Category: Course Student Work

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.

 

 

Chapter 4- 

• Density maps are used for identifying patterns rather than showing the precise location of something. They are more useful for mapping areas.
• For a density map, densities for a specific area can be represented by different shading or a density surface. You can map the densities of points or lines. You can also map data that is summarized by area. 
• Density of features (number of businesses in an area) or feature values (like how many employees are at each business) can be mapped. 
• Density by defined area can be graphed using dots or density value. Calculating density value- divide the number of features by the area of the polygon.
• Density graphed by a density surface- Created as a raster layer. Every cell in that layer is assigned a density value, which can be found using the number of features within a radius of the cell. This type of graphing gives the most detail, but is the most difficult to make. 
• Which method should you use? 
  • Defined area- when you already have data already summarized by area or when you want to compare administrative or natural areas with defined borders.
  • Density surface- Shows the concentration of points or lines.
• Calculating a density value for defined areas-
  • “Add a new field to the feature data table to hold the density value.” (Mitchell, 2020)
  • “Then, assign the density values by dividing the value you’re mapping by the area of the polygon.” (Mitchell, 2020)
  • “If the density units are different from the area units, you’ll need to use a conversion factor in the calculation to change the area units to the density units.” (Mitchell, 2020)
• Creating a dot density map- 
  • “The GIS divides the value of the polygon by the amount represented by a dot to find out how many dots to draw in each area.” (Mitchell, 2020)
  • GIS places dots randomly within the area. The dots do not actually represent locations. 
• Cell size- determines how coarse patterns on your map will be. 
  • 1. Convert density units to cell units (1 sq. km = 1,000 m * 1,000 m = 1,000,000 sq. m)
  • 2. Divide by the number of cells(1,000,000 sq. meters / 100 cells = 10,000 sq. meters per cell)
  • 3. Take the square root to get the cell size (one side)
• Search Radius- the larger the search radius, the more generalized the patterns in the density surface will be. The smaller it is, the more local variation shown. 
• Contour lines- Contour lines connect points of equal density value on the surface.

Chapter 5-

• Finding what’s inside-
  • Single area- ex. Customers within a proposed sales territory or a service area around a central facility.
  • Several areas- ex. the number of businesses within a group of zip codes.
• Multiple areas- 
  • Contiguous- such as zip codes or water sheds.
  • Disjunct- such as state parks.
  • Nested- such as 50- and 100-year floodplains, or the area within 1, 2, and 3 miles of a store
• In your map, linear features and discrete areas may not all fall inside the set boundary of a map. You can choose to include features only fully in a designated area, only the parts that lie within the boundary, or what partially lies in a designated area. 
• 3 ways of finding what’s inside-
  • One- drawing areas and features. This method is good for seeing whether one or a few features are inside or outside a single area.
  • Two- Selecting the features inside the area. This method is good for getting a list or summary of features inside a single area or areas. It’s also good for finding what is in a certain radius of another feature. 
  • Three- Overlaying the areas and features. This method is good for finding which features are in each of several areas or finding out how much of something is in one or more areas.
• Drawing areas and features:
  • Locations and lines- draw them using a single symbol or symbolize them by category or quantity. After, draw the boundary of the area on top.
  • Discrete areas- (1) Shade the outer area with a light color and draw the boundaries of the area features on top. (2) Fill the outer area with a translucent color or a pattern on top of the discrete area boundaries. (3) Draw the outer area boundary with a thick line, and the discrete area boundaries with a thin line in a lighter shade or different color.
  • Continuous features- Draw the areas symbolized by category or quantity (as a class range), and then draw the boundary of the area or areas on top.
• Selecting features inside an area- 
  • specify the features and the area. 
  • GIS checks the location of each feature to see if it’s inside the area and flags the ones that are.
  • It then highlights the selected features on the map and selects the corresponding rows in the feature set’s data table. 
• The vector method- GIS splits category or class boundaries where they cross areas and creates a new dataset with the areas that result. Each new area has the attributes of both input layers.
• The rastor method- GIS compares each cell on the area layer to the corresponding cell on the layer containing the categories. It counts the number of cells of each category within each area, calculates the areal extent by multiplying the number of cells by the area of a cell, and presents the results in a table.

Chapter 6-

• Finding what’s nearby-
  • An area of influence is measured by a straight line distance.
  • Travel to or from a feature is measured using distance or travel cost.
  • Travel can be measured over a geometric network, such as streets or deer walking to a stream.
  • Can also measure nearness using time it takes to travel there (ie. through heavy traffic would warrant more time).
  • You can measure distance as the Earth being flat (planar method) or you can use the curvature of the Earth (geodesic method).
  • You can get distance information on several features, not just one. 
  • Inclusive rings are useful for finding out how the total amount increases as the distance increases.
  • Distinct bands are useful if you want to compare distance to other characteristics.
• Using straight line distance-
  • (1) Create a buffer to define a boundary and find what’s inside it.
  • (2) Select features to find features within a given distance.
  • (3) Calculate feature-to-feature distance to find and assign distance to locations near a source.
  • (4) Create a distance surface to calculate continuous distance from a source.
• Centers- Source locations in networks. Usually represent centers that people, goods, or services travel to or from.
• Geometric network- composed of edges, junctions (points where edges meet), and turns. To get accurate costs to travel through a junction, make sure that (1) edges are in the right place, (2) edges actually exist, (3) edges connect to other segments accurately, and (4) there are correct attributes for each edge.
• Impedance value- the cost to travel between the center and surrounding locations for a street segment.
• Creating a boundary-
  • List all individual locations.
  • Get a count of locations in the area covered by the selected segments.
  • Have data summarized by area. ie. you want to total the number of households per census block to find out how many households are within a 15-minute drive of a recycling center.
  • Get a list, count, or amount for linear features or areas, ie. the total length of salmon streams within a half-hour drive of the town.
• “You can limit the area for which the GIS calculates cost distance values by specifying a maximum cost. The GIS stops calculating cost distance when all cells within the specified cost have been assigned a value. Any remaining cells are not assigned a value on the output layer. If you don’t specify a maximum cost, the GIS calculates a value for all cells in the study area” (Mitchell, 2020). 

Chapter 1: This chapter helped inform me on the basic principles of GIS. It opened my eyes to how specific and precise some data sets must be in order to achieve the expected outcome. Initially, when using GIS, the chapter discusses the importance of framing the question that you would like to answer. Mitchell discusses the different methods that you can use to achieve your results, as well as different ways the results can be presented to suit your needs. He discusses that data can be presented very specifically or on a broader scale. Mitchell goes over how continuous data may be processed differently from other data. This was something that intrigued me because it can be related to weather maps showing precipitation and wind patterns. It was also interesting to learn that this data is not as exact as other data presented by GIS because the data is processed as it varies on the landscape, and creates models using data that are similar to each other to create groupings on the map. Further in the chapter, Mitchell also discussed the differences between vector and raster models. While I can identify the use for each model, I personally prefer the vector models because they are more exact and are displayed both in a map and within a table. Raster models are used to process continuous data. However, I think the continuous data is a little more difficult to understand and process. For example, issues can be run into when presenting raster data because of the pixel size, which can impact how easy the data is to interpret. Mitchel also discusses the process of overlaying certain parts of a map and its difficulties. I never would have thought about the difficulties that you might run into based on the size of the area you are trying to examine and the curvature of the globe.

Chapter 2: This chapter helped me to understand visual displays of a map and how important these displays can be when using GIS. Mitchell talks about the importance of certain patterns in maps and how they can apply differently to each map. This let me know that when planning to map something out, I have to be specific and very conscious of how I represent different data points. This also let me know that mapping can be a process of trial and error. Sometimes, if symbols or colors on a map are too simple than it can be confusing for the audience viewing that map. Additionally, the chapter taught me the importance of breaking a map down into subsets. Using subsets can help me break down all of the data into smaller groups and notice patterns among the smaller groups of data, rather than all of the data at once. On the other hand, something that I also have to keep in mind when creating these maps is that I don’t want them to be too clustered, so I need to keep in mind the scale of the map. For example, if I have a larger map, I can create more categories and boundaries without them being too clustered. However, on a smaller-scale map, the same number of boundaries or categories might be too complex and make the map difficult to read and decipher. In order to avoid this, I can group the categories together in a table to differentiate between a general and a specific category. I think that this can be very useful because it allows me to see both the complex data and more specific categories if I need to. It also allows me to see the categories grouped together, which makes them easier to read on a map. I think one of the easiest ways to do this, however, is to group the data together by symbol rather than code. This way, there is a distinct visual difference that I can identify with having to read all of the codes in the data. 

Chapter 3: This chapter went further into depth about the different types of visual displays for maps and charts. The chapter discussed the different types of maps and ways to analyze data, and how they can be suited based on the way the data is skewed. While this chapter went into a lot of detail about counts, amounts, and ways to display data, it was a little overwhelming. I think that all the data provided by the chapter was very useful, and something that I can look back on to help me in the future. It was a lot to look at all at once. I think that after being able to apply the different methods that the chapter discussed, they will become more memorable. However, trying to differentiate between them after reading about them is a challenge for me. Mitchell discusses the importance of ranks, ratios, and densities and how they can be applied to certain maps. He goes further into this discussion by talking about how this data can be grouped into classes. This is something that I think is super important because it highly influences how the data is presented and how easy it is to understand. If the data is too exact, it can make it harder to read; however, by grouping it into classes, the data is simplified for the viewer. Additionally, Mitchel goes over the different ways that data can be displayed on a map. While I understand the importance of the variety of displays, I think that some of them make it confusing to analyze the data. For example, while I think the 3D models are cool, I also think that they make it difficult to label and analyze data.

1. I have completed the GEOG291 quiz
2. My name is Elaina Massaro. I am currently a freshman and plan to double major in Environmental Science and Zoology. I enjoy working with animals, reading, and doing ceramics.
3. While reading the first chapter about GIS, I was very intrigued to learn about the depth of the program. The chapter informed me on the progression of GIS and opened my eyes to how complex it has become over the years. It was interesting to follow the process of different people and scientists collaborating to create a huge program. Originally, going into this class, I did not have any prior knowledge of GIS or how it worked, so following the history of the program was something new for me. I thought that it was super cool to see how so many data sets can be overlapped and interact with each other. I think that GIS is an amazing tool that can be used to study interactions of both biotic and abiotic factors. Seeing the variety of ways that GIS is used was very eye-opening. Some of the applications are things that I would have never thought to use the program for. For example, Schuurman discusses how GIS can be used to predict future events such as city expansion. This is not something I would ever think to use this program for, nor would I think it to be possible. I also thought that it was interesting to see the data described by the author in the form of maps. This makes the data much easier to understand and allows me to comprehend the extent of the work that the program is doing and how many factors go into it. One of my sources talks about the application of GIS in animal rescues. It talks about how they use GIS to estimate where more animals are regularly dumped, and what they need to do in order to accommodate that. 

https://www.aspcapro.org/resource/using-geographic-information-systems-gis-map-animal-data

Another one of my sources uses GIS for animal tracking, specifically wolves. They use a variety of maps to show the wolves’ movements throughout the span of multiple days.

https://storymaps.arcgis.com/stories/32412cf13731440582fe051cd360b009 

Mitchell Chapter – 4 is about mapping density. This was mainly shown as population per county/census tract, or businesses per area. Mapping density is useful because it can be a way to display ratio data using either graduated colors, points or contour lines to visualize patterns. As an ecologist it could be useful to look at density maps showing percent logging or percent population in a given area so that I can find good study sites. The chapter covers different ways to display density on a map, and the main two methods are through points or density surface. When planning to map density it is important to think about what it is you are planning to use your map for. If you are planning to just try and visualize a trend and aren’t doing a lot of analysis from the map otherwise, it is best suited to use a large cell size. This is because a large cell generalizes your data, however it also processes a lot faster. It can be refined further to have a smaller cell size if further analysis is required. 

 

Key words: Defined area density (density based on area of a polygon), Dot density (area mapped by count/amount and each dot is given an amount to represent), Cell size (amount of space that the GIS uses to represent data, smaller is more detailed but takes longer to process).

 

Mitchell Chapter – 5 is about what is actually represented by the map, and what it can be used to interpret. This can be done by designating an area surrounding a central point or by layering data on top of one another. This chapter also includes different ways to highlight different details about the map, such as by using just an outline of an area, outlining and shading an area, or by screening out the space around the area. Some applications for this that I can see, is by using census data to find out the amount of people living in poverty within a given area, this can be done by defining an area and than using graduated colors or symbols to show the count for the feature data within the defined space. A scientific application that I could use this for would be to map the land use types for a study area, say Delaware county, then I could define my specific study sites within the county and then use the land use map as a sort of base layer to determine what the land use type is for all of my given field sites. 

 

Key words: Single area (area surrounding a central point), Count (total number of features in an area), Frequency (number of features within a given value or range of values inside the area).

 

Mitchell Chapter – 6 is about determining distance from a source. This is often expressed as a cost, whether that cost be time, money, or physical distance. To do so you need to define your area, by selecting either a line, network, or a surface. Measuring distance can sometimes be difficult as the earth is curved and depending on the scale of your area it is sometimes necessary to use the geodesic method to account for the earth’s curvature. A few things that can be done by measuring distance or cost induce: generating lists of customers near a given business to give advertisements to, counting the amount of properties near a fire station, or generating summary statistics around your area. A good use of concepts from this chapter would be buffering an area surrounding a tributary with vegetation and planning this out by selecting the tributary and then creating a buffer a set distance away from the tributary. 

 

Key words: Inclusive rings (creates an area that is a specified distance away from a given point), Distance bands (similar to inclusive rings, but spanning distances incrementally), Straight line distance (the GIS defines an area based on a source feature and a given distance), Cost by Network (a travel cost is designated per each linear feature), Cost over a surface (Surface features of a specified area are given travel costs), Buffer (a zone is marked out a set distance away from a designated area), Spider diagram (lines are drawn connecting features back to a certain point they are close to)