Month: September 2025

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.

Chapter 4 talks about mapping density. Mapping density shows you where the highest concentration of features are. It can be useful for looking at patterns rather than at the locations of individual features, and for mapping areas of different sizes. In order to map density you can shade areas based on density value. You can use GIS to map the density of points or lines. For lines. The density is usually based on length per unit area. There are different types of methods you can use. You can map density by area which is useful if you have data already summarized by area, or lines or points you can summarize. You can also create a density surface which is useful if you have individual locations, sample points or lines. Which method you want to use depends on what you have. In order to calculate density values you need cell size, search radius, calculation method and units. Cell size determines how smooth the surface is. If the cell size is smaller the surface will be smoother. If the cell size is large the surface will be more coarse. Search radius is also very important. The larger the search radius the more generalized the patterns in the density surface will be. There are two different

Chapter 5 talks about finding what’s inside. People map what’s inside an area to monitor what’s occurring inside or to compare several areas. You can draw an area boundary on top of the features to find what is inside. Geographic selection is also a quick way to see what features are within a given distance of another feature. If you have data that is already summarized by area you can only summarize it using boundaries that fully enclose the areas. You can also use GIS to create a report of selected features. You can also use GIS to create statistical summaries using the tools that are available with GIS. Statistics include average or mean, median, and standard deviation. You also want to create a map to see which features are inside in addition to statistics.

Chapter 6 talks about finding what’s nearby. You can use GIS to find out what is happening within a certain distance of a feature. Traveling range is measured using distance, time or cost which can help define the area served by a facility. Distance is one way of defining and measuring how close something is. But nearness doesn’t have to be measured using distance. You can also use cost to measure what’s nearby. You can use distance or cost to map what is nearby based on travel. For some analyses you can calculate the distance either assuming the earth is flat or taking into account the curvature of the earth. Once you have identified which features are near a source, you can get a list of features, a count, or summary statistic based on a feature attribute. An example of a list is the parcel ID and address of each lot within 300 feet of a road repair project. An example of count would be the total number of calls to 911 within a mile of a fire station. A summary statistic can either be a total amount such as the number of acres of land within a stream buffer, an amount by category or a statistical summary. To create a buffer you specify the source feature and the buffer distance. Once you create the buffer you can display it to see what’s within the distance of the source.

Chapter 4: Chapter 4 talks about mapping density. Mapping density is important because it allows us to see the highest and lowest concentrations of what we are looking at in a given area. This chapter outlines 2 main methods for mapping density, the first one being mapping by a defined area. We can use a dot map to represent the density of individual locations summarized by defined areas. The dots are distributed randomly within each area; they don’t represent actual feature locations. The closer together the dots are, the higher the density of features in that area. Dot density maps show density graphically, rather than showing the density value. The second method is mapping by a density surface. A density surface is usually created in the GIS as a raster layer. Each cell in the layer gets a density value, such as a number based on the number of features within a radius of the cell. This approach provides the most detailed information but requires more effort. A dot map simply represents density graphically. The dots in a dot density map represent total numbers or values in each area rather than a calculated density value. When creating a dot density map, you specify how many features each dot represents and how big the dots are. You may need to try several combinations of amount and size to see which one best shows the patterns. The larger the amount represented by each dot, the more spread out they will be. Select a value that ensures the dots are not so close as to form solid areas that obscure the patterns, or so far apart as to make the variations in density hard to see. It’s very important when mapping density, in any form, to make sure your map is still easy to understand what you’re trying to map, and picking the type of density map to create is a large part of that. 

Chapter 5: This chapter mainly focuses on the statistical analysis methods for understanding geographical relationships and patterns, including correlation and regression analysis, to better understand geographical processes. The chapter emphasizes grasping the concept, capabilities, and limitations of these tools. There are 3 ways of finding what is inside. We can draw areas and features, select the features inside that area, or overlay the areas and features. Drawing can be used when we need to find out whether something is inside or outside an area, selecting is used to get a list or summary of what’s inside the area, and overlaying the areas and features is used to find out which features are inside which areas, and summarizing how many or how much by area. When we get the results we need, GIS can create a report of the features we’ve selected. Typically, our summaries can come in either counts, just a count of the selected area, or the frequency of data within an area. The GIS uses either a vector or a raster method to overlay areas with continuous categories or classes. For the vector method, the GIS  splits category or class boundaries where they cross areas and creates a new dataset with the areas that result. For the raster method, the GIS compares each cell on the area layer to the corresponding cell on the layer containing the categories. When deciding which one to use, we need to remember that the vector method provides a more precise measure of areal extent but requires more processing and postprocessing to remove slivers and to calculate the amount of each category in each area. And the raster method is more efficient because it automatically calculates the areal extent for you, but it can be less accurate, depending on the cell size you use. A small cell size will give more accurate results but requires more storage space, processing power, and time. Raster overlay also prevents the problem of slivers. It is often faster because the computation that the GIS must do is simpler.

Chapter 6: This chapter is about finding what is nearby. Using GIS, we can find out what’s occurring within a set distance of a feature. Finding what’s within a set distance identifies the area, the features inside that area, and the area affected by an event or activity. Distance is one way of defining and measuring how close something is, but we don’t have to measure nearness using distance; we can also measure what’s nearby using cost. When mapping travel, we can use either distance or cost. Mapping travel costs gives you a more precise measure of what’s nearby than mapping distance and may require more data preparation and processing. When trying to find distance when mapping, we need to decide whether or not to take into account Earth’s natural curvature. The planar method is used when we don’t need to take into account Earth’s curves, and the geodesic method is used when we do need to take Earth’s curve into account. The planar method is appropriate when your area of interest is relatively small, such as a city, county, or state. The results of your analysis will appear as the correct shape when displayed on a flat map. Use the geodesic method when your area of interest encompasses a large region, continent, or even the entire Earth. Output layers created using this method will be displayed correctly on the curved surface of a globe. Inclusive rings are useful for finding out how the total amount increases as the distance increases. Bands are useful if you want to compare distances to other characteristics. To measure what’s nearby, we can use straight-line distance, distance or cost over a network, or cost over a surface. Once the GIS has selected the features, you can get a list, count, or summary statistic based on an attribute.

Chapter 4:

This chapter deals with mapping density, which allows you to see the concentration of certain features, rather than individual data points for each feature, which can make observing trends in distribution easier. Generally, density is displayed using a gradient of colors, with different shades representing different concentrations of the feature in question. Alternatively, dot density mapping can be used, where each dot represents a certain quantity of a feature in a general area, rather than the location of any one specific feature. Since density is calculated by taking the total number of a feature in some area, and dividing it by the area of the region it is found, it can be useful in showing things like population densities across counties, even if the size of the counties vary. Another factor in making density maps is cell size and search radius. As cell size and search radius increase, patterns become more generalized, making trends easier to pick out, but if the radius becomes too large, the pattern may become too general and no longer accurately represent the data. When calculating the cell values for the density map, there is also the option to use a weighted average, rather than a simple averaging of all the points within the search radius of the cell, and by using a weighted average, an easier to interpret, albeit more general map is produced. Rather than using a gradient of colors to represent the different density values, contour lines can be used to represent regions of equal density, with areas having more rapidly changing density having a higher concentration of lines close together. Often, using two methods in conjunction, such as a dot map overlaid on top of a gradient map will most accurately represent the data, allowing you to visualize both general trends in the data, as well as specific data points that would be lost if only a gradient map was used.

 

Chapter 5:

Mapping what’s inside an area is a useful tool for making determinations about actions that should be taken and to find trends or make comparisons between areas. Finding what is inside an area usually begins with determining whether the data you are looking at is inside a single area, or within several disconnected areas, along with whether the features are discrete, like store locations, or continuous, like soil type or rainfall amounts. Depending on the research you are conducting, you can also make decisions about whether to include features that are partially within your area, or within a certain distance of the feature you are focusing on. Multiple methods exist for finding what’s inside an area, those being drawing the area and the features, selecting the features that are within the area, and finally by overlaying the area and its features on top of each other, then calculating the stats for the areas where they overlap. When overlaying discrete features like house locations with your area, you are able to create summaries regarding quantities, densities and any other data you have available for these points. Meanwhile, if you are working with already summarized data, or continuous data like rainfall amounts, you must make sure that your summarized data falls completely within the area you are researching, since you cannot subdivide already summarized data further. Additionally, when overlaying areas on top of each other, sometimes slivers may occur, where small areas of overlap are formed due to boundary mismatches. In order to determine which areas are, or are not slivers, there are multiple methods that can be used, including comparing the potential sliver size to the smallest area input, since areas smaller than that value may not be accurate, or by comparing the sliver dimensions to the accuracy of your collected data, and removing areas smaller than this threshold.

 

Chapter 6:

GIS can be used to find out what is within a distance, travel range along roads, or travel range in terms of time, of a feature or region. Defining distance by straight line distance is often used when determining area of influence, such as all properties within 1 mile of a power station, while using a cost, such as travel time or distance, can be more useful when finding precisely how many of something are within some distance along roads, such as all bus stations within 3 minutes of walking from a store. By creating a buffer around objects, you can find which features are within a distance of the object, and by selecting multiple objects, you can find which features are near a set of objects, like which houses are within a quarter mile of a fire hydrant. Similarly, by computing statistics for multiple distance ranges around a single or set of objects, you can find differences in the ranges of features effected at each distance, such as houses within 3 vs 5 vs 10 minutes of a fire station. Another way to visualize distance data is by using a distance surface, which superimposes a gradient onto the map to help show how distance or cost changes as you get farther away from your object. By selecting multiple objects, you can even highlight the regions that fall within or outside a distance range for both objects, like houses in a city within 4 minutes of two or more fire stations. Measuring distance by cost, be that travel time or distance, allows you to set specified time and distances costs for each road segment, turn, and other factors along the path, allowing you to accurately estimate boundaries based on travel factors. Cost distances can also be calculated for surfaces or continuous features like terrain, allowing assessment to be made, for example, for the maximum distance a road could be built through a hilly region, or all forested areas within some cost distance of a house in the wilderness.

Chapter 4-1 importing data into a new ArcGIS Pro project
– create project
–Open Arc project, under new project click map then determine name and location and ok it
–save project as (tutorial4-1YourName)
-set up folder connection
–use folder connections for quick and easy access
–open catalog pane- expand folders expand youth pop- add folder connection- browse to chapter 4 file add MaricopaCounty to box and ok
-converst a shapfile to a feature class
–shapefile is a spatial data format for a point, line, or single layer polygon
–on analysis tab in geoprocessing group click tools- in georprocessing pane search for export features(converts shapefile to feature class)- for input features click browse then expand folders select desired and ok- for output feature class type cities (for this instance)
-import data table into file geodatabase
–verticle columns have attributes names, describing data in column
–horizontal row represents a census tract
–export data tool
-use database utilities in catalog pane
–create, copy, rename, and delete file geodatabases and anything else in the catalog pane
–deleting tables and feature classes from a file geodatabase is permanent however recoming a layer from contents pane only removes it from map
4-2 modifying attribute tables
-delete unneeded columns
–in contents click tracts then data design then fields- this view allows to create and modify fields in a table- hold ctrl while selecting then restore anything you dont want to delete then save to finilize deletion
-add field and populate it using calcculate field tool
–for census data must retrieve from actual website then add census areas and join datta tagble to shaprfile attribute table bsed on geocode to make file managable-ensure both tables are able to be joined with no leading zeros in file id
-file joins arent permanent to do so export features
4-3
–linking tabular data to the spatial features in feature classes.
–linkage allows symbolize maps using the attribute data
-data range queries

• queries often use date-range criteria
• 4-4
  • aggregrating data with spatial joins
    • aggregation of piont data requires a spatial join
• 4-5
  • Arc creates central points on the fly and renders them as point features if graduated symbols for symbology is chosen
• 4-6 creating a new table for a one to many join

Chapter 5 spatial data

• 5-1 working with world map projections
  • • geographic coordinate systems use latitude and longitude coordinated for locations whereas projected coordinate systems use a mathematical transformation from an elliposid to a flat surface and 2d coordinates
  • examine a world map in geographic coordinates
    • distortions are caused by displaying a map in geograaphic latitude and longitude coordinates
  • project the map on the fly to hammer-aitoff
• 5-2 working with us map projections
  • • you can either get accurate areas or accurate shapes and angles but not both
  • setting projected coordinate systems for the united states
• 5-3 setting projected coordinate systems
  • • for medium and large scale maps use localized projected coordinate systems tunded for the study area and that have little distortion
  • look up a zone in the sate plane coordinate system
    • state plane coordinate system is a set or coordinate systems that seperates the states and its territories
  • add a new layer to set a maps coordinate system
    • 2 options- add a layer with a coordinate system to a blank map, set a default  coordinate system for all new maps in a project
  • add a layer that uses geographic coordinates 
  • change a maps coordinate system
    • us developes the universal transverse mercator grid coordinate system it covers the worl dwith 60 long zones defined by meridians that are 6 defrees wide 
• 5-4 working with vector data formats 
  • examine a shapefile 
    • many spatial data suppliers use the shapefile data format
    • shapefile consists of at least 3 files- shp(geometry of features), dbf(attribute table), and shx(index of spatial geometry)
  • import a shapfile into a file geodatabase and add it to a map
    • use conversion tool to convert a shapefile into a feature class and store it in a file geodatabase
  • x,y data
    • GPS units and many databases provide aspation coordinates as x,y coords
  • convert a KML file to a feature class
    • kml is file format ssed to display geographic data in many mapping allplications
• 5-5 working with us census map layers and data tables
  • dowlad census TIGER files
    • when using census data or frequently updated data ensure use of correct time period
  • Dowload census tabular data
  • process tabular data in excel]
    • you can use excel to clean up dowloaded data making it easier and more accurate to use
  • add and clean data in arcgis pro
  • join data nad create a choropleth map]
• 5-6 dowloading geospatial data
  • • many government organizations display their data on public websites such as DOC, NASA, EPA, etc.
  • Add a land use layer from arc living atlas
  • extract raster features for hennepin county
  • Dowload local data from a public agency hub
    • many local agencies supply spatial data through open data portals or hubs

Chapter 6

• geoprocessing
  • • • a framework and set of tools for processing geographic data
  • 6-1 dissolving features to create neighborhoods and fire divisions and battalions
    • examine the dissolve field and other attributes
      • pairwise dissolve tool needs a dissolve field for combining block groups 
    • dissolve block groups to create neighborhoods
  • 6-2 extracting and clipping features for a study area
    • • tutorial is a workflow for creatinga study region from layers that have excessive features
    • use sselect by attributes to create a study area
      • study area is important when working geogrpically dense areas like NYC with streets and blocks
    •  use select by location to create study area block groups
• 6-3 merging water features
  • merge features
    • use merge geoprocessing tool to create one water feature class from 5 seperate classes
• 6-4 appending firehouses and police stations to ems facilities
  • append features
    • use append tool to append firehouses and police stations to already exisiting ems points 
• 6-5 intersecting features to determine streets in fire company zones
  • open tables to study attributes before intersecting
    • observing attribute tables of each feature class familiarizes you with attribuetes before intersecting features
  • intersect features
    • use pairwise intersect tool
  • summarize street length for fire companies
• 6-6 using union on neighborhoods and land use features
  • • union tool overlays geometry and attributes of 2 input polygon layers to generate new output polygon layer
  • open tables to study attributes
  • use union on features
  • calculate acreage
  • select and summarize residential land use areas for neighborhoods
• 6-7 using the tabulate intersection tool
  
  • study tracts and fire company polygons
  • use tabulate intersection to apportion the population of persons with disabiltes to fire companies

Chapter 4: This chapter covers the different ways to map density, and how they might apply to certain map displays more than others. The chapter discusses how density maps can be used to find specific patterns within an area. It explains how mapping different features can completely change a map. For example, Mitchell talks about mapping workers rather than businesses and shows an example. I would have thought that the two maps would have been pretty much the same, but the difference in what was mapped completely shifted the map. Mitchell covers the difference between shaded vs dot density. While I can understand the importance of dot density when comparing specific locations, I prefer shaded density. It is a bit less overwhelming and still displays the data. I also think that dot density can be a little misleading because it doesn’t show the exact locations where the density is higher. The dots are just evenly placed within an area, which can make it a little more confusing.  Additionally, dot density can be hard to differentiate when displaying lines of different boundaries because the dots can get lost within the lines. Mitchell also talks about the difference between density surfaces and density areas. While I understand what each is used for, I am still a little confused about how they are different from each other. The author goes on to explain how to calculate density. I understand this to an extent, but I think that I’d have to practice it myself before it fully sticks in my brain. Something that I think is super cool with GIS is how it layers data in order to compare it and add it into one larger set of data. There are so many small things that I wouldn’t think about when presenting density on a map. For example, Mitchell explains the importance of cell size, search radius, calculation method, and units. He explains how these small details can influence how fast a map processes, how detailed the data is, and how difficult or easy the map is to read. Something that I am still confused about, however, is the difference between areal and cell units. In the chapter, he shows an example with two maps, but I don’t see much of a difference between the two.

Chapter 5: This chapter covers how to map small, specific areas and find what falls within those areas. To isolate an area, it is easiest to draw an area map on top of the feature that you have already mapped. This can aid in comparing different areas on a map. In this chapter, Mitchell discusses discrete and continuous features. Something that is a little confusing about continuous features is that they change. I’m wondering if you can only include continuous features at a specific time, or if you have to keep updating the map over an extensive amount of time to see the pattern. Something that I thought was interesting was the different ways you can mark a certain area on the map. Mitchell talks about how you can just include the parts of a parcel within the specific area, and that you can highlight the parcel as a whole. Something else that I found interesting was how much of the work GIS can do for you. Throughout the chapter, Mitchell talks about the many calculations that you might need to do when creating overlays for the map, but he also talks about how easy GIS makes it for you by completing a lot of the other calculations. Mitchell also discusses the different ways to layer the map in order to display the result you want. He goes over the differences and benefits of putting a specific area either under or over the set of boundaries on the map. Another thing Mitchell mentions is the use of frequency in data. He provides examples for different ways to display frequency using charts instead of just maps. He also shows how to display the data using both charts and maps together. Something that I found a little bit confusing when reading about how lines are represented in areas is how they are split up when they fall into multiple areas. Mitchell touches on this briefly, but doesn’t go into depth on it. He talks about the GIS creating a new dataset for the line, which doesn’t make sense to me.

Chapter 6: This chapter covers how to map features within a set distance of a point. This chapter discusses how you can map travel and travel costs in a certain area. This concept is something that I understand to an extent; however, it is still a bit confusing to me. The chapter further explains this concept towards the end of the chapter, but it gets very complex. There are so many little components that go into estimating travel costs. For example, Mitchell brings up how the map creator might estimate the time that a turn takes, or a stop sign, or a light. I understand all of the little components, but it seems like entering all of that data would be very tedious and annoying. Mitchell also brings up the use of turntables to display to data, but that is also confusing to me since he doesn’t go much into depth about it. Another thing that this chapter discusses is inclusive rings. Mitchell shows displays of three different maps with inclusive rings of different sizes. Something that I’m curious about with this is if you have to completely remake the map for each ring, or if there is a way to essentially copy and paste the map so that it is more efficient. A tool that I think is super useful is creating a buffer of features within a certain distance. This allows you to highlight features without creating a border around them. Another way Mitchell talks about displaying data is through a spider diagram. I think that this diagram looks super cool, but I think it could only be used on a small scale. When used on a bigger scale, like he does in the chapter, many of the points blend together and make the diagram confusing. Something that I think is super cool and useful is displaying places within a certain travel cost distance of an area. This can be helpful for owners who might establish a business within that area and want to know how long it takes for their customers to travel to them.

Chapter 4:

Concepts & Definitions

• Mapping density: shows where the highest concentration of features is
• Defined area: mapping density graphically, using a dot map, or calculating a density value for each area
• Density surface: Created in GIS as a raster layer, each cell in layer has a density value
• Cell size: how coarse or fine patterns will appear, smaller cell size = smoother surface but more cells which will require more processing and storage space; larger cell size faster but more coarser surface and subtle patterns may not be noticed
• Search radius: larger search radius = more generalized patterns in the density surface
• Contour lines: connect points of equal density value on the surface

Notes

• Density maps are useful for looking at patterns more than locations of single features
• Density map shows the measure of number of features using a uniform aerial unit to clearly see distribution
• Mapping density useful for mapping areas like counties
• Dot maps can be an easy way to read a map if they are distributed throughout a defined area
• Density surface requires a lot of effort but gives the most detailed
• Density by a defined area is usually a shaded map, using multiple color shades
• Dot maps can give a quicker sense of density in a place
• Dots can be any amount of value 1 vs 100 vs 1000 units
• When creating a density surface, GIS will define a neighborhood around each cell center and then will automatically total the number of features that fall within it and assign that value to the cell
• Cells are square
• Converting density units to cell units: 1 sq. km = 1000m * 1000m = 1 million sq. m: 1 million sq. meters / 100 cells = 10,000 sq. meters per cell; sqrt 10,000 m = 100 m (one side of cell)
• GIS lets you specify the areal units for density values calculated, like square meters for wildlife animals
• Can display a density surface using graduated colors

 

Chapter 5:

Concepts & Definitions

• Single Area: A defined singular area to monitor activity/summarize information in it
• Multiple Areas: Like single area but looking at several of them to compare them
• Continuous values: numeric values that vary across a surface; temp, elevation, etc..
• Count: Total number of features inside an area; number of fast food in a county
• Frequency: Number of features with a given value/range of values, inside an area and displayed as a table, bar chart, or pie chart
• Sum: Overall total
• Average/Mean: Total numeric attribute divided by number of features
• Median: Value in middle of a range of values of an attribute
• Standard Deviation: Average amount values away from the mean

Notes

• Data should consider how many areas you have, and type of features inside them
• Discrete features unique and identifiable (like locations or crimes)
• Continuous features represent geographic phenomena
• Can use GIS to find out whether a feature is within an area
• Can create a boundary for linear features and discrete areas that may fall outside of a chosen area
• Can create a map showing the boundary of an area and features, good for seeing a few features inside/outside a single area; would just need data
• GIS can combine area and features to create a new layer with attributes to compare two layers
• Can symbolize locations or linear features with a single symbol or by category/quantity
• If mapping continuous data (soils or elevation), draw areas by category/quantity and then draw a boundary to highlight it
• Geographic selection is a way to find out which features are within a certain distance of another feature
• Overlaying areas and features can let you find which discrete features are within areas and summarize them
• GIS splits category/class boundaries where they cross areas and creates a new dataset within the areas that result
• Can use GIS to summarize the values and create a map/table of summary stats for each area

 

Chapter 6:

Concepts & Definitions

• Geodesic Method: Measuring distance using curvature of the earth
• Inclusive RIngs: Useful for finding how the total amount increases as the distance increases; like the total number of chicken diners within 1 mile versus 2 miles versus 10! Miles
• Distinct Bands: Used to compare distance to other characteristics, kind of like a range; number of beef stew shops between 1 miles and 2 miles
• Straight line distance: Can specify the source feature and distance and GIS will find the area/surrounding features within that distance
• Spider Diagram: GIS draws a line between each location and nearest source; useful for comparing patterns between multiple sources
• Graduated Symbols: Symbols used for comparing course features based on quantity; symbols = number of locations near a source feature

Notes

• Using GIS can tell you what’s occurring in a set distance of a feature and traveling range
• To find stuffy nearby, can measure using a straight line distance
• To define ‘nearby’, can be based on a set distance specified or travel to from feature to another; can also include travel cost
• Time would be an example of a cost, like going from a heavy traffic area to a store
• Effort could also be a type of travel cost; effort for a fish to swim upstream
• For measuring distance, have to consider if it’s a flat plane or use the curvature of the earth; small areas distances should be flat plane, larger should be done using the geodesic method
• Can specify the source locations and distance along a linear feature
• To create a buffer, specify source feature and then the buffer distance, GIS will draw a line and circle around the desired distance; can have different sizes of buffers around different features
• Can create distance ranges, each cell can have that unique value and can then display that value using colors
• Can create a boundary manually by drawing a line around selected segments or have GIS create the boundary; manually drawing boundaries give more flexibility but may take more time

Chapter4

This chapter explains what density maps are and how to create them. Density maps help me understand patterns rather than just the locations of individual features. We can map features using new data or previously summarized data, such as census tracts, counties, or forest districts. I was surprised that even when using the same data, the maps can look very different depending on whether they are density maps or other types of maps. Before reading this chapter, I thought density maps made with layers and different colors were easier to understand than those made with dots. However, after calculating density, I realized that dot density maps often work better because they make it easier to count and compare. I also learned that when creating a dot density map, it is important to specify how many features each dot represents and how large the dots are. Dot maps give the reader a quick sense of density in an area. Shaded maps, using a range of color shades, are also useful to understand which areas have the most or the least density. I was also surprised that some GIS software, such as ArcGIS, allows you to calculate density automatically. It is really cool! I was especially surprised by the textbook examples showing two maps with different cell sizes and two maps with different search radii. The differences in cell size and search radius make a big difference in how the maps look. Therefore, I think I should pay attention to cell size and search radius when creating density maps. Finally, I prefer when areas of higher value are shown using darker colors because it makes the maps easier to understand. I also wonder if there are cases where showing higher densities with lighter colors could actually make the map clearer. After looking into it, I found that this approach is sometimes used in night maps, aviation charts, maps designed for people with low vision, or certain types of visual design where the colors are intentionally inverted.

Chapter 5

This chapter explains how maps can help us see what is inside an area and compare it with other areas. Knowing what happens in an area by using maps helps people decide what actions to take. In order to find what is inside, we need to know the boundaries, check the features in an area, and analyze them. Before reading this chapter, I did not really understand the meaning of “finding what’s inside.” However, I fully understood it after seeing the map that shows calls to 911 within 1.5 miles of a fire station. The circle on the map, centered at a fire station, helps us see how to evacuate or reach hospitals in an emergency. The map shows this information at a glance. It also made me realize how important it is to know how many areas I have and what types of features are inside those areas. As the next step after mapping, I learned that I should think about what kind of information I need to analyze—such as a list, a count, or a summary—and how detailed the map should be. I think knowing these things helps make a map more efficient. When I make maps, I want to make sure who will use the map and what its purpose is. I also found it interesting that you can overlay another area on data that has already been summarized by area. This makes it possible to combine not only current data but also past datasets for further analysis, which I think is really exciting.

Chapter 6

This chapter explains that finding what’s nearby helps us decide how to measure nearness and what information we need for the analysis, which in turn determines which method to use. I didn’t know that when mapping what’s nearby based on travel, I can use not only distance but also cost, such as time and money. For example, if someone says a store is within 15 minutes from here, I might consider it close. But if you include factors like traffic jams, that perception can change. That’s why I used to think distance alone was what defined nearness. I also learned that inclusive rings are useful for showing how the total amount increases as the distance increases. I had only seen it used to show the relationship between the magnitude of an earthquake and the number of victims, but I learned that it can also be applied to various analyses such as commercial facility service areas, population distribution, and service coverage.  According to this  chapter, there are three ways of finding what’s nearby; straight-line distance, distance or cost over a network, cost over a surface. First, straight-line distance can create  a boundary or select  features at a set distance around a source well. Second, distance or cost over a network can find what’s within a travel distance or cost of a location over a fixed network well. Third, cost over a surface can calculate overland travel cost well. We can select one of them depending on what is surrounding features and  hot measure. WE need to specify the distance from the source and the GIS selects the surrounding features within the distance.

Mitchell Ch.4-6 300 Word Summary

 

Ch.4

This chapter focuses on mapping density, explaining why it is useful, how to decide what to map, and the two main ways of creating density maps. Density mapping helps identify and visualize areas where features are concentrated, making it easier to compare regions of different sizes by standardizing values to a common unit of area. This is especially helpful when working with large sets of features, such as crime incidents or businesses, because raw maps of locations can make patterns hard to see. For example, mapping burglary reports over time in different parts of a city can show where concentrations are highest and how they shift. Before making a density map, you should first decide which features you are mapping and what information you want the map to reveal, since this choice determines the method you use. The first method is mapping density for defined areas, which uses boundaries such as census tracts or neighborhoods. Within these areas, you can create dot density maps, where each dot represents a fixed number of features randomly placed to give a visual impression of concentration. Alternatively, you can calculate density by dividing the number of features by the area size and shading each unit accordingly. The second method is creating a density surface, which uses statistical methods to spread feature counts across space, producing a continuous surface that shows how concentrations rise and fall. This approach highlights hotspots and gradual variations without being limited to arbitrary boundaries. Each method has advantages: dot and calculated area maps are straightforward and good for comparisons between administrative units, while density surfaces provide a more detailed picture of distribution. Together, they show how density mapping is a powerful way to uncover spatial patterns and make more informed decisions.

 

 

Ch.5

This chapter was on mapping what’s inside a designated area. To map what’s inside you can use an area boundary to select features (kinda like click and drag I think). When mapping what’s inside the book says to consider how many areas are selected and what features they contain. Single areas let you monitor activity or information on the area. Mapping several areas at once lets you compare and contrast each area. Discrete features are unique, identifiable features kinda like landmarks where you can list, count, or assign a numeric attribute to them. Continuous features represent seamless geographic phenomena like topography or what an area’s composition is. The information within the analysis helps to identify the method to use such as a list, count,  or summary. What’s important is that you only list features that are within the borders of your area and nothing intersecting more areas. Drawing areas need a dataset to work and are good for finding how many features are inside or outside your area. Selecting the features helps you quantify and summarize features in an area but you need a dataset containing features and areas. Overlaying is for finding what features are in each area and is used for summary statistics. This method requires a dataset of areas and features. These 3 methods that measure specific things for an area and each have their own unique attributes and disadvantages. When making a map it’s key to decide what features are inside and outside an area. When making a map you can make features apparent by categorizing or quantifying them and then drawing the area on top in a thick line. When mapping several you should list them to make it easier for others to follow. No matter the method, always make sure that you know what features are inside your area, the method fits the prerogative, and that others can follow along. 

 

 

 

Ch.6

Defining what’s nearby helps you scope what features are within an area or set distance. Defining the analysis is done by measuring straight-line distance, measure distance or cost over a network, or measure cost over a surface. Measuring this is based on your definition of nearby and as a tip you can set a defined distance as a limiter to what you consider nearby. Identifying the information you need from the analysis is key. After establishing what the distance is from the source you wanted you can choose a list, count, or summary to a feature attribute. To find what’s nearby you can use a straight line distance you set. There are 3 ways to find what’s nearby strait-line distance, cost over a network, or cost over a surface. Strait line distance is done by setting the source feature and the distance you want to limit your scope too (GIS finds the area and the surrounding features within). Cost over a network allows you to specify the source locations and a distance to a feature linear of the source (GIS automatically finds what’s within these parameters). Cost over a surface allows you to specify the travel cost  and the source locations. GIS will then automatically create new layers for each source’s travel cost. Making a straight line distance is done by creating a buffer to define a boundary and likewise what’s inside it, calculate feature-to-feature distance to find and assign distance to locations near a source, or by selecting features to find features within a given distance. Creating a distance surface allows you to create a raster of continuous distances from the source. As quoted from the book “You can use the distance layer to create buffers at specific distances, and then assign distance to individual features surrounding the source or find how much of a continuous feature”. When it comes to scoping whats within a feature one thing is for sure mapping and setting what your distance will be and how you wish to carry it out matter.

Chapter 1

Chapter one gives a basic overview and an introduction of what GIS is, and how it is used. GIS is a process that looks at geographic patterns in data and relationships between features. This chapter lays out the steps to starting the GIS analysis process; frame the question, understand your data, choose a method, process the data, and look at the results. There are three types of features, discrete features, continuous phenomena and summarized by area. Discrete features are locations that can be directly pinpointed. Continuous phenomena is measured anywhere and covers entire areas, like temperature or rain. Lastly, features summarized by area represent numbers or density of an entire area. There are two ways to represent these three features, vector (typically used with discrete and data summarized by area) and raster (usually used with continuous phenomena). The chapter then goes over briefly how map projections distort shapes and measurements, and advises that small areas can ignore the distortion but it’s more of a concern when mapping larger areas like states or countries. There are five geographic attributes that help describe features, categories, ranks, counts, amounts, and ratios. Categories are groups of similar things that organize and make sense of the data, like categorizing roads as freeways or highways. Ranks are putting features in order, from high to low when measures are difficult or a combination of factors. Counts are numbers of features on a map and amounts represent any measurable amount of things associated with a feature. Ratios represent the relationship of two quantities by dividing one by the other. Lastly the chapter overviews how to use data tables that hold attribute values and summary statistics. There are three common operations, selecting, calculating, and summarizing. 

 

Chapter 2

Chapter two goes into detail about making a map using GIS, and what they could be 

used for. Mapping features can show patterns in the distribution of those features, and start to find the causes of those patterns. The chapter describes the process of making a map, starting with patterns from the data you collect and mapping those features with symbols. You have to think about the audience that will be viewing the map, adding reference locations to give context to the analysis or to make it more recognizable, and how the map will be presented, changing information presented based on size scale. Another thing it reminds you to do is make sure there are geographic coordinates assigned to the features you’re mapping. To map a single type of feature you would use the same symbol, and the GIS stores the locations of the features as a pair of coordinates that define its shape, so it can draw the features with the symbols you choose. To map by category, you would draw the features with a different symbol for the different category values, and the GIS stores each value for the features. You can also use many different categories to show other patterns in the data sets, but using more than 7 categories can make them much more difficult to see. The larger the area, the smaller number of categories would be beneficial and vice versa. Grouping a large number of categories can make it easier to see the patterns as well, as long as you’re specific about what the categories include. When choosing symbols, choose based on the type of feature. Individual locations would use a single marker in a color or shape for each category and linear features would get different variations of lines, and shaded or raster layers get different shades of the same color. 

 

Chapter 3

Chapter three describes mapping most and least values. You would use mapping most and least when you want to go beyond just mapping locations of features and give your audience more information about your data. You would map patterns of these features that have similar values, and the quantities associated with the three types of features discussed in chapter one, discrete, continuous phenomena, and data summarized by area. You assign symbols to these features based on their attribute (also discussed in chapter one, counts, amounts, ratios, and ranks), which contains a quantity. Counts and amounts show you total numbers, and lets you see values of the features. This is only a good method for discrete features and continuous phenomena because it would skew the patterns for summarizing by area. Ratios even out the differences between large and small areas, areas with many or few features, to give a more accurate distribution of said features. It’s very useful when using the summarizing by area type of feature. Ratios could be; averages, for comparing a place that has few features against one with many, proportions, showing part of a whole quantity and what it represents, and densities, for showing where features are concentrated. Ranks are useful when direct measures are difficult or for combinations of factors. The chapter also overviews how to create classes when using counts, amount and ratios, because each feature has a different value. There are four common standard classification schemes for grouping classes, natural breaks, quantile, equal interval, and standard deviation. Natural breaks, also known as Jenks, are based on natural groupings in your data and breaks where values jump. Similar values go in the same class. Quantile classes include an equal number of features in each class. Equal interval classes show the difference between the high and low values as the same for each class. And lastly, standard deviation classes are based on how much their values vary from the mean. The chapter then goes over how to compare each class, and the advantages and disadvantages for each one.