for emergency routing I do not see how you skip it.

 

Chapter Four

Density mapping is a useful tool, especially for analyzing and tracking patterns in a data set. As Mitchell introduced this topic to the reader, I wondered which field of study uses density maps the most, as density maps could be useful in many types of measurements across multiple fields. As population density is a common measurement to map and analyze, I wonder what interdisciplinary work is performed by both geographers and other unrelated fields such as demographers, epidemiologists, other health fields, or economists. Additionally, I thought about what types of data would not benefit from density mapping, or the specificity needed for a density map based on what the target demographic of the map is. In the next section, Mitchell highlights that density by defined area is better for comparing data with defined areas, with density by density surface is better for seeing the patterns or lines of data in detail. Something I found interesting in this chapter is learning that the GIS software places dots randomly in areas of a density map, and clarifies that density maps do not directly correlate with features unless adjusted. Additionally, another interesting fact is that GIS doesn’t have a set distance unit, so users can use miles, kilometers, acres, etc.

Important vocabulary for this chapter:

• Density of features: locations, broad data to show areas of importance. Ex: locations of a chain grocery store
• Feature values: the values within a density of features, usually more specific. Ex: employees who work at the chain grocery store
• Density by defined area: calculated by dividing total number of features/total value of features by the area of the polygon containing the feature. Calculated with [op_density = total_pop / (area / 27878400)]
• Density by density surface: calculated by each cell in a raster layer in GIS getting a density value. Usually more specific, but more effort.

Chapter Five

In this chapter, Mitchell starts by highlighting the importance of monitoring and mapping the insides of an area, especially in emergency situations such as chemical spills or other disasters, either natural or human-influenced. To define a data analysis of an area, Mitchell emphasizes the importance of defining a single area or multiple areas. In a single area, one county, geographic area, radius around a central point, or another relatively small area is drawn in order to fully define and understand the data analysis. In a multiple area analysis, areas are drawn through geographic boundaries, around multiple counties, or through different habitats. In the next section, Mitchell highlights steps on how exactly to merge and analyze both discrete and continuous features, specifically floodplains and and use types in this example: Find out whether a particular parcel is inside the floodplain, get a list of all parcels inside the floodplain, count the number of parcels inside the floodplain, use a map of the land and its uses to get an exact reading of which parcels of land used for different purposes are affected by the preexisting floodplain. Thinking on this topic, I wonder how exactly cities were planned pre-GIS technology, or if they were planned at all, especially cities that face heavy precipitation and rely on floodplains to prevent flooding. Additionally, I wonder if GIS technology helps decide different factors of historic buildings, such as which buildings are weather or waterproofed, which historic buildings are moved altogether, and which buildings have to be torn down or otherwise modified.

Important vocabulary for this chapter:

• Discrete features: unique, identifiable features that can be counted quantitatively. Ex:  locations, animal nests, rivers, or parcels of land
• Continuous features: features that cross set boundaries such as administrative boundaries. Ex: map of participation, soil types, or elevation.

Chapter Six

Chapter Six is dedicated to the importance of mapping nearby features, which initially makes me think back to the earlier chapters, where Mitchell specifically highlights the importance of using well-known landmarks in an area to make a map more legible to a larger audience. Closeness, being a relative scale, can be tricky to map. Mitchell suggests using highly traveled areas or other factors in order to map closeness, for example, mapping closeness by mapping between two cities that are commonly traveled between. Additionally, something nearby can be measured not only by distance, but also by cost, such as land types that cost the same to travel to, or two areas that cost relatively the same to travel to. From there, distance could either be calculated while taking the curvature of the Earth into account, which is called a geodesic model, or disregarding the curvature of the Earth, called the planar model.

Important vocabulary for this chapter:

• Planar model: A model and analysis of the Earth that does NOT take into account the curvature of the Earth, and instead reads the Earth as flat
• Geodesic model: A model and analysis of the Earth that DOES take the curvature of the Earth into account
• List: lists certain geographic IDs, such as addresses
• Count: total number of a certain data set, such as number of 911 calls in an area
• Summary: A total amount of something, or total amount by category, such as acres of land in a watershed, or number of acres of forests out of forests, hills, rivers, etc.
• Statistical summary: the average, minimum, mean, maximum, and standard deviation
• Inclusive rings: used to demonstrate how total amount increases as distance increases, using the radius around a point
• Distinct bands: overlapping inclusive bands, used to show amount in proportion to distance by two different distances.

 

Chapter 4: Mapping Density

Chapter 4 explains why mapping density is more useful than mapping totals. Density is the concentration of features or values within a uniform unit of area, such as people per square mile. This matters when areas being compared are different sizes: a large county may have more people, while a smaller county may actually be more crowded. Before making the map, we need to decide whether we are mapping feature density (the number of locations, such as businesses) or feature-value density, such as the employees at those businesses.

There are two main methods. The first is mapping density by a defined area, such as a county, ZIP code, or census tract. GIS divides the count or value by the polygon’s area, and the result can be shown through shaded areas or a dot-density map. In a dot-density map, each dot represents a set amount, but the dots usually do not show exact locations. Choose dot value and size carefully so they show the pattern rather than hide it. One weakness is that a single density value applies to the whole polygon, even though density may vary within it.

The second method is creating a density surface, a raster layer in which each cell receives a value based on nearby features. Cell size controls how fine or coarse the pattern appears, while search radius determines the size of the neighborhood used in the calculation. A larger radius creates a smoother pattern; a smaller radius reveals more local variation. I found it interesting that these choices can change the story the same data tells. This made me think about lottery retailers: mapping store locations would show retailer density, while weighting stores by sales would show a different pattern. My question is: how do analysts select a cell size and search radius that reveal the pattern without exaggerating it?

Chapter 5: Finding What’s Inside

Chapter 5 focuses on finding what is inside an area. This analysis can monitor one area or compare several, such as determining which properties fall within a floodplain or how many incidents occurred inside each police district. Before starting, we must identify whether the features are discrete or continuous. Discrete features are separate and identifiable, such as buildings, roads, or crime locations. Continuous features vary across a surface, such as elevation or rainfall. We also need to decide whether the result should be a list, a count, or a statistical summary, and whether to include partially contained features.

The chapter describes three methods. Drawing the area and features together is simple and fast because it visually shows what is inside, but it produces little analytical information. Selecting features inside an area creates a subset you can list or summarize, which works well for a single area. Overlaying creates a new layer containing attributes from both the area and feature layers. It takes more processing but is most useful for comparing multiple areas or calculating how much of a feature falls inside each one. A buffer, an area created at a specified distance around a feature, can also serve as the analysis boundary.

GIS can then calculate counts, frequencies, sums, averages, medians, and standard deviations. Count is the total number of features, while frequency is the number in each category. Because I am used to working with tables, overlaying reminded me of joining datasets, except the match is based on location instead of a common ID. One point I found important is that the analyst’s definition of “inside” can change the result. For example, should a property count as flood-prone if only a tiny corner overlaps the floodplain? My question is: what rule should be used for partial overlaps, and how should that decision be explained so the map does not mislead the audience?

Chapter 6: Finding What’s Nearby

Chapter 6 explains how GIS finds what is nearby, but “nearby” does not always mean the shortest straight-line distance. It can be defined by distance or travel cost. Cost can mean time, money, fuel, effort, or another difficulty. This analysis can show how quickly a fire station can reach a neighborhood or which customers are within a store’s service area. First, we decide whether to measure proximity outward from a feature or by travel to it, whether we need a list, count, or summary, and how many distance ranges to show.

The chapter describes three methods. Straight-line distance is the shortest direct distance between features and is useful for making buffers or selecting everything within a fixed radius. Planar distance works for smaller study areas, while geodesic distance accounts for the Earth’s curvature across large regions. Distance or cost over a network follows connected features such as streets. A network contains edges, junctions, and turns, and you can assign an impedance to each part, such as travel time. Cost over a geographic surface uses raster cells to represent movement difficulty. Hills, forests, water, or other barriers can make a location costly to reach even when it looks close.

Results can be displayed as a single range, inclusive rings, or distinct bands. Inclusive rings show cumulative totals within increasing distances, while distinct bands separate features into each interval. A spider diagram connects a source to nearby features with lines. This chapter made me think about traveling between Delaware and Cleveland: straight-line distance tells me little compared with roads, bus connections, waiting time, and actual cost. I liked this chapter because it shows that proximity is really about accessibility, not just miles. My question is: when traffic, weather, or road closures change constantly, how often must network costs be updated for the analysis to remain reliable?

Chapter 4

Chapter 4 talks about density(the number of things within a certain area/ volume) It talks about two ways to show density with the density by area model and the density by defined area or by surface. Density by defined area takes a distinguishable area(ex: county, zip code, etc) and collects the number of features and displays the area as a certain color based on the gradient number that the area has been given. Density by surface does not require defined borders and the range is editable. To find the density for either of these to find its range on the color coded gradient you must find the number of features and divide that by the area of which you are finding the density for. 

 

I face some confusion about the representation of density through defined cells. Is the total map split up into equal cells and the number of the features counted and then the circle is placed in the middle of the cell or is the cell based off of the location of the features being studied(ex: businesses, employees, etc)Or does the GIS find the average distance between all points in the cell and move the circle to that point.

 

I enjoy that they often include what one particular map might be used for and by whom it might be used. It is good to have a clear explanation of how these techniques are used by everyday people and businesses.

 

Chapter 5

This chapter “finding what’s inside” had a focus on defined areas and how you can manipulate both their visual properties and informational properties to gain insight. The example of a flood plain was used a lot in this chapter to show how different areas can be overlaid with each other. You can take a group of defined areas(in the text it was personal properties categorized by their use) and overlay something like a floodplain or a watershed above the initial scape. You can also emphasize different parts of the map, for instance you can only include parts of the property area that are included in the flood plain area or you can have all properties with a part in the floodplain highlighted. The text makes it clear that this sort of mapping would be very important for things like watershed access, flooding, fire protection, school districts and more.

 

The text also made some comparative examples with drawing areas vs. Selecting the features inside the area. Drawing does not require any complex calculations so it is much easier to do but cannot list information so it is visual only. The other option selecting the features is good for knowing what is inside a group of areas but does not allow you to analyze areas individually. There is also the third option of overlay which lists multiple layers and allows you to draw conclusions based on which things overlay each other. The main drawback to this though is that it uses a lot of computational power. 

 

An interesting question one might ask is how can these three methods be used together to make more meaningful conclusions?

 

Chapter 6

I think that it is an interesting idea to map the distance of something by cost. I wish you could do that in google maps to see how much you would need to spend on fuel.

 

This chapter analyzed the display of distance and how we show paths. A lot of the information seems like it would be tied to GPS but there was also some discussion of how it could be used by police stations, fire stations, etc… to understand their travel range. There was also discussion of the variable of cost which was a different way of looking at distance not in terms of location but in resources used to reach a point. I believe that this is a way to incorporate terrain changes into the mapping process.

 

There is also the sectioning of distances using inclusive rings which include all features from the outside to the center of the circle and distinct bands which are different distances from the center point and can indicate the number of features within a distinct distance band.

Another interesting style mentioned in the text was spider graphs which send a point straight to a feature within a certain range.

 

Switching from distance to cost creates a more detailed contour map because of the terrain features mapped.

Chapter 4:

Chapter 4 discusses how to map density. Mapping density is important to show the concentration of features and their patterns. For density, the number features are measured in uniform areal units. As with other GIS methods, careful consideration must be taken when deciding what to map. You have to consider if the data is plots, lines, or data summarized by data. You also have to consider whether you are mapping features or feature values. There are two main ways of mapping density, mapping by a defined area and mapping by a density surface. When mapping by a defined area density of individual features summarized by defined areas and each dot represents a set number. It doesn’t represent the exact location, but it’s easier to read than having one dot per location. When mapping by defined area density is calculated using the areal extent of each polygon. Dot maps are often used for mapping by defined area. They are based on the total count and how much each dot represents. There are several factors to keep in mind when making dot maps, like dot size, how much each dot represents, and area size. When mapping by density surface, raster layers are used and the total of features are divided by area. These maps offer more detailed information but require more effort. When deciding how to display the map it is important to consider aspects like cell size and search radius. These factors are important for making sure that the data is displayed in a way to properly display the patterns. The colors used for classes and how the breaks for classes are assigned. This chapter was helpful for understanding how to map density and the uses of it. There were a few points in this chapter where I struggled to understand the technical stuff, so I hope things will become more clear when actually using the GIS.

Chapter 5:

In chapter 5, the concept of “mapping what’s inside” is discussed. Mapping what is inside of a determined area is useful for monitoring what’s occurring inside it or to compare what’s inside several areas. There are several questions that need to be answered before deciding how to go about mapping. Are you finding what’s inside a single area or several? A single area lets you monitor activity or summarize information, while several areas lets you compare areas. Are the features inside discrete or continuous? Discrete features can be counted, listed, or summarized, while continuous features can be summarized. Do you need a list, count, or summary? The data needed is different if you want to get a list of all features, a total number of features or a summary of what’s inside of an area. Do you need to see the features that are completely or partially inside the area? Linear and discrete features may lie partially inside and outside of an area, so you must decide what to do in these situations. There are three ways to map what’s inside of an area. These include drawing areas and features, selecting features inside an area, and overlaying the areas and features. For drawing areas and features are drawn on top of features, which is good for seeing whether one or a few features are in an area. When selecting features inside an area you specify the area and the layer containing the features and selects a subset of features in the area, which is good for getting a list or summary of features in an area. When overlaying the areas and features the GIS creates a new layer with the attributes of area and features, which is good for finding which features are in each of several areas or how much of a feature is in one or more areas. This chapter did a good job at explaining what it means to map what’s inside an area.

Chapter 6:

Chapter 6 discussed the concept of mapping what is nearby a location. When mapping what is nearby, you can find out what is occurring within a set distance of a feature, identify what’s affected by an event or activity, and understand the cost of travel. The cost can be in regards to distance, time, monetary loss, effort, or other costs. As it seems to be a common occurrence with this book, the chapter discussed questions that you must ask yourself before you begin mapping. Is what’s nearby defined by a set distance or by travel to or from a feature?  You should measure surrounding features using straight lines for distance or a geometric network using road data for travel. Are you measuring what’s nearby using distance or cost? Measuring by distance or cost can change the result significantly in some cases. Are you measuring distance over a flat plane or using the curvature of the earth? You should use the curvature of the earth for large areas. Do you need a list, count, or summary? It is important to understand what kind of data you need to achieve your desired map. How many distance or cost ranges do you need? You could have one range, inclusive rings, or district bands. The three ways to map what’s near a location are straight-line distance, distance or cost over a network or cost over a surface. When mapping straight line distance you specify the feature and distance and the GIS finds the area or features within the distance. This method is good for creating boundaries or selecting features at a set difference. When mapping distance or cost over a network you specify source location and distance/cost along a linear feature (like roads) and the GIS finds segments within distance/cost. This method is good for finding what’s within a distance/cost of a location. When mapping cost over surface you specify location and travel cost and the GIS creates a layer showing travel cost from feature. This method is good for calculating overland travel cost. Overall, I feel like this chapter thoroughly explained the concepts of mapping what is nearby a location and how it can be used. There were also some connections made to chapter 5, which I found helpful.

Chapter 4

This chapter was all about how you would go about mapping density in GIS software. Obviously, with discrete points, you can observe density to some extent by looking at where a large amount of dots in a small amount of space are, but mapping specifically for density can give you a more specific understanding of which “dense” places are more or less so, rather than just identifying clusters. There are methods of mapping density that use dots randomly placed based on ratio per area by GIS, but this is also visually more accurate to look at than raw data, AND there are ways to make it more visually accurate, like processing this information in small areas.

There are many different ways to map density, including by area and with a density surface. Mapping by area can output a shaded fill map or a dot density map. This type of mapping is good for data that has already been summarized by area or that can be summarized by the GIS. However, it doesn’t pinpoint centers of density and can be very inaccurate for large areas because of this.

A density surface, which is created with the help of the GIS, is good for individual locations, sample points, and lines, so more discrete data. It can output a shaded or contoured map. Compared to mapping by area, it is more precise at pinpointing centers of density, but it requires more data processing.

For already defined areas, density maps can be in the form of dots or shades. As with everything, choosing one of these depends on what kind of information you are working with and what you want your map to convey. It is important to make the information you aim for as easily understood as possible.

Parameters to consider in density mapping are, cell size (if you are using a raster map), search radius, calculation method, and units.

Chapter 5

Chapter 5 was about graphic analysis and how to combine more than one layer of a map to create a map that can show correlation. Rather than focusing on physical features, this chapter talked about how to focus on occurrences in an area and can show viewers where attention is necessary.

There are two main ways to define your analysis, one being in separate layers, and another being a huge combined layer with all information in one.

Finding what’s inside a single area lets you monitor activity or summarize information about an area. This includes:

• Service area around a central facility
• A buffer defining distance around a feature
• Administrative or natural boundaries
• Manually-created area (for some sort of proposal)

For any of these when dealing with several layers of a map intersecting, you can choose to include all features that are even somewhat in your “boundary” layer, include only features that are fully in the boundary, or include parts of features whenever they show up in the boundary of the other layer. Which of these is chosen depends, again, on the map you are creating. It is best to create a map that makes it easiest to see features and important patterns, but from what the book described, this is a pretty intuitive process.

The GIS can place features that occur in both layers in a table, but if there are multiple areas within your data, it cannot recognize them as separate. I would imagine that if every area was a different set of layers, the GIS could recognize them as different that way, but this probably is very tedious and requires lots of processing.

The process GIS goes through to help with this is also described in this chapter. GIS can choose the best overlay method based on your data, and if it chooses a format your map currently isn’t in, it can convert it to that format in order to move forward with the processing.

Choosing a vector map can give you a more precise measure of areal extent, but requires lots of processing to remove silvers (errors in matching layers up) and to calculate the amount of each category.

Choosing a raster map can have varying precision based on the cell size used, and small cell sizes, which are more accurate, require more processing like a vector map would. However, raster maps don’t create the issue of silvers, they are faster to create, and they automatically calculate area extent. It is slower than vector processing though. I think personally that this is the better method as long as you use small cell sizes.

Chapter 6

Chapter 6 addressed how to represent how nearby activity can affect data between two features or using a set distance or cost in the form of a sort of “radius” around a certain area. This radius can have multiple levels if it helps to convey closeness better.

There is also an option of whether or not to include curvature of the earth depending on how large of an area your map is covering. I would imagine that it is also important to account for geographic features or barriers that would change the cost of travel, like if there is a mountain to be climbed or dodged.

There are 3 main ways nearby activity can be represented.

1. Straight line distance: specify the source and the radius, and the GIS creates a nice circle using that information. It is good for creating a boundary or selecting features at a set distance, and you only need two very simple layers to do it.
2. Distance or cost over a network: specify source location and distances along each linear feature you want included. This one is good for finding what is within a specific travel distance of a location accounting for means of getting there (like roads). You need 3 layers, but one of them can be directly from ArcGIS
3. Cost over a surface: specify the source features and a travel cost, and the Gis will show the travel cost as it grows from each feature. This is sort of like the sorted radius, but it is based on cost and not distance. I would assume it accounts for geographic features as mentioned before.

The chapter also goes more in depth about how to actually get these different things to work in the software, like how to get a map to show distance from feature to feature, and how to make the map have several distance ranges. You can also set several different source features, and have the distances set to be “near at least one” of them after defining both.

Overall, so far from reading these chapters, I am getting the gist of how GIS works, but a lot of the specifics on how it works and what to do when I want specific things to happen is losing me a bit without having been on the software. I am hoping that once I begin working with it, I will understand better.

Chapter 4:

In the previous chapter, we learned how correlations can be shown when using data sets to map the most and least of this information. This chapter focuses on mapping density, its importance, and the various ways we can use and build it. This is crucial because it shows viewers where high concentrations of features are located. GIS allows you to map features or feature values; each will result in different outputs (based on what the user inputs). To make either useful, we need to create a density map. One way to do that is to define an area. This can be done graphically with a dot map or by calculating each value per area. Using calculations requires a couple of things. First, by adding a new field for a density table based on the coverage area of the polygon. Then, assign density values and finally divide the values by the polygon area. This method may need a conversion equation if the density units don’t equal area units. This creates a map by total amount or counts, while noting what each dot signifies (e.g., 10 or 10,000 people). Another way is by density surface, created through a raster layer.  It divides the total amount of features by the search radius field. When it comes to calculating density sizes, a few things are required: cell size (to define patterns, its size is given by the length of the cell), searching for a radius (used to find objects on a map), calculation methods (which have 2 methods that can be used: simple and ring series), and units (measurement). How do we display a density surface? We can display it using gradient colors and contours. (This topic was discussed in Chapter 3.) The results will depend heavily on how the density surface is created.

Chapter 5:

Previously, we learned about map density, importance, and their use cases. Within this chapter, discussing the importance of mapping its inner contents. Helping the reader compare fewer and more areas (reviewed in the previous chapter). To define our evaluation, there are a couple of things the user wants to know. How many areas do you have, and whether they are inside one area (e.g., a shopping mall) or have multiple areas (e.g., zip codes)? Each area must be identifiable by a unique name. Drawing areas and features like surfaces and lines works well when the user is trying to find what’s inside and outside a given area. It’s easy to use, but it relies heavily on visual information. Selecting features in a region is useful if you want a summary or list of what’s inside that region. Its downside is that it works for only one area, not multiple. Overlaying has the benefits of both drawing and selection (only within), but it requires more processing power. The only time a user should choose overlaying is if they have multiple areas (examples: zip codes, disjuncts: pieces of land that are separated, or nested: smaller areas inside larger ones) and want a summary of each area; a single area (examples: a shopping mall, buffer, an administrative or natural boundary, manually drawn area, or a result of a model) and want a list of discrete features; or a single area and want a summary of continuous values. Overlaying with discrete features is used when the user wants to know which features are inside or outside a given space. A use case is finding the number of bears in a specific park. With continuous data, we blend gradients to find patterns. A pretty common example is varying temperatures or rainfall across a certain region.

Chapter 6:

In the prior chapter, we learned about mapping inner contents. This chapter covers mapping nearby areas. People define distance as a measurement (think inches, feet, and miles), while others measure it by cost. The best method for your analysis entirely depends on a couple of things. Maybe you want to use list, count, or summary (discussed in previous chapters). Or you might want to know how many distance ranges you’ll need. There are 2: inclusive rings, used to find the total increase as the distance rises. The other includes distance bands, used for when you want to compare distance to different characteristics. How do you find what’s nearby, though? There’s straight-line distance, used to create boundaries or select features around an area. Cost over a network, which finds what’s within travel distance. Then there is cost over a surface, which not only measures distance but also calculates the area within a given range. Buffers create a boundary line to show what’s far away. To create one, the user defines the source feature (or features) and the buffer distance. The aftermath lets people see details in a given area. Special ones are used for finding multiple features. To find objects in several distance ranges, create multiple proximity zones. To pick features close to many objects, the user selects and tags the feature with a code. For features within several distance ranges, you select each object once for each distance. Feature-to-feature is great for tracking distance from a source object, which has its own use cases. There are several options when making a map for this case. Out of the many maps used in this chapter. The one that caught my attention the most was the spider diagram (not really a map, but close enough). It looks like a firework going off in multiple locations.

Personal Summary

Chapter 4: 

In chapter four, the topic of mapping density is covered.  For context, in GIS, density is equivalent to concentration. By mapping areas of both high and low concentrations, it allows you to recognize patterns. After covering the Why the chapter transitions into the what.  It stares that in order to map density, “you can shade defined areas based on a density value or create a density surface.  ( This confused me because it didnt explain what each of them were.  So I did a little reasearch because I was confused and found that a Density surface is absically a gradient over a wide range while a density value is a bullentin point in a small area that gives specified values). It also talks about using GIS to map both points and lines. ( points make sense to graph but lines not so much, I tried to figure out what it meant but it just confused me more).  The last thing mentioned in the topic of “deciding what to map” was the topic of features vs feature values, Which was easy to understand since they gave examples for this one.  Mapping  features would be like mapping the locations of buisnesses while feature values would be like taking the number of emplyees at each buisness. The chaprer then tells two ways to map density, The first is by mapping a defined area (which is I think using Density value because the images show plots of a small region) in this method you can use a dot map to calculate density for each value. The other method is by density surface (ok I think I was right)  which is when each layer gets its own density which makes a gradient type look agross the entire map.

Chapter 5: 

The title of this chapter is “finding whats inside” (im gonna take a guess just by the title it might mention how to interpret the density and make sense of it when making a hypothesis) . The chapter starts off by exlpaining the importance of mapping out the inside of an area. It states that “people map what’s inside an area to monitor what’s occuring inside it, or to compare several areas based on whats inside each”. ( I think this is a common thing thats done in most ENVS/BIO type labs at this school. I never realized that GIS was so commonly used and I never knew what it was until this year).  An example that the chapter used to express the importance of mapping was by  monitoring drug-related arrests within 1,000 feet if a school. In doing so they would give harsher punishments to said dealers. The chapter then goes into “definng your analysis” ( I assumed correctly) .  It mentions that you can draw an area boundry on top of the features to help summarize them (this made no sense to me at all, but It could be because I havent done any actual GIS work yet).  The chapter also states that in order to analyze your data you have to conside how many and the type of features it contains.  (which makes sense because how can you hypothesize something whithout knowing what exactly your looking at). After further reading I think im starting to understand the boundries, I assumed it was no different then any other boundry and it isnt but its there to seperate a specified area from things not considered “in range”.

Chapter 6: 

Chapter six is titled “finding whats nearby”,  but covers things like defining your analysis, three ways to find whats nearby.  (this doesnt really make sense to me considering the last chapter was talking about setting boundries in order to cust things off, but now it wants you to also map whats around)? The chapter starts by explaing the “Why”, turns out its important because lets say your going to like walmart but theres a target close. By having the target pinged on the map it lets you know that theres also a target you could go too. (or at least thats how i interpreted this section of the reading). The chpater also talks about a method used to find whats nearby, the method they mentioned was measuring using straght line distance (is this like literally a straght line from A to B) by doing this they can gain an idea of measuring “nearness”. Another meathod that they said is measuring distance or nearness by travel cost (this was in context to traveling only though).  In the chapter it also talks about measuring whats nearby using either distance or cost,  it states that you can measure cost in terms of how long it will take to reach points A to B ( I was thinkning like cost as in $, this makes stuff so much more understandable) . The chapter also starts bringing up other topics that I didnt really take into account. For exampke if your measuring travel are you measuring the distance over a flat plane or using the curvature of the earth. ( If im being honest this did not cross my mind but it makes so much more sense to think of this. This makes me wonder if things like google maps uses a flat map or curvature to measure long disances).

Chapter 4 

Chapter 4 explains the reasoning behind mapping density, as well as when it is important to map density, what to map and the two types of ways of mapping density. Density maps allow viewers to observe each individual feature of a map using a uniform areal unit of measurement, such as per square mile, per county, or per census tract. Census tracts are specific geographic regions defined solely for the purpose of conducting the census. When would a density map be used? One example could be: a local nonprofit wants to see which areas of the State of Ohio have a more concentrated homeless population using census tracts as a measurement, and so the nonprofit collects the data on the number of homeless individuals in each census tract, with the darker shade being the highest concentration of homelessness and the lighter shade being the lowest concentration. When choosing what to map, you can choose to map (1) the density features, which include the number of businesses, schools, parks, etc., or (2) to map feature values such as the number of employees at each business, the number of teachers per school, the number of trees per park, etc.. Density maps can be represented using a random distribution of dots on a map to show the concentration of particular features, or using a density surface, which is a raster layer that can show the number of features within a particular radius of cells. For example, if local law enforcement were mapping the density of individual crimes within a given area, a density surface could be used to show the concentration of higher occurrences of crimes in a given area and which areas crime occurs the least. When creating a dot density map, dots should not be so large that areas with higher density are obscured and difficult to interpret.

 

Chapter 5

Chapter 5 explains the reasoning behind “mapping inside” an area and its uses. Mapping inside requires you to draw a boundary around the area that you want to analyze. For example, if a construction company is trying to see which neighborhoods are more prone to flooding, the construction company may draw a boundary around a particular area of interest and see which properties are near floodplains or if floodplains intersect with certain areas to get a better sense of where to build. In the chapter, Mitchell also provides an example of a district attorney being able to draw a boundary to map out which crimes occurred and whether the crimes that occurred were within a certain radius of a school, which could lead to tougher criminal penalties. The chapter also touches on discrete and continuous features when mapping inside. Discrete features are easily identifiable features on a map such as roads, rivers, waterstreams, or even locations such as student addresses. Continuous features are represented using seamless geographical phenomena such as vegetation. For example, a geographer could represent the different types of soil using continuous categories. When mapping inside, a geographer may want to know which type of soil is within a 1000-square feet radius. When counting the number of features within a boundary, Mitchell states that you should include features that are partially within the boundaries inside of count and if you want to emphasize the different categories and features within a particular boundary, that you can use GIS to create an overlay that clips out the features and portions of categories that’s on the outside of a boundary that way you are left with a map of only the boundaries and all of the features and categories inside the boundaries being focused on. You can also create an overlay to where the features of the summary statistics you’re mapping are in color and the outer boundaries features are still present but in black and white to emphasize their nonimportance.

 

Chapter 6

Chapter 6 explains how to map what’s nearby. Similar to the last chapter, you could draw a circle around a particular area of interest to see what is all within the boundaries. Chapter 6 explains that while doing that, you can also map what’s nearby based on the distance specified from one area to another as well as the travel costs. How can this be done? Mitchell explains that this can be done using a simple straight-line distance. For example, if Starbucks executives were looking to see which grocery stores, parks, or recreational centers are nearby and how far, the distance from each of the locations could be represented using a straight line which could represent a mile or 2 miles, etc. Travel costs could also be measured when looking at what’s nearby. To calculate the travel cost, Mitchell points out that you would need to use GIS, assign each street segment with a cost. To do this, you would need to find the per unit cost (for example, labor and fuel costs) and then multiply that by the length of each street segment. You can also measure the travel time for each street segment or block while considering the speed limits for each street segment and multiplying the length of each segment by the speed limit. This is the formula the chapter provides for calculating travel time: “minutes = length / ((mph * 5280) / 60)”.