Category: Course Student Work

Chapter 4:

This chapter discusses what mapping density is and how it can be used for GIS work. I honestly have never thought about this kind of mapping, but it does relate to some important concepts such as population and urban densities. After the chapter defines mapping density and its importance, the chapter talks about how you figure out what to map for mapping density. You can either use point or line methods for this, which is related back to Chapter 3. In general, there are two ways to map density. The first way is to map it by defined areas, which correlates to dividing the total number of features to the area given, which gives the density of each defined area. The second way is through density surface. This method seems more complicated for me to understand, but it does seem like it shows more information compared to the defined areas method. The chapter then dives further into using both methods, and how to undergo both methods using GIS. For the density of the defined areas, one method besides calculating a density value, you can use a dot density map for the defined areas. This allows more accurate representation of where the density is taking place in the given areas. To undergo the second method, the first thing to do is to calculate the density values that you would use. To do this, you need to know factors such as cell size, search radius, the calculation method, and the units that you are willing to use. Overall, these steps seem quite confusing for me, as the process seems more complicated for the density surface. Once the data is presented on the map, then you need to figure out how to present the given data. It is important to know things such as what colors to use to represent the density, along with using the proper contours. 

Chapter 5:

When I first started to read the chapter, I honestly had no idea what “finding what’s inside” actually meant. From what I have interpreted, this chapter focuses on helping pinpoint or to highlight certain features found within a given area of a map. This allows those to see more detail on what is going on in a specific area of interest. When undergoing this process, you need to define the actual area you want to focus on. This area does not have to be a single region, as it can pinpoint other surrounding areas as well. You also want to figure out if the area is either discrete or continuous, which relates to other ideas discussed in the first two chapters. The next question that needs to be addressed is the kind of information that you are trying to collect. It is also important to note that all the features that are being measured within the given areas can also be found outside the given area being measured as well. Overall, there are three ways to make these areas inside. The first method is drawing areas and features, which seems to be a quick and easy method, but it does not provide precise measurements in the areas. The second method is selecting the features inside the area, which gives good insight into one area, but only that one selected area. The final method is overlying the areas and features, which gives the most accurate and precise representation of the data, but is the most complicated process. The chapter then goes on to explain which method one should choose and the process of completing the method of choice. All these methods seem complicated and require multiple steps to complete in my opinion.

Chapter 6:

The start of this chapter seems quite similar to what chapter 5 had to entail. However, what makes this chapter different is that it allows one to define a set distance or feature, which allows one to see what occurs within that area. From what I understand, this allows us to predict how adding something to an area will affect that area, whereas chapter 5 looks at what is already defined in a given area. The first step to this process is to determine the distance of a given area. Then, you need to figure out what kind of information that you want to collect. Usually, this either involves gathering a count, list, or a statistical summary. Another important idea to consider is the amount of ranges that you need, as there can be multiple given ranges within a map. Once those steps are complete, there are three methods to undergo finding what’s nearby. The first method is straight-line distance, which measures distance and is quick and easy to use, but it only gives a rough estimate of the distance. The second method is distance/cost over a network, which can measure either and is more precise, but it requires an accurate network layer. The third method is cost over a surface, which measures cost which allows one to combine multiple layers, but it requires data from multiple sources. The chapter then goes on to explain what method to use and how to use the said method through GIS. Like the previous chapter, the steps to undergo any of these methods seem quite complex. However, I feel like using this book as a guide when it comes to actual GIS work will not be a bad idea, as this book does contain useful introductory information.

Ch. 4

     This chapter begins with map density and what it depicts. In simple terms, map density shows where high concentrations of a certain subject are. Paying attention to map density can help in observing patterns within an area to determine places of interest. Map density can also be shown in two different ways, being by area or density surface. The density by area maps are personally a little easier to understand. More dots or marks in a section of the map means there is a higher density of a certain feature there. Surface density doesn’t show much area separation, making it harder to work with to a degree (but it’s more accurate). With surface density, the GIS will determine the surface density for you, but the user has to determine the cell size. You have to be careful when determining the cell size because if it’s too big, the map may appear vague and unclear. Smaller cell sizes tend to have more detail, which in turn takes up a lot of storage space and may take longer to generate every detail. The textbook guides you through how to calculate an appropriate cell size based on the map size. 

 

Ch. 5

     Chapter 5 starts with explaining why it’s important to map things in the first place. Many different groups of people can utilize these maps for things like research and political demographics. For example, researchers tracking whale migration would want to see where individuals frequent for breedings and feeding, so marking these places and movements on a map proves to be useful to see where the whales prefer to be for certain reasons. It’s very important to label and define what’s inside the map so you don’t lose track of your data. The book gives a few different ways of finding and keeping track of features in the map, which consists of; drawing areas, selection inside an area, and overlaying areas. Drawing area only gives you a visual of where things are, but does not give you detailed information about what you’ve found. Selection allows you to actually specify the features in front of you. The GIS will search the area map for you and determine the feature and mark it. Overlaying will assign a code to a certain feature that’s in the area. The GIS will check the area for the specific feature and give the features IDs. Overlaying seems to be the most detailed and informative way of keeping track of features on your map. 

 

Ch. 6

     This last chapter gives reasons for why it’s significant to “map what’s nearby”. The three big methods the book gives for this is cost over surface, straight-line distance, and distance/cost over network. The straight-line distance is the simplest method of determining nearby features. This only measures the distance and roughly estimates the time it would take to reach a location. We use things like this a lot when we go places that are unfamiliar and need to know how to get there and the best (possibly shortest) route to get there. The cost over surface method allows us to determine the cost of travel to a destination. This requires more data to determine the cost involved, but the calculations prove to be pretty reliable. The last method is cost/distance over the network. This method combines the previous two, needing locations of interest along with the value/cost of an area. The last chunk of this chapter tells you how to calculate the cost (time, money, etc.) over a geographic surface. Once you add all of the layers to the map, the GIS thankfully calculates the total costs for you. Once that’s been done, the map user can filter through the cells of the map to pick out areas of higher or lower cost. 

Chapter 4:

• Why map density

Mapping density shows you where the highest concentrations of features are. These maps are useful for looking at patterns rather than locations of individual features and for mapping areas of different sizes. Density maps let you measure the number of features using a uniform areal unit so you can clearly see the distribution. This is especially useful when mapping areas which vary greatly in size (census tracts or counties).

• Deciding what to map

 Although you can simply map feature locations to see where they are concentrated, creating a density map gives you a measurement of density per area, so you can more accurately compare areas, or know certain areas  meet your criteria. You can create a density map area on features summarized by defined area or by creating a density surface

• Two ways of mapping density 

You can use a dot map to represent the density of individual locations summarized by defined areas. Each dot represents a specified number of features. The dots are distributed randomly within each area; they don’t represent actual features in that area. Dot density maps show density graphically rather than showing density value. You can use dot map to show density when you have many clustered features. A density surface is usually created in the GIS as a raster layer. Each cell in the layer gets a density value, such as number of businesses per square mile, based on the number of features within a radius of the cell. This approach provides the most detailed information but requires more effort

• Mapping density for defined areas

With this method, you calculate density based on the areal extent of each polygon. First, add a new field to the features data table to hold the density value. Then, adding the density values by dividing the value you’re mapping by the areas of the polygon. If the density units are different from the area units, you’ll need to use a conversion factor in the calculation to change the area units to the density units. Density by defined area is usually displayed as a shaded map, using a range of color shades with one or two hues. In this case density is treated as a ration and is mapped like any other ratio map. Some softwares lets you calculate density on the fly by specifying the value you’re mapping density for and the attribute containing the area of each feature. The GIS then calculates the density and shades accordingly. Density value for each polygon applies to the entire polygon. The actual density at any given location within the polygon may vary greatly from this value. This is especially true for large polygons

• Creating a density surface

The GIS calculates a density value for each cell in the layer. Density surfaces are food for showing where pint or line features are concentrated. The GIS defines a neighborhood around each cell center. It then totals the number of features that fall within that neighborhood and decides that number by the area of the neighborhood. That value is assigned to the cell, the GIS moves on to the next cell and does the same thing. This creates a running average of features per area, resulting in a smoothed surface. 

Chapter 5:

• Why map what’s inside

People map what’s inside an area to monitor what’s occurring inside it or to compare several areas baked on what’s inside each. This allows for people to discern whether they should take action. Summarizing what’s inside each of several areas lets people compare areas to see where there’s more and less of something.

• Defining you analysis

To find what’s inside, you can draw an area boundary on top of the features, use an area boundary to select the features inside and list or summarize them, or combine the area boundary and features to create summary data. You need to consider how many areas you have, and what type of features are inside the areas. 

• Three ways of finding what’s inside

Finding what’s in a single area: finding what’s inside a single area lets you monitor activity or summarize information about the area. Multiple areas: finding how much of something is inside each of several areas lets you compare the areas. discrete features are unique, identifiable features. You can list or count them, or summarize a numeric attribute associated with them such as locations, linear features, or discrete areas.

• Drawing areas and features 

Drawing areas and features: Good for finding out whether features are inside or outside an area. Locations, lines, areas, surfaces. Trade Offs: Quick and easy but visual only so you can’t get information about features inside. Selecting the features inside the area: Good for getting a list or summary of features inside an area. Locations, lines, areas. Trade Offs: good for getting info about what’s inside a single area, but does not tell you what’s in each of several areas (only all areas together). Overlaying the areas and features: Good for finding out which features are inside which areas, and summarizing how many or how much by area. Locations, lines, areas, surfaces. Trade Offs: good for finding and displaying what’s within each of several areas, but requires more processing 

• Selecting features inside an area

With this method you specify the features and the area. GIS checks if each feature is inside the area and flags ones that are. You can also use this method to find what’s inside a set of areas you are treating as one. However, the GIS doesn’t distinguish which area each feature is in, only that it’s in one of them. Geographic isolation is also a quick way to find out which features are within a given distance of another feature. 

• Overlaying areas and features

Overlaying areas with discrete features: GIS tags each feature with a code for the area it falls within and assigns the areas attributes to each feature. GIS checks to see which area each feature is in and assigns the areas ID and attributes to the features recorded in the data table. Overlaying areas with continuous categories or classes: GIS summarizes the amount of each category or class features falling inside one or more areas. You can get a map, table or chart of the results. GIS uses either a vector or a raster method to overlay areas with continuous categories or classes. Overlaying areas with continuous values GIS can summarize the values and create a map or table of summary statistics for each area. These include mean, minimum value, maximum value, value range, standard deviation, and sum. You can create a chart from the table to compare areas based on a particular statistic.

Chapter 6:

• Why map what’s nearby

Using GIS, you can ding out what’s occurring within a set distance of a feature.

Traveling range is measured using distance, time, or cost. Knowing what’s within traveling range can help delineate areas that are suitable.

• Defining your analysis

Defining your analysis: to find what’s nearby, you can measure straight-line distance, measure distance or cost over a network, or measure cost over a surface. Defining and measuring near: what’s nearby can be based on a set distance you specify, or on travel to or from a feature. When a surrounding feature is within a feature’s area of influence, measure using straight line distance. When movement or travel between the source and the surrounding features happen, measure over a geometric network. Distance is one way of defining or measuring how close something is, but nearness doesn’t have to be measured using distance. You can use cost to measure distance (i.e. travel costs). If you’re mapping what’s nearby based on travel, you can use distance or cost. Costs give a more precise measure of what’s nearby than distance but depending on the situation, distance can be more sufficient. Planar method: some analyses calculate distance assuming the surface of the earth is flat. Appropriate for small areas; city, country, or state. Geodesic method: some analyses calculate distance taking into account the curvature of the earth. Appropriate for large regions; continents, entire earth. List; an example of a list is the parcel ID and address of each lot within 200 ft of a road repair project. Count: a total or count by category. For example, the total number of calls to 911 within a mile of a fire station over a six-month period, or number of calls by type. Summary statistic examples: total amount, such as the number of acres of land within a stream buffer. Amount by category, such as the number of acres of each land cover type within a stream buffer. Statistical summary such as an average, minimum, maximum, or standard deviation

You can specify a single range or several ranges using inclusive rings or distinct bands. Inclusive rings are useful for finding out how the total amount increases as distance increases. Distinct bands are useful if you want to compare distance to other characteristics. 

• Three ways of finding what’s nearby

Straight line distance, you specify the source feature and the distance and the GIS finds the area or the surrounding features within the distance. This is good for creating a boundary or selecting features at a set distance around a source. You need a layer containing the source feature and a layer containing the surrounding features. Use straight line distance if you’re defining an area of influence or want a quick estimate of travel range. Distance or cost over a network, you specify the source locations and a distance or travel cost along each linear feature. The GIS finds segments of the network are within the distance or cost. You can then use the area covered by these segments to find the surrounding features near each source. This is good for finding what’s within a travel distance or cost of a location, over a fixed network. You need the locations of the source features, a network layer, and a layer containing the surrounding features. Use cost or distance over a network if you’re measuring travel over a fixed infrastructure to or from a source. Cost over a surface: you can specify the location of the source features and a travel cost. The GIS creates a new layer showing the travel cost from each source feature. This approach is good for calculating overland travel costs. You need a layer containing the source features and a raster layer representing the cost surface. Use Cost over a surface if you’re measuring overland travel

• using straight line distance 

Using straight line distance: create a buffer to design a boundary and find what’s inside it. Select features to find features within a given distance. Calculate feature-to-feature distance to find and assign distance to locations near a source. Create a distance surface to calculate continuous distance from a source. Creating a buffer: to create a buffer, you specify the source feature and the buffer distance. For locations, the GIS draws a circle of a radius equal to the distance you specified. For linear features, the GIS draws a line around the features at the specified distance. For areas, the GIS draws a line around the feature at the specified distance. For areas, the GIS draws a line at the specified distance from the boundary- rather than the center of the area. Finding features within several distance ranges: If you want to know which features are within several distance ranges of the sources as inclusive rings, you have to create several separate buffers and select the surrounding features for each. 

• Measuring distance or cost over a network

Measuring distance or cost over a network: GIS identifies all the lines in a network, within a given distance, time, or cost of a source location. Source locations in networks are often termed centers because they usually represent centers that people, goods, or services travel to or from where you can then find the surrounding features along, or within, the area covered by those lines. Specifying the network layer. A geometric network is composed of edges (lines), junctions, and turns. Turns are used to specify the cost to travel through a junction. To get accurate results, make sure: edges are in the right place, use edges that actually exist, edges connect to other segments accurately, and use the correct attributes for each edge. Setting travel parameters: In addition to specifying the cost for individual segments, you can specify the cost for turns from one segment onto another or for stops at an intersection.

• Calculating cost over a geographic surface

Calculating cost over a geographic surface: Calculating cost over a surface lets you find out what’s nearby when traveling overland. With this method, the GIS creates a raster layer in which the value of each cell is the total travel cost from the nearest source cell. Calculating cost over a surface also shows you the rate of change. Specifying the cost: Cost can include time, money, or some other cost. To calculate cost over a surface, you specify the layer containing the source feature and a second layer containing the cost value of each cell. To create a cost layer based on several factors, you combine all the input layers. Getting the information: Once the GIS has created the cost distance layer, you can either identify the area within a specific distance of the source features or summarize how much of something is within the distance. If you’re mapping discrete features with the cost distance surface, you can show them on top of the distance grid. The distance grid is displayed using graduated color

CH4

Chapter four covers mapping density. “Mapping density lets you see the patterns of where things are concentrated. This helps you find areas that require action or meet your criteria or monitor changing conditions.” Mapping density is to highlight patterns more than the individual features that might be better represented in a different type of map. Mapping density is best represented using shades of colors to represent different ranges of data. The data available will determine what the map might look like. There are two ways to map density: by defined area or by density surface. A defined area can be shown using a dot map and the places with a higher density of dots represent the higher density of features on the map. Defined area map density would also be used if we were interested in how much of a feature is sequestered to a certain area. We might see this map type divided by state, county, country, or any other predetermined separation. The density surface is created in the GIS and shows the density without the predetermined separations. This map does require more work but is more accurate. GIS will calculate the density surface for you. You can determine what the cell size will be. If a cell is too large the map might be too vague, but a cell too small makes the map convoluted and hard to digest. A cell too small also takes a lot of time to process and can take up a lot of storage on the computer/external hard drive. The book gives a detailed description of how to calculate the proper cell size based on how much space is being shown on a map. A map that covers 1 sq. km would have a cell size of 100m. The search radius depends on what the goal of the map is. A search radius too small might not have enough significant features, but a search radius too large can lead to the map becoming too generalized. These types of maps allow the researchers to get a good picture, but it might not be the whole picture.

CH5

People map certain features on a map to determine what is inside the area. Politicians, police officers, and even business owners use these maps to decide whether or not to take action. If a business owner sees that in one area in a city, there are other businesses nearby that are doing well, they are more likely to try and open a branch in that general area. It’s necessary to determine how you will split your data into a single or multiple areas. A single area might be useful to determine if a hotspot is occurring. Multiple areas allow you to compare each and determine if there is a common theme spread amongst them. These areas might be zip codes or state parks. Defining each area and comparing it will give researchers the ability to identify interesting patterns within the maps. You can determine the type of map you need by identifying whether the individual feature is continuous or discrete. The book identifies three ways of finding what is inside the map. Drawing areas and features allows the researcher to highlight what features are inside and outside the boundaries. This is the quickest way to map, but it is strictly visual. Selecting the features inside an area can show the reader information about a single area but does not go deep enough to show several areas. Overlaying the areas and features is the most time-consuming method, however it is also the most detailed. This way finds features inside the areas. This does take the longest, but if a detailed map is what’s most important this is the map in question. Drawing areas can be difficult to decide on a map. A map either has discrete or continuous areas. Discrete areas are often quantitive data like types of injury or crimes in an area. Continuous areas might be more fluid like elevation, population, or even businesses in a specific area.

CH6

Chapter 6 tries to answer the question, “why map what’s nearby?” Traveling range can be determined not only by distance, but also by time, cost, or other factors. The three main ways of finding what’s nearby are straight-line distance, distance or cost over a network, and cost over a surface. Straight line distance is the most simple way for the GIS analysis to work. This type can only measure distance and provides only a rough estimate of the time that it would take a person to get to that destination. This might be used to determine the radius of a school or police station. A cost over a surface allows the researcher to combine different layers of features to determine the travel cost. This type specifically measures cost. It does require a lot more data than the previous type, but the calculation over a range is relatively reliable. The third type is distance or cost over a network. This type is a hybrid of the two previously mentioned methods. This method needs both locations of source features and information about the length or cost value of a particular area. This is the most precise method because it combines both travel distance and cost, but it requires a very accurate network layer that might be difficult to obtain. Calculating costs in GIS can be confusing. The last section of the book goes over how to calculate cost over a geographic surface. Cost can mean time, money, or other factors. Cost layers in GIS can be based on a single factor or using several. Once you have inputted all of the layers, GIS will combine them to determine the total cost. Each cell is given a cost and based on the type of map assigned a color or shade to represent the cost. Once GIS has determined the cost of each cell the researcher can manipulate the map to exclude costs, either too high or too low, and even exclude entire sections due to geographic concerns. The maps that GIS can make are so highly specialized that with the right data GIS can make whatever map the researcher wants.

Chapter 4

• Using density maps can be beneficial if you need to focus on identifying patterns or trends in comparison to other mapping methods that use the specific location of points of interest to gather information. Density also functions as a proportion, which can be helpful in comparing counties of different sizes or larger populations.
• A dot map is one way of mapping density. Each dot represents a set number of subject (ex. businesses) and the closer the dots are together the higher the density of the subjects in that area. This is an example of mapping density based on area, which is relatively easy but does not provide precise area about the centers of density. This method is useful if you already have data summarized by area.
• Another method of mapping density, creating a density surface, can be done in the form of a contour map. This method takes more data processing than the ‘density based on area’ method, but can also present a more precise view of the centers of the densities.
• One thing that stood out to me was that in dot density maps the dots don’t represent the actual location of the object. I assumed that the dots showed the location of each object and you could compare the density based on the object’s proximity to one another. According to the book, though, GIS places the dots randomly within the given area
• There are several factors that can affect legibility for both methods of mapping density. When using dot maps you can manipulate the size of the dots and how many dots are present (how many objects each dot represents). With the density surface method you can manipulate the cell size, search radius, calculation method, and units, which all can have a substantial impact on map appearance and effectiveness.
• When using graduated colors in a density surface map there are several ways to classify the different ‘levels’ of gradation: Natural breaks, quantile, equal interval, and standard deviation. The number of classifications can also impact the appearance of the map.
• Using contour lines is another way of showing density, as it connects points with the same value to create a line that indicates different levels of density.

Chapter 5

• The beginning of this chapter sort of answers a question I had earlier on in the reading (about what ‘inside’ could mean in the context of GIS). Considering ‘inside’ as in the confines of a boundary such as district or county makes much more sense.
• When looking at what’s mapped inside you should consider whether a feature is discrete or continuous; Discrete features can be given numerical values and are unique and identifiable. Discrete features can include locations or linear features. Continuous features are less definite in their boundaries and seamless in their transitions. Soil types, precipitation, and elevation are all examples of continuous features
• You can collect many different types of information from inside a given area depending on what you are looking for (ex. count, summary…). You can also determine whether to include objects/’parcels’ that are fully inside or partially inside the given area.
• There are three primary ways of finding what’s inside an area, each having their own pros, cons, and applications. These three methods are drawing the areas and features, selecting the features inside the area, or overlaying the areas and features. They can also all have a variation in the best way to display the results obtained that correlates to their most relevant function.

Chapter 6

• There are many apparent and practical uses for applying GIS to find what’s in a nearby area. There seems to be a theme of using GIS to search for objects that are within a given radius of something, in units such as travel time/cost or meters.
• It is often more beneficial to map nearby objects based on travel time than by distance, although it typically takes more preparation and data to map. The example provided of 3-minute travel time surrounding a fire department is a good way of showing this benefit; Travel time is more relevant to us in the instance of an emergency than distance, because travel time is the factor that we have to deal with and is what is actively being used as the unit of measurement when the service is dispatched.
• If mapping a large section you may want to consider the curvature of the Earth. In general, you should also keep in mind what you hope to get out of mapping, such as a list, count, or summary of information.
• There are three methods of determining what’s nearby: Using straight-line distance (is relatively simple but rough in approximation), distance or cost over a network (gives precise information and is used for measuring travel over a fixed infrastructure), and cost over a surface (used for determining how much area is within the travel range, which is measure by cost).
• Similarly to the different methods of mapping demonstrated in previous chapter, these three methods all can be presented in different manners. They serve seperate and often overlapping purposes. In designing a map or project it’s best that you select a method of determining what’s nearby that fits your specific project. This can include matching what information you hope to receive, the measurements you use to get this information, and how you want to present your findings.
• I think that all these different methods could be very overwhelming to learn at first, but once I have a specific idea of what kind of project I’d be applying these methods towards then it would be easier to select one.

Chapter 4 talked about map density and its variables and when it is important to use it. I found it helpful the way it broke down what to map using density, the two ways of mapping density, mapping density concerning defined areas, and creating a density surface. The book talks about how density maps are useful when looking at patterns rather than locations of individual features and gives you a measurement of density per area, so comparing areas is done more accurately. Deciding what to map can be tricky though because density maps may not be useful in certain situations. I liked how the reading brought up that you should think about the features you are mapping and the information you need from the map before making the map itself. A concept I thought was interesting was the difference between map features and feature values. A map feature could be an example of locations of businesses where feature values could be the number of employees at each business. This distinction was helpful when thinking about the purpose of the map you are making. Mapping density by area is useful when you have data or lines or points that can be summarized by area resulting in a shaded fill map or dot density map. This is an easy process but is not as precise especially for large areas. Creating a density surface is useful when you have individual locations, sample points, or lines resulting in a shaded density surface or a contour map. This requires more data processing but can give a more accurate view of the centers of density. It was interesting to see how GIS plays a role in creating a density surface to make a running average of features per area resulting in a smoothed surface.

Chapter 5 discussed mapping what is inside an area to see what is happening there compared to other areas in order to see where there is more or less of something. The book goes into explaining the three ways of finding data for the inside of an area; drawing areas and features, selecting the features inside an area, and overlaying areas and features. Drawing areas and features create maps showing boundaries that can be used to see which features are inside and outside a specific area. This approach can show and use locations, lines, areas, and surfaces. Another way to find data is by selecting the features inside that area to get a list or summary of features inside an area or within a given distance of a feature. This approach shows locations, lines, and areas to get information about what is inside a single area rather than information about what is in each of several areas. The last way of finding data for the inside of an area is overlaying the areas and features to find out which features are in which areas, to give a summary of how many by area. Areas and features are combined through GIS to create a new layer showing attributes of both where they can be compared to calculate summary statistics for each area. This approach uses locations, lines, areas, and surfaces making this way good for displaying what is in each of the areas, but it does require more processing. 

Chapter 6 discusses mapping data within a set distance to identify the area and features inside that area that are affected by an event or activity. This approach is effective in finding what is within a set distance to monitor activity in the area. Throughout the reading of this book, I have noticed how helpful the examples are that it gives. One example was a state forester monitoring logging that could use this method to make sure logging does not occur within a 100-meter distance from streams. This chapter also goes into detail about how to define your analysis, the three approaches to this method, using a straight-line distance, measuring distance or cost over a network, and calculating cost over a geographic area. To define your analysis there are three ways to get an accurate measure of what is nearby. This includes using straight-line distance to create a boundary or selecting features within the distance around a source. This is a pretty quick and easy approach but it can only give a rough estimate of travel distance. Measuring travel distance or cost of a location over a fixed infrastructure is achieved through GIS finding segments of the network that are within the distance or cost. This approach gives a more precise measurement over a network but requires an accurate network layer. Looking at costs over a surface is another method that can measure overland travel and calculate how much is within the travel distance. This allows for combining several layers to measure over-land travel costs but requires more data preparation to build the cost surface. 

Chapter 4-

Chapter 4 discusses mapping the density of features, why to do it, and how to do it, so you can more accurately compare areas and know if certain areas fit your criteria. The first subsection, “why map density,” describes how it shows you where the highest concentration of features is and how it’s good for looking at patterns in features. If you have multiple features on a map, or when you’re mapping areas, mapping density can help to differentiate easier. “Deciding what to map” asks you to consider what kind of data you have, if you want to map features or feature values- feature values being the number of employees at a business vs. the location of businesses, for example, since the two can create different patterns. “Two ways of mapping density” describes mapping density by defined area- by mapping it graphically, using a dot map (which doesn’t actually show exact locations just the density of features in an area), or by calculating a density value for each area, and by creating a density surface- each cell in the raster layer gets a density value, typically if you have individual locations, sample points, or lines. The next two subsections go into extreme depth on how each method works, how to do it, and what the GIS does. 

Chapter 5- 

Chapter 5, “Finding What’s Inside,” describes why you should map what’s inside, defining your analysis, ways of finding what’s inside, drawing areas and features, selecting features inside an area, and overlaying areas and features. The first subsection, “why map what’s inside,” states that you can look at what’s happening inside an area, or compare what’s happening in several areas based on what’s inside. You can know whether to take action by observing one. “Defining your analysis” describes how depending on your data, you can either draw an area boundary on top of the features, use an area boundary to select the features inside and list or summarize them, or combine the area boundary and features to create summarized data. The subsection goes in depth on what you need in order to make a decision and why. “Three ways of finding what’s inside” describes drawing areas and features as a way to find what’s inside- creating a boundary of an area and features to see what’s inside and out, selecting the features inside the area, and overlaying the areas and features- the GIS combines the area and the features into a new layer or compares two layers, and the subsection also compares each method and what features best suited for them. “Drawing areas and features” describes the actual process of finding what’s inside, by making a map to see what features are inside the area either using locations and lines, discrete areas, or continuous features. “Selecting features inside an area” is a method where you specify the features and the area, then highlighting selected features on a map and putting them into a data table, or can summarize an attribute associated with the features. “Overlaying areas and features” is a method for discrete features or continuous ones.

Chapter 6-

“Finding What’s Nearby,” talks about how this lets you see what’s in a set distance or travel range of a feature to monitor events in an area or find an area served by a facility or the features affected by an activity. The first subsection, “why map what’s nearby,” talks about how finding what’s in a set distance identifies an area and the features inside an idea affected by an event or activity. “Defining your analysis” describes how in order to find what’s nearby, you can measure straight line distance, measure distance or cost over a network, or measure cost over a surface, and how the information you need from the analysis will help to choose the best method. “Three ways of finding what’s nearby” goes more in depth about the previously mentioned methods, what each is good for and what you need, and how to choose what method works best for you. “Using straight-line distance” is the simplest way of finding what’s nearby, by either creating a buffer to defined a boundary and find what’s inside it, select features to find others within a given distance, calculate feature to feature distance to find and assign distance to locations near a source, or create a surface to calculate continuous distance, and each method is discussed in depth. “Measuring distance or cost over a network” is a method where the GIS identifies all lines within a given distance, time, or cost of a source location, by specifying the network layer, what the GIS does, assigning street segments to center, setting travel parameters, specifying more than one center, and selecting the surrounding features to make the map. “Calculating the cost over a geographic surface” lets you find what’s nearby when traveling overland by creating a raster layer with the value of each cell being the total travel cost from the nearest source cell, and how and why to do this.

Charlie Hornacek

Chapter 4:

In Chapter 4, the focus is on mapping density and the various methods to achieve this. Density mapping is crucial for identifying patterns and groups within a given space, rather than analyzing individual data points. One approach discussed is mapping by defined areas, using dots to represent features. However, to avoid clutter, dots can be redistributed within specific areas, offering insights into which areas are more densely packed. Alternatively, density surface maps, utilizing raster cells, measure the number of features within a certain radius, assigning values and colors based on the concentration. The chapter also emphasizes the importance of choosing appropriate class ranges for natural breaks and quantile methods, which may seem exponential or arbitrary.

Chapter 5:

Chapter 5 delves into mapping areas, aiming to determine the best locations for specific purposes or understand the proximity of one element to another. Two main types of values, discrete and continuous, are assigned to features. Discrete features are clearly separated, while continuous features form a continuous path. The chapter explores methods to find what is within an area, such as drawing areas/features, selecting certain features, or overlaying areas/features. Each method has its applications, whether for firefighting strategies or legal considerations like property lines. The chapter highlights the versatility of mapping based on what’s happening inside an area, providing valuable insights for various applications.

Chapter 6:

Chapter 6 focuses on mapping things within a certain distance or time of a location, taking into account the complexity of factors like transportation infrastructure and terrain. Straight-line distance, buffer techniques, and the consideration of Earth’s curvature are discussed. The chapter introduces the concept of buffers around lines or shapes, offering flexibility for different classes of points/lines/shapes. The ability to determine distances across networks and analyze travel costs, especially for roads, adds a practical dimension to spatial analysis. The chapter also touches upon the simplicity of the algorithm for determining distance across networks, emphasizing the importance of accounting for real-world factors in mapping.

Chapter 4.

 

This chapter covers mapping density and the process of deciding how to do so. The primary use of density mapping is to determine patterns and groups, rather than analyze individual data points. Density is mapped by finding the amount of something within a consistent unit of space. It could be features or feature values, which will often present different results and patterns. 

There’s a few different methods to mapping density.

One of the ways of mapping density is mapping by defined areas. Typically, you could map features on a surface using dots, where each dot represents 1 feature. In certain scenarios, however, this can become too cluttered to discern some important patterns. Instead, the dots could be defined by certain areas (legal borders, zip codes, etc.) and then randomly redistributed throughout their area, which gives a good sense of which areas are more densely packed with this feature. This can also be done by adding each feature in an area and assigning ranges of number of features to a colour, then give each area their respective colour based on this range. 

Another way is density surface maps. This is a method using raster cells instead of other defined areas like what would be with vector boundaries. Density surface takes each raster and measures how many features fall within a certain radius of that raster, then assign a value (and typically colour) to it based on the amount. That radius is called the “Search Radius.” Often, the search radius is weighted so that closer points to the cell have a higher value than further ones. I’m assuming that it uses a Gaussian function for the weighting, or at least an approximation of a Gaussian function. This is inherently similar to the Kuwahara filter, which is a noise-reduction filter. It’s used to discern patterns out of impossible-to-read, noisy scannings of people’s heart muscles by dividing an image into cells and giving each cell a weighted search radius.

This chapter further expands on the ideas surrounding cell size in order to properly show patterns without generalizing to becoming choppy, while still not taking much storage space. 

I’m not entirely sure why the numbers for the class ranges are chosen for the natural breaks and quantile methods. It seems exponential, but also partly just random and I don’t fully understand the choices.

 

Chapter 5

 

 Chapter 5 is all about mapping areas. This can be used to determine where is the best place to place something, or to see what is possibly close enough to something to affect it. These can be in a radius around a point, in a radius surrounding a line, in a manually created bordered area (such as property line/zip code).

There are two main types of values to assign to features: Discrete and Continuous. Discrete are things that are unique and separated clearly by a point or border. Continuous are features that are not separated like this, but instead have a continuous path from one data point to another in all places. This is things like density or elevation maps. Knowing what features are all within a certain area, such as other areas or single points, creates a better understanding of the area and allows determining how the features may affect the area. 

When dealing with features that have a distance or area that are within an area, mappers can choose to only select features that are fully inside the area, partially inside, or just only consider the parts of the features inside the area. There are different applications for each, I would assume, such as streams in the area of dangerous chemical spill. It would be useful to map teh entirety of streams that fall at all within the area of the spill, because likely they would be affected downstream since the water may carry the contaminants. The overlay method would be very useful for legal things like property lines, since its not important to know the rest of the are, just where it overlaps. 

You are also able to find the features of features/areas within marked areas, and find values, frequencies, densities, averages, or summaries.

I feel as if the shaded areas outside the highlighted areas would be especially difficult to see, and I have a hard time discerning which is which colour.

I feel as though there would not be many uses for taking the average elevation of watersheds in a scenario like the one shown where they overlay a continuous elevation graph over watershed boundaries. There seems to be greatly varied elevation within the watershed boundaries, with lower land on one side and very high land on the other. I suppose that over a very large map that contains many many watersheds could be more useful as to generalize elevation. 

 

Chapter 6

 

This chapter covers how to find and analyze things within a certain distance or time of a location. Determining time or distance is considerably more complex when it’s regarding how something like a person or animal would actually get to that location. People can only drive on roads, and there aren’t always direct roads to a location. Additionally, stops and traffic lights could influence how long it takes to get to somewhere. Steep elevation or dense forest could influence how deer may travel across land, and direct paths may not be short or possible.

The straight line distance system seems like it works fine for many situations, especially ones where short time/distance is not immediately important.

The “buffer” for straight line distances can be placed around lines or shapes in addition to just points. Additionally, you can have multiple lengths of buffer lines for different classes of points/lines/shapes, and they can be combined where they overlap.

Earth’s curvature causes maps to become distorted at large scales, and so this is something that has to be taken into account when working with large maps and distances.

It’s also possible to determine the exact distance between two features (usually a feature and a source) within a selected range. With multiple sources, one can find whichever source is the closest to the point you want to determine, and then finding the distance to that and classifying it based on that one. The distance from the further ones will not affect it’s distance to one.

The algorithm used to determine distance across networks was considerably more simple than I thought it would be. 

Using cost layer, you can determine the cost to travel or expand to that location, for things like roads.

Chapter 4-

This chapter starts by explaining the difference between mapping features and feature values as densities. Mapping a feature would show the density of a business in a particular area, whereas mapping a feature value might show the density of workers at the business. It’s interesting how different these two maps look, even though the data seems fairly similar. The density maps are a little confusing right now, but I think it will make more sense when we’re creating them in the lab ourselves. The book does mention not to make dots too big when graphing density, otherwise, it is hard to pick out the patterns since they all mesh together. Also, this chapter mentions not to use too many classes when graphing density surfaces, otherwise the colors are hard to distinguish. I think it’s an interesting idea to make a map using light colors for high density because it’s usually the other way around. Although, the book mentions that the reader’s eye is drawn to the light color, and I think this is true. This chapter taught me that density maps are useful because they display patterns rather than features, which is very beneficial for real-world applications. The book mentions that crime analysts, transportation planners, and urban planners can all use density maps for their jobs. This chapter also emphasizes the idea that it’s important to decide what kind of data you have before choosing what kind of map to make and how to map it. The chapter presents many different ways to map densities, such as dots, graduated colors, or contours. There are many useful images that show common mistakes people make when mapping densities and how to fix them in order to create an effective map. 

Chapter 5-

Chapter 5 is about mapping what’s happening inside a certain area. When a map is made based on data occurring inside the area, trends can be tracked and policies or courses of action can be made based on the data. The book mentions that firefighters could use this method when there are toxic plumes so they know what areas are being affected. Also, mapping data inside an area can be done with one specific area or multiple areas. When mapping multiple areas, this would be used to compare certain trends or patterns. One thing I find interesting about all these methods is how much you can manipulate the map to display a very specific type of data in a way that is easy to understand. This chapter goes on to explain the three different ways of finding out what is inside an area: drawing areas/features, selecting certain features, or overlaying the area/features. These all have instances when they are most beneficial, so I think that you would just have to decide what kind of map you want to portray. This chapter has another chart comparing all of these methods that has pros and cons, which I think I’ll look back on later! One thing I like about this chapter, and the others before, is how it builds on material from the other chapters. It incorporates terms and ideas that were introduced earlier, like discrete or continuous features, and almost forces you to learn what they mean. I think building the information up like this is a very effective way of teaching this subject. I think maps based on what’s happening in an area have many diverse applications and it will be interesting to see how these are made in practice. 

Chapter 6-

Chapter 6 seems similar to 5, but it explains how to map what is nearby rather than inside an area. These maps are useful because you can specify a distance and analyze data within that set amount, or you can even use them to calculate travel costs and mileage. I think it’s interesting how this method can be measured using either a set distance or a set cost. If you have a budget, you can determine how far you can travel based on that budget. You can also take into account the time it takes to travel somewhere based on heavy or light traffic. Like the last chapter, this chapter explains the three different methods of mapping what’s nearby- straight-line distance, distance/cost over a network, and cost over a surface. These methods all vary in precision and uses, and it also depends on what type of data you have available to input. I think that mapping distance may seem like a fairly simple task or something that is just intuitive when looking at a map, but it’s interesting to see all the work that goes into creating maps like this. Also, the work that gets put in to make these maps means that people can look at them and read them easily. I like how each of the chapters goes into detail about what the GIS does when you’re making a map. It’s interesting to know what is happening “behind the scenes”. It’s nice how this book talks about things that you can do to make maps and show data, while also explaining the way the computer helps you out. I think it explains these concepts in a way that isn’t overwhelming and gives you just enough information to understand the process.