Month: September 2023

Ch.1

Not everyone can just go into GIS, but you need to understand the proper tools and structure for your intended analysis. I think it is interesting how a lot of GIS users become advanced analysts, so that is another possible career path.

GIS Analysis: a process for observing geographic patterns in a set of data and at relationships between different features.

It is important that you understand your data and be able to find the proper way to develop it.

Important Steps to GIS Analysis

Frame the question > Understand your data > Choose a method > Process the data > Look at the results > Understanding geographic features

Geographic features are discrete, continuous phenomena, or summarized areas.

Discrete features: are discrete locations and lines, the actual location can be pinpointed. At any given spot, the feature is either present or not.

Continuous Phenomena: examples are precipitation or temperature and can be found or measured anywhere. You can determine a value at any given location. 

It is important to note that continuous data often starts out as a series of sample points, either regularly spaced or irregularly spaced.

Example of regular spaced: sampled elevation data

Example of irregularly spaced: weather stations

Interpolation: Where GIS can use sample points to assign values to the area between points.

Sometimes non continuous data can be treated as continuous in order to create maps showing how a quantity varies across the place. Continuous data can also be represented by areas enclosed by boundaries-if everything inside the boundary is the same type- such as a type of soil or vegetation. 

Important Note: “If the features aren’t tagged with the codes for the areas by which you want to summarize them, the GIS lets you overlay the areas with the features to identify which ones lie within each area and to tag them with the appropriate code”.

Vector and raster are the two ways geographic features can be shown in GIS. It is important to use the right size when dealing with these models. Continuous categories are represented by vector or raster models. Continuous numeric values use raster models only.

Geographic features have specific attributes that go with them.

Examples: categories, ranks, counts, amounts, ratios

Categories are groups of similar things. (not continuous)

Ranks put features in order, from high to low. (not continuous)

Counts and amounts show you total numbers. (are continuous)

Ratios show you the relationship between two quantities and are created by dividing one quantity by another for each feature. (are continuous)

Important: working with tables that contain the attribute values and summary stats is a vital part of GIS analysis. Three common operations you perform on features and values within tables are SELECTING, CALCULATING, and SUMMARIZING.

Select attribute= value

Select Landuse= com

Select Landuse= com and acres > 2

CH.2

A lot of people use maps, use them to see where, or what, an individual feature is. Patterns are often seen. Individual features vs distribution of features. 

GIS can tell police officers where to assign patrols based on crimes that occur.

Step 1: Need to decide what to map

• Decide what features to display

Step 2: What info do you need from the analysis?

• Might need to know where features are or are not? The question. Patterns.

Step 3: How will you use the map?

• Appropriate audience
• Make sure issue is being addressed
• Make sure to add just  the right amount of info(no unnecessary details)

Step 4: Preparing your data

• Make sure the features you’re mapping have geographic coordinates assigned and, optionally, have a category attribute with a value for each feature before mapping

Step 5: Assigning geographic coordinates

• Each feature needs a location in geographic coordinates

Step 6: Assigning category values

• Each feature must have a code that identifies its type, when your map FEATURES BY TYPE, example is whether a crime is burglary or assault
• In some cases, a single code indicates both the major type and subtype

Step 7: Making your map(finally!)

• Tell GIS which features you want to display and what symbols to use to draw them
• You can do this by creating a layer for either single type or categories

Mapping a single type

• Must draw all features using the same symbol

You can map all features in a data layer or a subset you’ve selected based on a category value.

Using a subset could reveal patterns that aren’t always apparent.

Step 8: Mapping by category

• You can understand how a place functions when mapping by a category(roads like freeway and highway)

How many categories? Want to display no more than seven categories and grouping them could make it easier to understand/distinguish. Example: 18 categories grouped into 5

Just know that GIS is very complicated, complex and delicate

You can group categories in several different ways.

1. Assign a general code to each record in the database
2. Create a linked table to match detailed codes with general codes
3. Assign categories on the fly by specifying symbols

Choosing symbols: make sure the symbols you choose are chosen carefully(combination of color and shape)

The map you create will be more understandable if you display recognizable symbols. Include a map reference if you think there is a chance that people won’t get it.

Step 9: Analyzing geographic patterns

• Pretty self explanatory(look for patterns)

CH.3

Why map the most and least?

• Lets you compare places based on quantities, so you can see which places meet your criteria

In order to do this… your map features must be based on quantity associated with each.

Mapping features based on quantities adds an additional level of info beyond simply mapping the locations of features. 

To map?

• Need to know the type of feature
• Know the purpose of your map

You can map quantities associated with discrete features, continuous phenomena, or data summarized by the area.

Locations can be dotes

Lines can be rivers

You might want to present your map in a specific way, but must explore the data first.

Knowing quantities(counts and amounts), will be important or could be when presenting your map. You can map counts and amounts for discrete figures. 

• Might need to summarize by area’

Might need to show ratios to get your point across…averages are good and so are proportions.

Proportions are often presented ias percentages. Densities show you where features are concentrated. 

Ranks: poor-fair-good-excellent or 1-8

Once you’ve determined the type of quantities, need to decide how to best represent them on the map.

Might need to make trade-offs when doing this.

Mapping individual values could be very important in order to present a more accurate picture.

You will need to keep in mind classes and how to create them manually.

The four most common schemes are natural breaks, quantile, equal interval, and standard deviation. GIS can compare these different classification schemes.

Good: mapping data values that are not evenly distributed

Bad: difficult to compare the map with other maps.

There are pros and cons to each different common scheme.

You might need to deal with outliers in your data, so know how to deal with them.

Note: might have to decide how many classes to include and make them easier to read using GIS

Towards the end of chapter 3. Talks more into detail about features and details about maps.

Might need to use charts, contour lines to map data. 

GIS can create 3D perspective views! How awesome!

1.      GIS Analysis can be defined as looking for geographic patterns in data that is found and is also used at looking at patterns in relationships between features. The process can be described in a few short steps. First, frame the question, understand the data, choose a method for how you will get said data, process the data, and finally look at the results. There’s different types of geographic features and it’s important to understand when dealing with mapping. 

• Discrete features- I think they gave an unclear definition for this but I got another definition from Esri. It’s defined as discontinuous but has very defined features. 
• Continuous Phenomena- An example of this is precipitation and it can be measured anywhere
• Summarized by area- Represents the density of singular features within a certain boundary or area. 

When learning how to map, it’s important to understand the geographic features and their attributes. 

• Categories- Groups of things that are alike. Example, categorize roads as highways, alleys, or etc. 
• Ranks- Puts things in order specifically from high to low. To put this into context of geographical measures, it may be hard to find a direct measure. An example they gave is assigning soil as all the same suitability for a plant. 
• Counts and Amounts are grouped into the same category and are both defined as it shows you total numbers. They are then specifically defined. A count can be defined as the actual number of features on a map. An amount is any measurable quantity that is associated with a feature. 
• Ratios- the relationship between two quantities and are created by division. One quantity is divided by another for each feature. It’s like taking an average. 

You also have to work with date tables when learning GIS. There’s a lot to GIS. Using data tables you have to learn selecting, calculating, and summarizing. 

2.       Chapter two is all about mapping. You have to decide what to map, obviously, before you start going crazy. Much like writing a book or article, your focus has to be right and you have to make sure you’re reaching your target audience. The mapping also has to be well organized and relatively easy to follow just like a book, it has to make sense. Kind of off topic but I’ve seen people on TikTok trace a pile of Rice to make a country and they make different features on the map and I thought that was really cool. Anyway, to make a proper map you need to make sure the features you map have geographic coordinates assigned. This means including the latitude and longitude of each mark. To make the map easier to read, you need symbols for different attributes. This makes it easier to see patterns. GIS does the work for you when trying to map something out. Its job is to use the coordinates to draw the attributes or features using the symbol of your choice. You can layer data and then select a specific thing you want to see by itself instead of seeing every feature together. This is very useful when you want to find specific patterns. This can be used in Apple Maps when you’re looking for close attractions but only want to see restaurants. GIS is widely used around the world, but I think not everybody knows the proper name for geographic information systems. When mapping categories, they suggest limiting it to 7. This is because the map could potentially become hard and confusing to look at. The scale matters when mapping these categories because the said features are spread out, then you would be able to map more categories without making it hard to understand. 

3.      This chapter is called the “Most and Least”. They phrase it as mapping the most and the least places lets us compare things based on the quantity. I thought this was a bit weird at first because I think mapping everything would be the most accurate. Maybe it would be the most accurate but mapping everything makes it harder to understand and could make the map convoluted. This chapter talks about how you have to keep one focus on your map and keep the intended audience in mind. I already stated this in the above chapter and said how making a map is kind of like writing an article. You have to keep the map purpose from drifting off. This chapter seems like a review. Quantities can be counts or amounts and knowing the difference will help you best pick which one to use for a map.  

     A new idea they introduce is density. When densities show in a map it shows where those features are most concentrated. The chapter starts going into data like statistics. I know this is important to GIS but I hate math. Matter of fact, I took stats during COVID so I actually learned nothing. What I know about stats is that I hate everything about it including standard deviation. The easy thing to understand about stats is that some data may have outliers. This means that there will be points that lie way outside of the average points. This could then skew the data either left or right. There’s different ways to label and create a map including graduate symbols, colors, contours, charts, and 3D viewing. Graduated symbols show a range of values. Charts show categories and quantities. These are for discrete areas. You can use pie charts and bar graphs to show data as well but I think we all know that. 

Chapter 1: Introducing GIS Analysis

Some comments, notes, and questions I had while reading this chapter were plentiful. It was interesting to find out that spatial analysis is working to bring attention to real-world problems and issues people face. GIS is growing rapidly and we are finding more and more uses for its capabilities. GIS analysis is using models of the real world and looking at the geographical patterns. There are 3 types of geographical features: discrete, continuous phenomena, or summarized by area. Discrete locations and lines are actual spots found and can be easily read line buildings or bodies of water. Continuous phenomena are going to span the whole map you make because it shows weather patterns like rainfall or temperature. Interpolation is when GIS gives values to points irregularly or regularly spaced on a map. Geographical features summarized by area could be the number of households in each county, or business zip code. Reading about the two types of GIS models vector and raster I am a little confused. A vector model is a map that shows a point and is labeled using x and y coordinates mainly used to show small details. The raster model is for larger spaces that need to be analyzed and is not good if looking for precise measurements. I thought it was interesting to find out that map projects get distorted because of the curvature of the earth. So when looking at a large section of Earth it will relatively not be 100% accurate. After reading about ranks and counts and amounts for features that are mapped it gives me an idea why different levels are color-coded and categorized for their particular purpose. How will summarizing the values of a large population help us to find an average? Will data tables help to sort out ranks for a map that needs to be created? 

 

Chapter 2: Mapping Where Things Are

Why map where things are? To see patterns that may need another perspective besides being in a table or literature. What is the difference between distribution features and individual features? Distribution features are patterns and many professionals use GIS to target areas they are in need of their assistance. Deciding what to map? Is a process since you want a map to be easily read and understood and to correctly convey information to readers. Learning how to create features to be layered over other features to display different years or a pattern of change. Being detailed on either category or doing a broad overview to minimize distractions for the intended audience. Preparing data needs to be organized before starting a map and to have all features with a location. How will geographic coordinates be assigned to the GIS database? Will we be learning how to code features or will the software we use already be coded? It was neat to find out that we just told the GIS what features and symbols we wanted to be shown. Mapping a single type may or may not be able to show patterns but for a single type, all the same symbols will be used. What is the main idea behind GIS? It has many capabilities for setting coordinate pairs and storing the locations, symbols, and streets. Using a subset of features to layer data was neat to show the relationship between two completely different categories. 

 

Chapter 3: Mapping the most and least

To find a place to map you should look for areas with equal quantities of an abundance of species and, or a place with a decline or decrease in species. It’s all about finding information that will be helpful to us in the future. What we need to map is finding patterns with similar values with quantities that should be studied. The features we will be focusing on mapping will be centered around discrete and linear features. The continuous phenomena are areas that cover. When looking at a map or creating one the dark areas will be greater in value from lighter shades. All ranks will be coded in this way so that the more pigmented areas will be darker or have more than light colors. A map that shows the longest salmon runs were categorized and coded on a map and from the pattern it was determined that the counties will longer runs have watersheds. The difference between presenting or doing research with a map will be set up in two separate ways. If presenting a map it will need to be simple to show a pattern and a more generalized view. If doing research on a map it can be detailed and have lots of different features.  Business can be mapped by the number of employees and the size of circles will be used to show employment numbers. For a larger area say the whole state of Ohio using counts and amounts would be the most beneficial and it will be rounded numbers. Block groups vary in size so a rough guess would be sufficient.  Using ratios to show the distribution of features with averages, proportions, and densities. 

Chapter 1: GIS itself is the process of looking at geographical patterns in your data and at the relationship between the features within said data.

Start by framing your question: This can typically start off as a question, and being as specific as possible about the question you are asking will help with deciding the best method to approach it with.  Understanding your data can also aid in making things more clear and narrowing down what method you should use. Finish by looking at your results and deciding if the data is relevant/useful, or if you should use a different approach.

There are multiple kinds of features in GIS: For discrete locations and lines, the actual location can be pinpointed, and the feature is either present or not. Continuous phenomena like precipitation can be found or measured anywhere. Summarized data represent the counts or density of individual features within area boundaries.

Vector and Raster: With the vector model, each feature is a row in a table, and feature shapes are defined by x, and y locations in space. With the raster model, features are represented as a matrix of cells in continuous space.

Types of attribute styles: Categories, ranks, counts, amounts, ratios

The only thing I worry about from this chapter is coming up with my own question. There are so many different topics with so many different subtopics, and the possibilities are so open that it’s almost overwhelming.

Chapter 2: Prepping your data involves ensuring that the features you are mapping have geographic coordinates assigned and have a category attribute with a value for each feature. If you are bringing data from another program or entering it by hand, the features will need to have location information like a street address or latitude-longitude, and GIS will assign the coordinates.

To make your own map, you’ll tell GIS which features you want to display and what symbols to use to draw them. Mapping a single type involves drawing all features with the same symbol, while mapping by category involves using a different symbol for each category. If you use the method with multiple categories, you shouldn’t use more than seven categories, otherwise, you will have to group categories.

Along with symbols, text labels can also be used to help distinguish categories (e.g. OW = Open water)

Chapter 3: This chapter continues to discuss different methods of displaying data, as well as how they should be understood. It seems like the best method for display varies between the project and what its purpose is.

Natural breaks: Data is not evenly distributed

Quartile: Data is evenly distributed

Proportion: part of the whole

Rank: high, medium, low

Density: concentration of data/feature

I found all three chapters helpful in terms of explaining the basics of GIS. There are pictures to illustrate every point that is made, which is super helpful for me, as I have always needed some kind of visual or example to understand any concept.

GIS ch1

 

This first chapter broke down the basics of GIS. What to use it for, how to use it, and which options are best for depicting different types of data. A big part of this chapter was the introduction of vector models and raster models. Vector models are often coordinates and lines that are the summary of data tables. Vector models are especially useful for discrete data which are values more specific than the alternative continuous values. On the other hand, the raster model is more useful for continuous numerical values. Raster models are depicted as cells that can be combined side by side with other cells to show how the data connects or overlaps. Layers are more prominent and used more often in these models. Another key difference between raster and vector models is that raster is more scale sensitive. Distortion can happen in all models and all scales but it is most significant in raster models. To counter this, you can find the appropriate sale from the original scale and the minimum map unit. Between these two models, continuous categorical values can be used and seen in either. This also brings up the continuous phenomena. The continuous phenomena describes how certain analytical values can be found or measured anywhere. 

Another important factor of GIS is layering. This chapter gave some good information on how overlaps can make tags for pieces of information which can then be used for layering. 

Towards the end of the chapter, categories, ranks, counts, and ratios show up. These are all attribute values that are important factors in GIS. Categories are values with a common aspect. Ranks are orders assigned to categories. Counts are total numbers. Ratios show the relationship between two or more categories. Categories and ranks are noncontinuous values because there can be the same value while counts and ratios are continuous values because they are completely unique. 

 

GIS ch2 

 

In this chapter, more of the mapping mechanics were thrown out there. Things such as category classifications, scales, and vector and raster models were revisited along with the addition of the use of subsets, grouping, zooming options, and colors or shapes of a map. From all of these other factors, chapter two explains the change of patterns. Patterns can be much more recognizable when you use a distribution of data instead of more individual sizes for the map. Using subsets can bring more detail to certain categories which could also bring out some unseen patterns. Similarly, zooming in or out can show us new things based on the original map scale like discussed in chapter one. For the sake of clarity, many large scale maps don’t use shapes for location points because with so many points it may be hard to recognize the shapes in clusters. In smaller scale maps, more colors and related categories can be used because there is less area to focus on so it will add detail without subtracting clarity. For this reason it is suggested to use no more than seven categories on large scale maps but if there are more, there is the option of grouping. This sometimes jeopardizes important information for the sake of clarity but can even emphasize already existing patterns that were not as prominent. 

Chapter two made me excited to start thinking about ideas for my own GIS maps. With all of the examples being featured along with the first look into how we will be doing this unfamiliar task, my mind is stirring. A lot of these examples were crime based and from seeing all of them I feel like I have a pretty good basic understanding of crime patterns shown here. This gives me a sort of reference point for how I want anyone viewing maps that I may make to see them. Aiming for clarity along with detail and distinguishable patterns. 

 

GIS ch3

 

Chapter three is about being able to understand what you’re putting in a map and what purpose each feature has. This chapter also mentions these factors from an audience perspective along with an exploratory perspective. Either way, you start with determining certain types of quantities like the previously mentioned ranks, counts, and ratios. This time, there is an addition of averages, proportions, and densities used to present gathered data. Averages are used when there are not a lot of features in one area and a lot in another area and you need to find a connection between the two. Proportion is used to find part of a larger whole or break down a large scale into a smaller scale. Densities are used sizes in an area that have a lot of variety. Another important strand of terms is the ones used for creating classes. Natural breaks, quartile, equal intervals, and standard deviation. Natural breaks are classified by jumps in the raw data and are useful when data is not evenly distributed. Quartiles are classified by similarities in numbers of features (low to high) and are good for data that is evenly spread. Equal intervals are classified with even amounts of highs and lows. It is simple to understand and good for continuous data. Standard deviation is classified by distance from the mean which makes it good for comparing values to an average. 

Other useful pieces of information in this chapter were what to do with outliers depending on the type of map you use and the types of features. Also, the use of raw data is interesting because a lot of times raw data is good to look at for lots of detail, but it does get overwhelming if presented to the audience who may not have as much previous knowledge on the topic represented in the map as whoever collected or used the raw data. 

I found the section providing examples of all the map features and their advantages and disadvantages helpful because it pulled all of the beginning chapters together in a visual way. It also just summarizes a lot of the past chapters so I think I’ll be referring back to those pages later on in this course.