Geography 291: Geospatial Analysis with Desktop GIS

Mitchell- Chapter 4

Why Mapping Density?

• Shows concentrations of features
• Good for overall patterns
• Density map uses uniform areal unit to show feature distributions

Deciding What to Map

• Map density in one of two ways:
  • Shading defined areas based on density values
  • Create a density surface
• Mapping the density of points or lines is usually done using density surface
• Can map data already summarized by defined areas (ex: census tracts, forest districts, administrative boundaries)
• Can map density of features or of feature values

Two Ways of Mapping Density

• Density map gives you density per area measurement
• By Defined Area:
  • Dot density maps show density graphically, rather than showing density value
  • To calculate density value for each area, divide total number of features by area of polygon
  • Each area then shaded based on calculated value
  • Can see areas of higher density but not centers of density
  • Could also create shaded fill map
• By Density Surface:
  • Density surface usually created in GIS as a raster layer
  • Each cell gets density value based on number of features within radius of cell
  • This provides more detailed info, but more effort
  • Point data used to create density surface may be
    • Locations of features
    • Sample points you’ve collected data for
  • Can create density surfaces for individual locations or linear features
  • Can also create contour map

Mapping Density For Defined Areas

• Dot density map or calculated density for an area
• Calculating Density Value for Defined Areas:
  • To do so, add new value for feature, then assign density value by dividing value being mapped by area of designated polygon
  • Make sure units align
  • Use range of shades of a couple hues
  • Density treated as ratio and graphed like one
  • ArcGIS lets you calculate density easily, but values are temporary and not stored in database
• Creating a Dot Density Map:
  • Map each area based on total count/amount and specify how much each dot represents
  • GIS divides value of polygon by amount represented by a dot to find how many dots to draw in an area
  • Give quick sense of density in a space
  • Dots represent totals rather than calculated density values
  • Specify dot value and size (don’t be scared to play around)
  • Display dots based on smaller areas, draw boundaries of larger areas
  • That way boundaries won’t obscure dots

Creating a Density Surface

• • Created in GIS raster layers (good for showing where point or line features are concentrated)
  • To create density surface GIS:
    • Defines a neighborhood (based on specified search radius) around each cell center
    • Totals number of features in this neighborhood
    • Divide number by area of neighborhood
  • If using data value instead of features GIS:
    • Totals that value for all features in neighborhood
    • Divides that by area of neighborhood
  • Parameters that affect how GIS calculates density
    • Cell size
      • Determines how coarse or fine patterns will appear (smaller cell size = smoother surface, but longer processing time and more storage space)
      • Larger cell = faster processing but coarser surface
      • In general, set cell size to have 10-100 cells per density unit
      • To calculate cell size
        • Convert density units to cell units
        • Divide by number of cells per density unit
        • Take square root of cell area to get length of one side
    • Search Radius
      • Larger the search radius the more generalized the patterns in the density surface will be (GIS also considers more features when calculating value of each cell)
      • Smaller radius shows more local variation (be careful not to make too small or you’ll lose broader patterns)
    • Calculation Method
      • Two Methods For Calculating Cell Values:
        • Count features in search radius of each cell
          • Result is a series of overlapping rings
        • Weighted method
          • Uses mathematical function to give more weight to values closer to center of cell
          • Weighting drops off rapidly for features outside search radius
          • Every cell in layer is counted and assigned a value
          • Smoother, more generalized surface and easy-to-interpret patterns
    • Units
      • Choose a value for units that reflects features being mapped
      • If areal units are different than cell units, legend values are extrapolated
  • If you have data summarized by defined areas create a density surface:
    • Use center points (centroids) of defined areas to create density surface based on value assigned to each area
    • Do this to highlight patterns in the map with less emphasis on individual polygons
  • Display density surface using either graduated colors or contours
    • Graduated colors
      • Classify unique density values for each cell to see patterns
      • Create custom class ranges or specify standard classification scheme and let GIS create classes for you
      • Classification schemes:
        • Natural breaks– based on groupings of data values
        • Quantile– each class same number of cells inside
        • Equal interval– difference between high and low value the same for each class

• Standard Deviation– classes defined by number of standard deviations from mean of all values in layer

• • • • GIS lets you specify number of classes to assign the density values
      • Using a few classes highlights the areas with highest density but may not show subtleties in the patterns
      • Density surfaces usually displayed using different shades of a single color
      • If using standard deviation use shades of one color for values above mean, and shades of a different color for values below the mean
      • Areas of higher value use darker colors
    • Using Contours

• Contour lines connect points of equal density value on the surface

• • • • Most GIS software makes contour lines automatically (just specify contour interval)
      • Contours are good for showing rate of change across the surface (closer contours = more rapid change)
      • Play around with interval until the lines aren’t too far apart nor too close
  • Density surface can show how values vary across a region
  • patterns in density surface are affected by distribution of sample points
  • May not actually be any features where highest density is

• Interpolation process- generalizes and smooths the data so that extremely high/low values may disappear

• • Makes patterns easier to see

Mitchell- Chapter 5

Why Map What’s Inside?

• Monitor what’s happening inside an area, or compare it to other areas
• Let’s people compare areas to see where there’s more or less of something

Defining Your Analysis

• • Consider how many areas you have, what types of features you have inside the areas
  • Single area:
    • Service area around central facility
    • Buffer that defines distance around some feature
    • Administrative or natural boundary
    • Area drawn manually
    • Result of a model
  • Multiple areas:

• Contiguous- touching or adjacent

• Disjunct- separated

• Nested– areas within each other

• Discrete features- distinct and identifiable

• Continuous features- represent seamless geographical phenomena

• • Spatially continuous categories or classes
  • Continuous values
• Do you need a list, count, or summary?
  • GIS can:
    • Find out if feature is inside an area
    • Find number of features in an area
    • Get list of features in area
    • Get summary of what’s inside an area
• Linear features and discrete areas might lie partially inside and outside an area (can choose whether or not to include these)
• If you need list/count of features include those partially in
• If need to know amount of something in area only include those inside

Three Ways of Finding What’s Inside

• Drawing Areas and Features
  • Create map showing boundary of area and the features
  • Good visual approach to finding what is in an area
  • Need dataset containing boundary area and dataset containing the features
• Selecting the features inside an area
  • Specify area and layer containing features, GIS selects subset of features in area
  • Gets list/summary of features inside single area
  • Need dataset of areas and dataset of features
• Overlaying the areas and features
  • GIS combines area and features to create new layer with attributes of both or compares two layers to calculate summary statistics
  • Good for finding features in multiple areas or how much of something is in an area
  • Need dataset of areas and dataset of features
• Overlay the areas and features if:
  • Have multiple areas and want summary of what’s inside
  • Have single area and you need a list or summary of discrete features
  • Have a single area and need summary of continuous values

Drawing Areas and Features

• Key is to create map that makes it easy to see which features are inside the area
• Individual locations/linear features can be drawn using single symbol (or symbolize by category/quantity)
• Discrete areas:
  • Shade the outer area with light color and draw boundaries of features on top (emphasizes features)
  • Fill outer area with translucent color or a pattern on top of discrete area boundaries (emphasizes outer area)
  • Draw outer area boundary with thick line and discrete area boundaries with thin line in lighter shade/different color (use if shading discrete areas by category or class range)
• Use contrasting shades/colors to distinguish areas
• If graphing continuous data
  • Draw areas symbolized by category or quantity
  • Then draw boundary of areas on top

Selecting Features Inside an Area

• • With this, specify features and the area
  • Can also find what’s inside set of areas you’re treating as one

• Geographic selection- quick way to find out which features are within a given distance of another feature

• If you have data summarized by area, can only summarize it using boundaries that fully enclose the area
• GIS can create report of selected features or create statistical summary
• Common summaries:
  • Count
    • Total number of features inside area
  • Frequency
    • Number of features with a given value inside the area displayed as a table
  • Summary of numeric attribute
    • Sum- overall total or total by category
    • Average- total of numeric attribute divided by number of features
    • Median- value in middle range of values for an attribute
    • Standard deviation- average amount values are from mean
• Draw features inside area one color, outside area different lighter color
• Draw features inside based on attribute value and draw features outside single color
• Draw all features based on attribute value, but outside ones a lighter color

Overlaying Areas and Features

• • Helps find which discrete features are inside which areas and summarize them
  • GIS tags each feature with a code for area it falls within and assigns the area’s attributes to each feature
  • Can get list of features or summary of attribute value by area
  • When line or area feature falls within two or more areas, GIS splits the feature where it crosses the boundary and builds new areas in a new dataset
  • If overlaying single area, can do same kind of analysis you would do with geographic selection
  • If overlaying several areas on set of features, can summarize features by area
  • Can also summarize by category or value
  • If overlaying area on data summarized by area, make sure summarized area falls completely inside
  • If have single area, mapping individual locations similar to geographic selection
  • If mapping line or area features can just draw portion within the area
  • Overlaying areas with continuous categories or classes
    • GIS summarizes how many of each category or class features fall inside one or more areas (can get map, table, or chart of results)
    • Vector method
      • GIS splits category or class boundaries where they cross areas and creates a new dataset with the areas that result

• Slivers- very small areas that are a result of overlaying areas on areas

• • • • Any areas with areal extent less than smallest area in either input dataset should be considered a sliver
      • Consider accuracy of your data
      • Good idea to only remove very small areas first then manually check any remaining small areas
  • Raster method
    • GIS compares each cell on area layer to the corresponding cell on layer containing the categories, counts number of cells of each category within each area, calculates areal extent, displays results in a table
• Vector method provides more precise measure of areal extent but requires more processing
• To display and analyze results you’ll need a table that lists areal extent of each category (GIS creates this with raster overlay)
• If looking at how much of each category is inside a single area, you can use table to create a bar chart showing amount of each category in an area
• For multiple areas with a single category a simple bar chart will show how the areas compare
• If multiple areas with multiple categories create a histogram showing multiple side-by-side bars (can also place pie/bar chart in each area)
• If you have a layer of continuous values, you can have GIS summarize the values and create a map or table of summary statistics
  • Can create chart from the table to compare areas based on a particular statistic

Mitchell- Chapter 6

Why Map What’s Nearby

• • Can find out what’s happening within a set distance of a feature or find out what’s in traveling range

• Traveling range– range measured using distance, time, or cost

Defining Your Analysis

• • Can measure straight-line distance, measure distance/cost over a network, or measure cost over a surface
  • Surrounding features may simply be within a source feature’s area of influence (no movement between source and surrounding features)

• Area of influence- usually measured using straight-line distance

• • May also be movement or travel between source and surrounding features
  • Can measure what’s nearby using distance or cost

• Travel costs- time, money, and effort it takes to go somewhere

• • Decide whether you are measuring along flat surface (planar method) or along curve of Earth (geodesic method)
  • Planar method is appropriate when area of interest is small, geodesic method when investigating large area
  • Output layers made using geodesic method will be displayed correctly over curved surface of the globe
  • \once you identify features near source you can get a list of the features, a count, or a summary statistic based on feature attribute
  • Summary statistic can be
    • Total amount
    • Amount by category
    • Statistical summary (average, minimum, maximum, standard deviation)

• Inclusive rings- useful for finding out how the total amount increases as the distance increases

• Distinct bands- useful if you want to compare distance to other characteristics

Three Ways of Finding What’s Nearby

• Straight-line distance
  • Specify source feature and the distance, GIS finds area or the surrounding features within the distance
  • Good for creating a boundary
  • Use if defining area of influence or want quick travel range estimate
• Distance or Cost Over a Network
  • Specify source locations and a distance or travel cost along each linear feature, GIS finds which segments of the network are within the distance or cost
  • Good for finding what’s within travel distance or cost of a location
  • Use if measuring travel over a fixed infrastructure to or from a source
• Cost Over a Surface
  • Specify the location of source features and a travel cost, GIS creates new layer showing the travel cost from each source feature
  • Good for calculating overland travel cost
  • Use if measuring overland travel

Using Straight-Line Distance

• Create buffer to define a boundary and find what’s inside
• 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
• Distance calculated from distance formula in math/physics
• Creating a buffer
  • Specify the source feature and buffer distance
  • With multiple features, Gis can create a buffer of equal or variable distance for each
  • Can specify several source features and GIS will create buffer around all of them at once
  • To know how many features are within several distance ranges of a source as inclusive rings you need to create multiple buffers at each interval
  • Show features like streets, administrative boundaries, water bodies, or other landmarks
  • Map should also clearly label buffer distance
  • Can select features within range of buffer, or can do multiple times for multiple distance ranges
  • To create map of features within buffer of source, put all features but make ones within buffer distance a different color
• Can go feature to feature and find each distance from the feature to the source
  • Can put this info in a data table to analyze
• You can specify maximum distance within which locations should be included
• Can use tags for each location to easily map what’s within several distances of a source
• Options for point-to-point maps
  • Surrounding locations color-coded by distance from source
  • Surrounding locations color-coded by closest source
  • Spider diagram
    • Line drawn between each location and its source
  • Source features using graduated point symbols
• Creating a Distance Surface
  • Create raster layer of continuous distance from the source
  • Creating distance ranges
    • Display each cell using graduated colors either as continuous range or grouped into classes
  • Can specify maximum distance to limit GIS when it calculates distances
  • Need to create separate input layer for each source you plan on mapping
  • If mapping discrete features, place them atop distance surface (surface displayed using graduated colors)
  • Continuous data stored as raster cannot be shown on top of distance surface
    • To show, combine reclassified distance surface with surrounding features

Measuring Distance or Cost Over a Network

• • GIS automatically identifies all lines in a network within given distance, time, or cost of source location
  • ArcGIS provides street network that is updated regularly

• Geometric layer- composed of edges, lines, junctions, and turns

• Junctions– points where edges meet

• • Street networks commonly used for finding what’s nearby
  • Travel time is one of most common costs
  • You can specify the cost for turns from one segment onto another or for stops at an intersection, can also limit which segments in a network can be travelled

• Turntable- data table that lists the junctions for which you want to specify a cost

• • Can specify direction of travel along segments
  • If more than one center, GIS assigns segments to each concurrently
  • Create a boundary in these cases
    • Want list of individual locations
    • Need count of locations in area covered by selected segments
    • Have data summarized by area
    • Need a list, count, or amount for linear features or areas
  • Sum as you go in these cases
    • Want a precise count of locations along network segments
    • Don’t need list of individual locations
  • Can manually draw boundary or have GIS make it (compact or general)

• General boundary- connects farthest reaches of selected segments

• Compact boundary- outlines the selected segments

• Can have GIS create distance rings to find what’s within several distance or cost values
• Summing as you go
  • GIS sums counts or amounts as it searches outward from center along the network
  • Can specify that GIS stops assigning streets once it reaches a maximum count or amount
• Once GIS finishes assigning segments it automatically shows the entire network and highlights selected segments
• Display center using symbol easily distinguishable from other locations
• Label centers

Calculating Cost Over a Geographic Surface

• GIS creates raster layer in which the value of each cell is the total travel cost from the nearest source cell
• Calculating cost over surface shows you rate of change
• Make sure to specify what cost is (time, money, distance, etc.)
• To create cost layer based on single factor, reclassify an existing layer based on an attribute value
• To create cost layer based on several factors, combine all the input layers
• Calculated value is assigned to entire area covered by the cell
• Larger the cell, more approximate the value for locations farther from the cell center
• Can modify cost distance by assigning maximum cost or using barriers to specify areas that are off-limits
• Output layer is a surface of increasing cost values radiating outward from source
• When mapping, show discrete features on top of cost distance surface (distance grid displayed using graduated colors)

Chapter 4:

 

This chapter is about mapping Density which reveals where certain features are concentrated. It does this by standardizing values by area to make the comparisons a lot clearer. This is especially helpful when you are working with larger data sets like censuses and crime reports. Before creating your own density map, you should make sure you know what feature you are mapping so you can reveal the proper patterns and information that you are looking for. One of the methods to track density uses defined boundaries and includes dots for tracking or has that area shaded based on the density of what you are measuring. The second method uses a continuous density surface and includes a spreading feature that shows change over time and to highlight hotspots. Area based mapping methods are pretty simple and are very useful for unit comparisons. Density surface mapping which uses more statistical information that helps to show detailed patterns. Overall, both methods to mapping and tracking the concentration of information are very helpful to highlight trends. 

 

Chapter 5:

 

This chapter is about what is inside an area which can help to reveal to see if certain features occur in said area of interest.  This use of GIS is extremely useful for monitoring different activities within a certain area and comparing areas based on what is inside. There are a few things that you need to identify before using this method, one of these things are discrete locations, roads, and areas. You also need to identify continuous features such as categories and values of this area that you are looking into.  When looking at an area and analyzing it, there’s three methods to do so. One of these methods is drawing areas and features which gives you a simple and quick overview of the boundaries. The next method is by selecting features within an area, this allows you to look at lists and counts of features within your data sets. This allows you to see the overall statistical view of your desired area. The final method involves overlaying areas and features, this combines the boundaries into new data sets and calculates summaries for that overall area. This method works with multiple areas and it’s most detailed, but it also requires the most processing out of the three methods. Overall one of the highlights of GIS is using it to see data and change over time within an area and there’s many ways to do that effectively. 

 

Chapter 6:

 

This chapter is all about using GIS to find what’s nearby when looking at a map. The purpose of doing this is so that you can monitor events to find certain areas and identify groups of specified data sets.  You can also see what’s in a certain distance of the features that you’re interested in looking at. When defining the analysis, nearness can be measured in two ways: straight line distance which is a simple area of influence or network distance which might be the amount of time it takes to get to a certain place that you are mapping. There’s two types of methods when looking at the nearness of an area: there’s planar which is when you’re looking at small areas like a city or county, and there is also geodesic which is a larger scale region or even a global analysis.  As we learned before some of the results that we can see are lists counts and summary of statistics. 

Chapter 4:  Chapter 4 talks about map density and the importance of it. Map density is useful when mapping areas on a census or in areas in which vary greatly with size. It explains that in order for you to map density you use shades and the denser a population of whatever it is that you are mapping the darker it is. This chapter explains the differences between map features and feature values explaining t hat essentially the feature is what something is and the feature value is kind of like an adjective for the feature where it is just something that further describes the feature. It explains a couple of ways in which you can map density, the two ways in doing so are mapping density by defined area and mapping density by density surface. The density by defined area is used by mapping dots on the map and is largely considered to be less informational than mapping by density surface which uses blob type images. This chapter explains that you can compare areas to find something that reaches the goal of your map. One major thing this chapter tells you how to do is create a surface layer. These surface areas show you where about the points or line features are concentrated on the map that you are creating. It explains that the GIS calculates density by taking the cell size, search radius and units into account when mapping.

Chapter 5: This chapter explains the reasons in which we map the contents of the area in which the map is surveying. This is helpful regardless of if you are mapping single areas or many different areas at the same time knowing what exactly it is that your map is showing is extremely important. When you are defining what it is that lies within the mapped territory it is important that you summarize the information that you need and/or combine your map features with the area boundary and that in itself will create a summary of your data. Your data will consist of what? That is an important question before you summarize. What features are going to be in this map, what is it that is important for the audience to know? Another question that you will have to ask yourself is, are you features that you have created due to those data points going to be discrete meaning that they are unique and can be easily identified, or continuous which is a seamless representation of that data with no specific point?  After you have asked those questions you will need a list, count, or summary to get an idea of what is inside of the area you are going to map. It explains whether or not you want to show feature of your map that are partially outside of your focused area or whether you would just map partial features like portions of rivers and other large features that could span a large area.

Chapter 6: This chapter explains that you should also map what is nearby your specified area this is because it is nice to know the travelling range to places. The traveling range to areas within your map are measured in distance, time, or cost. This is helpful for when you want to make a map that is explaining the distance from another feature the example in the book is streets within a three minute range of the local fire station. So for other maps that have that same type of purpose it is important to know how to calculate the distance of things from each other and the distance between features on your map. You can define distance or nearness based off of a feature’s travel costs. Something that it explains is that your maps come out in a way that is presented as flat. So, your information will be slightly skewed just due to the curvature of the earth’s surface. So the maps will contain something of a straight line distance which you get from specifying your distance and source feature. This chapter also explains what a buffer is and how you use buffers in order to make your distances more accurate. Basically it has the GIS draw around your distance due to the buffer which leads to more accurate distances. These buffers also show features in which are close to or at least near more than one source of information on the line of distance. Once the buffer is placed you can use the buffer to select features that fall within it.

Chapter 4: 

There were 5 key points for chapter 4 – why map density, deciding what to map, two ways of mapping density, mapping density for defined areas, and creating a density surface. 

The reason that we map density is because it shows where the highest concentration of features is. It looks at patterns rather than locations and it’s helpful for mapping areas of different sizes. To map those areas, you have to shape them based on density value or surface. You have to look over what data you have and decide if you want to then map features or feature values. 

There are 2 specific ways of mapping density. Those are by defined area or by density surface. You would use mapping by area when you have the data already summarized by area. You would use mapping by density surface when you have individual locations. This chapter specifically goes into those two kinds of mapping in a lot more detail including how they are used, the calculations needed and what the results could look like. 

Mapping density for defined areas – you can make this in two ways. One is by showing density for each area graphically using a dot density map and the other is calculating a density value for each area and then shading based on that. An example of the calculation for that would look like this: pop_density = total pop/(area / 27878400). It also dives into how you would create that dot density map. 

Creating a density surface – these are created by GIS as raster layers. It helps show where point or line features are. There are a few things that go into calculating density surface, which include cell size, search radius, calculation method, and radius. The chapter goes over each of those parameters and what they do. To display a density surface you can use things such as graduated colors and contours. 

 

Chapter 5: 

The main topics of chapter 5 were why map what’s inside, defining your analysis, three ways of finding what’s inside, drawing areas and features, selecting features inside an area, and overlaying areas and features. 

As for why we map inside, it is to help monitor what’s occurring inside it, or to compare several areas. This is important to know whether or not to take action. In order to do this you have to draw a boundary on top of your features to define the analysis. You can find what’s inside of single areas, or several areas. The chapter then goes into more detail on those two. And another important thing to figure out is whether or not they are discrete or continuous features. 

When looking at the information you need from your analysis, you’re going to want to find out if you need a list, count, or summary. You then need to see if the features are completely or partially inside. 

There are 3 different ways of finding out what’s inside – drawing areas and features, selecting the features inside the area, and overlaying the areas and features. All of which this chapter goes into more detail about. It also goes over different guidelines that will help you choose the best method and how to make those maps. Look at locations and lines, discrete areas, and continuous features. 

When using your results there are a few things to look at – counts, frequency, and a summary of numeric attributes. This chapter also goes into a lot of detail on overlaying areas with features including what GIS does, how you would use them and what results you could get out of that. It looks at continuous categories or classes as well which includes a couple different methods: the vector method and the raster method and what the difference is between the two, as well as which one you should use. 

 

Chapter 6: 

The main topics for chapter 6 included why map what’s nearby, defining your analysis, three ways of finding what’s nearby, using straight-line distance, measuring distance or cost over a network, and calculating cost over a geographic surface. 

In terms of mapping what’s nearby, this is important because GIS can help find what’s occurring within a set distance. You also need to define your analysis and determine whether or not it’s by distance or by travel to or from a feature. 

Knowing what information you need is super helpful in creating the best method for your analysis. You can get a list, count or summary and each has their own benefits. Inclusive rings and distinct bands are useful in this as well. 

There are 3 different ways of finding what’s nearby – straight-line distance, distance or cost over a network, or cost over a surface and the chapter goes into detail for each of those regarding what they are used for and the pros and cons of each. 

Important information for straight-line distance: creating a buffer, getting the correct information which also involves finding features for multiple sources and several distance ranges and making a map. 

Important information for features within a distance: getting all the information and again, selecting features near several sources and distance ranges and making a map. 

Important information for feature to feature: specifying a maximum distance, getting the information, and making the map. Color coding is used a lot when making maps.. You can use it by distance or source and you can also use things such as spider diagrams and graduated point symbols.

The chapter goes into how to create distance surfaces which is something that has been touched on a few times within these past few chapters. You have to create distance ranges and then decide if they are discrete or continuous. Specifying maximum distance and multiple source features are also important. 

When measuring a distance or cost over a network, you want to specify a network layer and use GIS to help you. To assign street segments to centers you can use distance or cost and for setting travel parameters you need to specify turns and stops. It’s important that if you have more than one center, it is specified by GIS. Once GIS has identified the segments, you can find out what’s covered in those segments, including using boundaries and summing as you go. 

Lastly, for calculating cost over a geographic surface, it’s important to specify the cost by creating a cost layer. You can modify the cost distance and use barriers to get all the information needed. 

Chapter 4 Summary

This chapter explores various methods for mapping density and how the choice of method can significantly affect the way data is interpreted. Mitchell shows how changing what you map—like workers vs. businesses—can drastically alter the message of a map, which surprised me. He explains the differences between dot density and shaded density maps. While dot density helps compare specific locations, I personally prefer shaded maps since they’re less overwhelming and easier to read. Dot density can also be misleading, as the dots are evenly spread rather than indicating exact locations, and they can get lost in maps with complex boundaries.

Mitchell also discusses density surfaces vs. density areas—concepts I grasp generally, though I still find their differences a bit unclear. He introduces how to calculate density, which I understand in theory but feel I’d need to practice. One fascinating aspect of GIS is how it layers data to create richer, more detailed maps. Small elements like cell size, search radius, calculation method, and units all influence how a map looks and performs. That said, I’m still confused about the difference between areal and cell units, even with the example maps shown.

Chapter 5 Summary

This chapter focuses on mapping specific areas and identifying what features fall within them. Mitchell explains how drawing an area over existing data can help with comparisons. He also covers discrete vs. continuous features—I found continuous features a bit confusing since they change over time. Do they need constant updates, or can you only include them at a single moment?

I found it interesting that you can mark either partial or whole parcels in an area. Mitchell highlights how GIS handles many complex calculations for you, especially when creating overlays. He also explains how to layer data differently to get specific results, and how frequency can be shown with both maps and charts. One unclear part for me was how lines are handled when they cross multiple areas—Mitchell mentions GIS splitting them into new datasets, but doesn’t explain it much.

Chapter 6 Summary

This chapter is about mapping features within a set distance, especially for travel and travel cost analysis. I get the overall idea, but the details—like accounting for turn times, traffic lights, and stop signs—seem tedious and a bit overwhelming. Mitchell briefly mentions turntables for displaying this data, but doesn’t go into enough depth for me to fully understand.

He also introduces inclusive rings to show areas at different distance ranges. I’m curious if these require remaking the map each time or if there’s a faster method. A tool I found useful was buffering, which highlights features within a distance without adding a border. Another method he shows is the spider diagram, which looks cool but gets messy on larger scales. A particularly helpful application is mapping locations within a certain travel time—useful for businesses analyzing customer accessibility.

Chapter 7. 7-1. This chapter focuses on polygon data, such as how to make and edit polygons. To move an existing polygon, use the select tool, then go to the edit tap and select move, you can then freely move the polygon around, when it’s in the correct position click the green checkmark. To edit an existing polygon shape use the edit vertices button under the edit tab, then add points where you want to make changes then drag the highlighted areas to create the desired shape. To split a polygon use the split tool.  

 

7-2. This section teaches us how to create and delete polygon features. To create a polygon use the create feature class tool, and select polygon as the geometry type. To draw the polygon, go to the edit tab and select create and then the layer you want to work with. Using the line function draw the outline of the polygon and double click the last vertices to finish drawing the polygon. To make a polygon transparent select the layer then click feature layer and then in the effects group type in your desired transparency level. As you create polygons it is automatically added into the attribute table for that layer. To delete polygons, use the select feature, click on a polygon and then use the delete button in the edit tab. To snap a polygon to something such as a street use the snap function in the edit tab and then click create to make a polygon. 

 

7-3. This chapter is about using cartography tools. We used the smooth polygon tool to take away the edges on a polygon. 

 

7-4. CAD’s or computer aided drawings are often used in conjunction with GIS to display where something is and the interior of it, such as an academic building. CAD drawings cannot be edited directly so the data needs to be exported as a feature class. The new layer that was created converted polygon data to polyline, so the apply symbology from layer tool to import symbology to the polylines. To select the whole CAD layer, right click the feature in the contents pane, then go to selection, and select all. To merge the CAD layer to the actual feature layer, use the modify button in the edit tab. Then select Similarity 2D, then add new links. You then add points on the corners of the CAD layer and then on the corresponding concerns of the actual layer. Then when you are done click transform in the modify features pane. 

 

CAD layer on top of the actual feature layer. 

Chapter 8. 8-1. This chapter is about using geocode data. This is in essence features that have been assigned a name or value by a human, it is then matched with data that is present online or from a database. To start creating a geocode of zip codes, use the create locator tool, and import your data, select zip for the role, then for *ZIP select GEIOID10. This creates a locator in the catalog pane. To turn it into a geocode find the locator in the catalog pane, right click it and go to properties, then geocoding options, then match options. Use the geocode address tool to create addresses using your locator, this generates points on the map.  To rematch data, you can go to data for the layer, then click rematch. The collect events tool is useful for counting the number of features in a layer and generating graduated symbols for said feature. 

 

8-2. When using a geolocator, it is possible to alter the accuracy of the algorithm. In this section we set the min and max values for accuracy to be 10. This resulted in points with low accuracy. 

 

Chapter 9. 9-1. Chapter 9 focuses on spatial analysis tools. In section one we learned how to use buffers, which is just a polygon surrounding a map feature. To create a buffer around point data use the pairwise buffer tool, then if you want the output features to all be in the same layer set the dissolve type to be into a single feature. To calculate the frequency of points within a buffer select features that intersect the buffers and then calculate the summary of a field within the data table for your class of interest. 

 

9-2. To create multi-layered rings, use the multipole ring buffer tool, and input your set distances. To measure data within those rings, use the spatial join feature so that the data from one layer can be summarized in the rings. 

 

9-3. To determine distance as a function of time in arcpro, create a workflow. This is done by selecting the attributes you wish to work with, then in the analysis tab clicking on workflows, then network analysis, and service area. Once this is done select the class you wish to work with, in our case it was Facilities, then click on the service area layer ribbon, then in the travel settings select towards facility for direction. The cutoffs section is for the travel time. Click the run button on the left side of the tab to run your analysis. 

 

9-4.  This section teaches us how to use a network to create a model to show which public pools are located so that they achieve the optimal amount of attendance within a given range. This is done by creating location allocation in the network analysis tab. Then in the data group import the facilities that are going to be used, then import demand points. The demand points are essentially people that can potentially use the pool, this is how the model will draw lines later. In the travel settings group, select towards the destination for direction, then include the number of facilities you have in the facilities group. Once all information is entered, click run model (it also helps to hide the demand points). 

 

9-5. Performing cluster analysis. Using the multivariate cluster tool, we are able to perform cluster analysis using multiple variables.

 

Multivariable analysis of age of arrested individuals 

Chapter 4. 4-1. This section is about making a geodatabase. These are useful since you can continuously add data to them, and they have no limit to the amount of feature data that you can add to it. To start, make a new project map, then add the data that you need, in our case it is the Maricopa County folder. To add this navigation to the contents pane and click folders then add folder connection and select the folder you want to import. To accrual use the data in the folder we imported we had to convert a shape file (an old file type) to a feature class. To convert from a shapefile to a feature class go to tools in the analysis pane, then select the export features tool. There are other export tools that can be used to convert different types of files (like CSV) to tables (Export tables). Note that to turn off a layer in a geodatabase you need to delete the feature, you can not simply turn it off like in the contents pane. 

 

4-2. To create a new column in a data table right click the feature class, go to its attribute table, click options, then fields view. Add a new field, then save. It will likely have no data in it so go back to the attribute table then right click the new column, then calculate the field. In the calculate field pane double click the data you want to be imported to that column then ok. To join tables, right click your desired feature layer, then click joins and relates, then add join. This join is not permanent so in order to make it stick right click the layer you have been using this whole time, then click data and export features. This converts the table that was made into a feature layer. 

 

4-3. This section has us using attribute queries which allows us to select data rows and spatial features based on the values we tell it. This is similar to the SQL we did in the previous week. We have to use a script to do our query and some key things to know are: text values need to be in “”, number values do not need to be in quotes, most of the time an or statement needs to be in parentheses. To display only features that we want from a class, use select by attribute and use the expression you want, then open the attribute table for said layer, then go to properties for that layer and make a definition query and use the same expression you use to select, then click ok. This displays only the features you want displayed, and this can be verified in the table. To make an “or” expression with the definition query write out your expression and then click the SQL editor button to see the actual script, then add parentheses around the or statement. We used attribute selection to observe the amount of burglaries on weekends vs. weekdays. This was done by using a definition query to select the month of august and the crime type and attribute select to filter the days of the week. We selected Saturday or Sunday, and then in the attribute table we can use the switch button to select all the weekdays and back; allowing us to see the distribution of weekend and weekday crimes

 

4-4. This section teaches us how to use the spatial join tool. This tool allows you to join the spatial data from 2 feature classes into 1. To do so just locate the spatial join tool and select the layers you would like to join together. 

 

4-5. Creating central point data for polygons generates a point that is in the middle of a polygon. This is done by using the calculate geometry attributes tool, selecting your input field, then using 2 fields of X and Y with the central point as X, and Y coordinate respectively. This is a good way to generate graduated symbols since they are centred. 

 

4-6. This section is about creating a table for data that is hard to interpret or understand called one-to-many data, which includes terminology or codes that untrained people likely cannot interpret without a reference (i.e. police or FBI codes). To join the table with the data, right click the point layer with spatial data, then select joins and relates, then add join. 

 

Chapter 5. 5-1. This chapter is about spatial data, specifically how coordinate data is interpreted and generated. There are different types of spatial data projections that are useful at different scales due to the curvature of the earth. The standard coordinate projection that is used for GIS is GCS WGS1984, but it is not good at showing real spatial distribution, so instead we used Hammer-Aitoff world to show the globe, which shows distribution rather well at a global scale. This is done by right clicking the base map and going to properties, coordinate systems, projected coordinate system, world, and then Hammer-Aifoff. 

 

Hammer-Aitoff world map projection. 

 

5-2. It is good practice to visualize projections that show good area, over shapes and angles. A good projection to use for the continental United States is Contiguous Albers Equal Area Conic. 

 

5-3. When choosing a projection for the United States check the livingatlas website for your study areas code. This helps to choose a projection that is best for your region that has the least amount of distortion. This section also includes how to add data to a map, this is done by clicking add data under the map tab and selecting the data you want to add. 

 

5-4. There are many ways to store data, and many file types. Some of the most common for shape files are .shp, .dbf, and .shx. Those files are for geometry, attribute tables, and indexes of spatial geometry respectively. To convert a shape file to a table just like with feature classes, use the export features tool, then right click the table and select create points from table, then select current map under coordinate system. To convert KML data to a feature class, use the KML to layer tool. 

 

5-5. This section has us downloading US census bureau data and using it to make our map. To get started go to census.gov/cgi-bin/geo/shapefiles, choose a year, and layer type. Then further census data can be downloaded from data.census.gov. To format the data in XL we made columns: GEO_ID, MALE_BIKE, and FEMALE_BIKE for our commuter census data. 

 

5-6. To add data from the living atlas, go to the catalogue pane, then select portal, then living atlas. This allows you to use data from the web, in our case we accessed the NLCD which shows national land cover usage. We then used the extract by mask tool to create a mask that only shows the raster data in Hennepin county. In the extract tool, in the environment under the extent tab I used the current display, and then the extent of layer buttons to select Hennepin county. Contour data and other types of data can be found at apps.nationalmap.gov/downloader. 

 

Chapter 6. 6-1. This chapter deals with dissolving block groups and aggregating polygons. To dissolve block groups use the pairwise dissolve tool. 

 

6-2. This section teaches us how to create a study region using multiple layers with an abundance of features. To display only a region from a layer, use the select by attribute function to select your designated space, then go to data for the layer containing said feature, then click export feature and create a new layer. Then to select data within your exported feature you can use the select by location button to designate the layer you want to select data from and where you want that to be localized. To clip 2 layers together, use the pairwise clip tool, this allows you to select data within a given area. 

 

6-3. To merge multiple layers into one use the merge tool. 

 

6-4. To merge points from 2 datasets into one, use the append tool, the target dataset is the one where all the points are going and the input datasets are where the data is coming from. 

 

6-5. To generate a layer that has lines or points intersecting a given set of data, requires the use of the pairwise intersect tool. 

 

6-6. To create a layer that has set data that is combined from 2 other layers, use the union tool. 

6-7. The tabulate intersection tool can be used to associate data with different classes, such as assigning a split amount of disabled persons to different fire companies.

Chapter – 1: 1-1. We made a map of Allegheny County, Pennsylvania. We started by highlighting Allegheny county, then we displayed the Urgent care clinics in the area, the FQHC clinics, Poverty risk area, population density, as well as rivers and Pittsburg for context. By looking at this map you can clearly see that the distance between hospitals increases as population decreases.

1-2. Zooming in and out of the map while vector data is being displayed can either mask or display it. Such as if you zoom in to a certain extent then vector data is no longer shown and if you zoom out it is redisplayed. This was shown when we turned off population density and selected poverty density instead, which is raster data and is displayed all the time regardless of magnification. To zoom in on a given area you can use the scroll wheel or you can press shift and draw a rectangle and it will zoom into that specific area on the map. 

 

We then used SQL (structured query language) which is how we query tabular data. We used it to select McKees Rocks; this was  done by selecting the Select by attribute button in the map tap, then Municipalities was selected under input rows, then in where we selected Name, then is equal to, lastly Mckees Rocks. 

 

1-3. To look at attribute data for a class, you can right click on it in the contents pane, then select attribute table. When doing this we adjusted where the website was in the table. Data can also be sorted by right clicking on the column and sort either ascending or descending. Data can also be turned off in the display, this is done by opening the attribute table and clicking the menu button (3 stacked lines) this allows you to rearrange features or turn on/off their visibility.  In this window you can also select a number of features, or to exclude features from a selection you can click the select button, click on an amount of featured data displayed on the map, then in the attribute table by selecting switch it will select all of the features besides the ones that were highlighted on the map. 

 

Descriptive statistics can be generated using GIS. To do so in ARC, navigate to the analysis tab, click tools, expand toolboxes, analysis tools, and summary statistics. 

 

1-4. To change the appearance of a symbol for a class such as a point, right click that class in the contents pane, click symbology, and then select the current symbol and the new symbol you would like to use. In this pane it is also possible to change the color of the symbol. Similar to symbology you can label and alter the labels of certain classes. We used municipality data, and labeled it. This is done so by right clicking a class and selecting label, then you can alter the text by selecting label properties. 

 

We were also able to include data that was not previously in the contents pane by going to the catalog pane, opening the database, and dragging parks onto the map. This added a new class to the contents pane. 

 

Chapter 2: 2-1. This chapter is about using maps to solve or investigate problems. This chapter looks at using different layers to best represent different data or variables, and this is achieved through using thematic maps. This is a type of map that has the subject/variable in full display and uses spatial data to  give context to your subject. 

 

To highlight land use, go to symbology for a given class and then select unique values for primary symbology. This then generates colors for each feature type, it is possible to select specific colors within this panel. This is done by clicking more and then formatting all symbols.  

 

2-2. Using the map made in 2-1 we added general and detailed labels. When selecting a class in the contents panel it opens up 3 ribbons, one of which is titled Labeling and this allows you to select the type of field to use, in our case we used zone. Once you’re done making your selection click the large Label icon. To remove redundant labels, right click the layer you want, go to labeling properties, position, conflict resolution, remove duplicate labels, and then select all. 

To remove pop-ups right click a class and then select disable pop-ups. To manage them go to configure pop-ups, double click the fields, deselect the Only Use Visible Fields and Arcade Expressions, along with the Display box, then you can select the features that you want displayed when you click on a polygon. 

2.2 New York city land use map. 

 

2-3. Some maps have a large number of features, such as point data. In cases where you want to filter what is being displayed, you can do so by going to the properties of that class, selecting Definition query, new definition query, then creating clauses for the features you would like displayed. We then used symbology to change the color and shape of the points for different food facility types. This makes it easy to distinguish between the different facilities and it also makes it easy for those with colorblindness to understand the map. 

 

2-4. In this section we created a choropleth map which uses colors, and color values to visualize data. This method uses natural breaks as a default setting, but quantile classification can be a suitable alternative for a first time use since it’s easy to understand. To make a choropleth go to the symbology for the layer you want to use, select graduated colors, then your choice of field, your chosen method (quantile or natural break), your number of classes, and your choice of color scheme. You can also make a histogram to observe trends in the data. To convert a 2D to 3D you can do so by going to the view tap, in the view group section click convert and select to local scene. Then drag the layer you want converted to 3D to the top of the layers heading. 

 

2-5. In this section we create graduated symbols to display data. This is done by changing the symbology to graduated symbols. 

 

2-6. To use percentage data, create whatever symbology you want to use, then go to advanced symbology, format labels, then change category to percentage, percentage to number represents a fraction, and rounding decimal places to 0. To import the same symbology for a different layer, go to its symbology, go to options, then in the symbology layer, select the layer you are importing the symbology from. To compare layers easily, go to feature layer, and in the compare section click swipe, this allows you to click and drag the screen to reveal the layer underneath without having to turn layers on or off. 

 

2-7. Dot density can be used to display more than one variable, however when using dot density be sure to use the same general hue for the colors so that it does not emphasize one variable over another. To make a dot density map go to symbology and choose dot density for the primary symbology. The dot value represents the amount of individuals 1 dot represents. 

 

2-8. When working with visibility ranges it is important to note the ratio that you are using. For instance 1:50,000,000 is considered small scale despite the value being large, this is because 1/50,000,000 is a rather small number. Whereas a ratio of 1:24,000 is considered large scale since 1/24,000>1/50,000,000. To set visibility ranges, select your layer, then labeling at the top of the screen, then if you want it so that if you zoom out any further you wont see the label anymore set the minimum scale to current. This can be done for labels as well as entire feature layers, by repeating the same steps as before in the feature layer tab. 

 

Chapter 3: 3-1. This chapter focuses on how to share maps with those who have limited experience with GIS or do not have access to Arcpro. This section is about how to make map layouts. To make a layout go to insert, and new layout. To add a map to the layout click map frame and select the map you would like to include. When resizing the maps in the layout, to get the map into full view there is a full extent function in the layout tab. When placing objects in a layout, it is helpful to create guides which can be made by right clicking the rules and selecting add guide, guides allow you to snap objects to them. To add a legend just click one of your maps in the layout to make it active, then click the legend button at the top of the screen. To add a chart right click your data frame, then create a chart, select the elements you need to create your chart, then click export to save it. 

 

3-2. This section focuses on how to share/publish a map. To do so a map needs to have a base layer, and a property of the map needs to have been changed. To share a map, highlight the map layer in the contents pane, then under the share tab, select web map, then insert any information you need, click everyone for it to be a public map, then click share to publish it. To view your maps, go to arcgis.com, sign in, then go to contents, then my contents and your map will be displayed there. To modify the symbology in the web version, click the layers button, select your layer, then go to styles on the right side of the screen, this is where you can change the symbology. The same can be done for pop-ups by navigating to the pop-ups tab on the right side of the screen. 

 

 

 

3-3. This  section teaches you how to use ArcGIS story maps. This is essentially a better version of google sites. It allows you to create storyboards that include maps and charts. To start working on it insert a picture for the background at the top, by clicking the add cover button and selecting an image. Then you can start typing where it says “add your story”. To insert maps click on the plus button and add a sidecar then select your map. 

 

3-4. This section teaches us how to make a dashboard, which is a map that has other forms of data accessible such as charts. To make a dashboard start by uploading a map to arc online. Then open the map and click open the dashboard. From here you can add a table by clicking the plus sign button that says “new element” then the middle of the map, and then by selecting table. To add a chart it is the same thing but select the type of chart you want to insert instead of a table. Dashboard – Bzdafka 

Map displaying age of 311 calls to have overgrowth debris removed. Size of point corresponds to age

i read the chapters 🙂

CHAP 4

• Map density shows where the highest concentration of features is
• Useful for looking over patterns as opposed to individual features
• Density maps allow for one to look at features with a higher concentration than others
• These kinds of maps are highly useful when varied by size
• You can differentiate between defined areas or just density size
• You will need to include conversion factors in calculating density if map units is different than density units
• If defining density by a specific region, you run the risk that density might change in a given space and not remain uniform throughout
• Dot maps are density maps which use dots to represent density, 1 dot per a certain amount of the count.
• GIS can be used to summarize feature values within polygons
• Cell size: defines how course the patterns are, needs to be goldilocked to balance pattern definition with storage saving
• Search Radius: affects generalization of patterns
• There are 2 methods to generate density maps; the “simple” method and the “weighted” method.
• GIS lets you specify areal units, which are the unit the map measures itself in (i think?)
• Centroids: density surface map feature which allows you to define an area
• Contour lines and colors both often used for map density
• Density maps show us how values vary across regions of a map as well as distribution of samples
• Sometimes density map data can be inaccurate; if you are studying how many employees there are, the suburbs would be empty because there are no businesses in them

CHAP 5:

•  Mapping an area helps us monitor what occurs within it
• Data must be analyzed based on whether it is a singular discrete area or multiple areas/continuous areas
• Data for analysis can be arranged as either a list, count, or summary
• There are three ways of figuring out whats inside
• method 1: drawing area and features. Requires a dataset with a boundary of an area, good to see whats inside of it
• method 2: selection of the features inside the area. needs a dataset with a boundary like above, but also all of the attributes you want to summarize. its good for getting a list of summaries from within 1 group
• method 3: overlaying areas and features. this method requires the same things as the above method, good for finding which features are in each of several areas
• Method 1 is only visual, method 2 only works for one area, and method 3 requires the most processing
• maps can be made by drawing features in different or same symbols
• Discrete areas can be made by either shading the area in on top of other boundaries or by making the boundary of the area thicker than surrounding ones or by doing both
• You can select different parcels within your area for summarization
• Count: a summary which shows the total number of features within an area
• Frequency: a summary which shows the number of features with a given attribute or value inside an area. This is displayed most commonly as a table, but can be turned into a pi chart
• The most common numerical summaries are:
  – SUM: all of the features numerical values added together
  – Average, AKA mean: the sum of the numerical values divided by the amount of features
  – Median: The absolute middle value of the numerical values
  – standard deviation: showcases how much the values stray from each other.
• You can overlay features on top of each other

CHAP 6:

• Mapping nearby areas helps to identify the area, and may be useful for study- like a study on travel distances or trying to plan a walkable city or any other sort of example
• “nearby” is based on a distance set by you, either whatever is in the source feature’s area or within a certain amount of travel from the area
• nearby can also be measured by “cost”, such as how long it takes someone to get through heavy traffic
• analysis differs depending on if you’re accounting for the curvature of the earth
• You can specify a single range or several ranges
• “Inclusive Rings” are used to see how total amounts increase as distance increases
• “Distance Bands” are used to compare distance to other characteristics
• There are three ways of mapping what is nearby:
• Method 1: Straight line distance. Specifies a source feature and the distance and GIS finds the area or surrounding features within it, presumably with just a straight line. Requires a layer containing source feature and surrounding features.
• Method 2: Distance and/or cost over a network: Specifies source locations and a distance or travel cost between them using linear features. You can use the featured segments to find surrounding features.
• Method 3: Cost over a surface. You specify the location of source features and travel cost, allowing GIS to make a new layer showing travel cost from each feature. Requires a layer containing source features and a raster showing cost surface.
• You can create a buffer by specifying source location and buffer distance- this creates a line around the feature(s). You can even have the GIS sense when these overlap and create a single buffer area out of all of them.
• With the buffer you can specify only the feature points within the buffer, allowing for analysis of data within
• You can create seperate buffers per range and overlap them with inclusive rings, or have GIS make multiple distance bands
• You can also use selection to specify points within a range, which works similarly to buffer selection methods
• You can have GIS ID the actual distance between two locations
• You can specify maximum distance in which locations can be included
• GIS can also identify nearby networks, such as streets
• It can also also ID across a geographical surface such as streams or mountains

Chapter 4: Mapping Density

Mapping density helps show where the concentration of features is the greatest, and is useful for looking at patterns instead of the locations of features by themselves, for both areas with many features or features per unit of space. When deciding what to map, you should think about the features you’re mapping, as well as any information you might need (density surface), using either data that has already been summarized or by mapping density or feature values yourself. The two ways of mapping density are by a defined area, such as a dot map, if the data is already summarized, or by a density surface, using a raster layer in which each cell gets a density value based on the number of features within a radius of the cell, if you have individual locations, sample points, or lines. A density surface is created by using raster layers, where GIS calculates a density value for each layer. A neighborhood is defined, and the total number of features is divided by the area, which is then assigned to a cell. This creates an average of the features per area. Larger cell sizes create a coarser surface that processes faster, while smaller cells create a smoother surface that processes slower. To calculate cell size, you need to convert units to cell units, then divide that by the number of cells, and take the square root of that number. The search radius is the number of features divided by a correspondingly larger area, in which a larger search radius will produce more generalized patterns, and a smaller search radius will produce less generalized patterns. Calculation methods for cells are either simple (creates overlapping rings), or weighted (creates a smoother surface). Units chosen to create a cell should correspond with the features and what you hope to get out of the map.

 

Chapter 5: Finding What’s Inside

Mapping inside an area shows what is occurring inside an area, and is useful for comparison. You should consider whether you will need a single area or multiple areas. A single area is useful for monitoring activity and summarizing information, while multiple areas allow for them to be compared. Features can be discrete (unique and identifiable) or continuous (seamless, a summary). A count, list, or summary should be used as information. Three ways of finding what’s inside an area are drawing areas and features, selecting features inside an area, and overlaying the areas and features. When making a map, Locations and lines should be used for individual locations or linear features, discrete areas for seeing parcels inside a single area, and continuous features for drawing the areas symbolized by category or quantity. Selecting features inside an area is used for specifying the features and the area, and GIS then flags features in a specified area. The amount of features in an area can be counted in the following ways: 

• Count – total number of features in an area
• Frequency – number of features with a given value, or range of values
• Sum – overall total or total by category
• Average – total / # of features
• Median – middle value of a dataset
• Standard deviation – the average amount that values are from the mean

 

Finally, overlaying areas and features is used for finding discrete features within each area. 

 

Chapter 6: Finding What’s Nearby

Mapping what is nearby an area or feature allows GIS to find what is occurring within a set distance of a feature, and also find out what is within traveling distance. In defining your analysis, you should be able to define what is near, expressed as distance, time, or cost of traveling to or from that location. Of those options, mapping travel is most precise. You should also be aware of whether you’ll need to take into account the Earth’s curvature (geodesic method) or not (planar method). Information needed to map what is nearby should be a list (ex, a parcel ID and address), a count (by category), or a summary statistic (total amount, total/category, or a statistical summary). Distance and cost ranges can either be an inclusive ring, which is a circular area, or distinct bands, which are essentially multiple inclusive rings stacked on top of each other. There are three ways to find what’s nearby: 

1. Straight-line distance: Specify the source feature and distance, and GIS locates the area or features nearby
2. Distance or cost over a network: GIS finds segments within range or specified source locations and a distance or cost within each linear feature
3. Cost over a surface: GIS creates a new layer showing travel cost based on a specified location of the source features and a travel cost

Straight-line distance can be used by creating a buffer defining a boundary and what’s inside it, selecting features to find features within a distance, calculating feature-feature distance, or by creating a distance surface. The equation to find distance is as follows: square root of (x1 – x2)^2 + (y1 – y2)^2. To create a buffer, specify the source feature and the buffer distance, and GIS will draw a line around a certain distance from the feature.