Geography 291: Geospatial Analysis with Desktop GIS

This was the first time I have used ArcGIS and it was very easy for me to navigate. I found the tutorials extremely useful and easy to follow. There were some points where I did skip as I could not locate what they were asking of me, so that will most likely have to be something I look back on incase it is useful for future assignments. Overall I had fun exploring ArcGIS and seeing the cool features it has. My favorite is the 3d models they look really cool and I like also how you are able to explore with your mouse. Below I have attached some photos I took from each chapter (1,2 and 3).

Chapter 4, “Mapping Density,” got me thinking about how we present information, especially when you’re dealing with varying areas. It’s not enough to just count things up, sometimes we need a bit more context, like how spread out something is. I think that’s why the book dives into density mapping, the distribution of features or values per unit area. There’s a difference between saying “there are 100 houses in this neighborhood” and “there are 100 houses per square mile in this neighborhood.” What I found particularly interesting was how you can map density in a few different ways. The book highlights two methods: mapping density for defined areas, like census tracts, which is where you calculate density within existing boundaries. Then, there’s creating a density surface, which involves creating a continuous surface that shows how density changes across an entire area, even without defined boundaries. It’s like taking something that’s usually summarized by area, and making it a landscape you can view. It seems to me that these two methods would be used for different purposes and I’m wondering which gets used for what application and why? Mapping density is about more than just visualizing data. It’s about taking into account the context of the data and how it’s distributed. It’s a really valuable tool to see the patterns that might be hidden at first glance. I wonder if density mapping could be applied to research in neuroscience, since it deals with location based data sometimes.

Chapter 5 seems to build off the idea that location matters, but in a different way. Now instead of looking at where things are, we’re looking at what things are contained by. The book’s main question here is, Why map what’s inside? and, in my understanding, it’s because it allows us to explore relationships between features. The book outlines three approaches for this: drawing areas and features (where we manually create areas to select features), selecting features inside an area (where we use existing boundaries), and overlaying areas and features, which sounds like the most complex one. The overlay method, as I understand it, combines two layers of features to see how they interact spatially. I’m starting to think this is where GIS really shines because it creates new relationships between features that wouldn’t exist in the real world otherwise. I’m curious how you all go about choosing which of these three methods to use? I imagine that using drawn areas is more appropriate when you need to be more precise with your selection, and that using existing boundaries is better for broader analysis. How do you decide which features to overlay, if that makes sense?

Chapter 6 seems to add a layer of complexity to our spatial analysis by focusing on closeness. I think the main idea is that we can learn a lot from the distance and relationships between features. The book asks, Why map what’s nearby?, and the answer I think is that it allows us to explore how features interact, or how they might influence one another. Three ways of exploring this include using straight line distance, measuring distance or cost over a network, and calculating cost over a geographic surface. It seems like the first one is the most basic, just measuring distance as the crow flies, so to speak, while the second one takes into account that movement is often confined to networks, like roads. The last one, where you calculate cost over a geographic surface, is a bit more abstract, where you take into account the “cost” of travel, which is interesting. I’m realizing that “cost” doesn’t always mean money, and that different types of cost can be included in research. It seems to me that GIS is very useful for understanding and calculating all these different types of distance. I’m also thinking about the different applications for these analyses. It seems that you could use straight line distance to do quick analyses, or when the network isn’t important. You could use network analyses to find optimal routes, and surface analysis to calculate the cost of travelling across different topographies. I am wondering if there are times when you would use a network analysis to find straight line distance, or is that redundant?

Chapter 4

Often, GIS can be used to map density as a manner in which to clearly represent areas of highest concentration- such as with feral cats or oversized rats- for the purpose of seeking out various patterns. For example, oversized rat phenomena could be located in large urban centers due to the access of food, adaptations, etc, but maybe their oversized rats can be found in a rural area that is coincidentally near a nuclear waste site. Things such as the United States Census records data that can be used for density mapping for population, income, family size, etc. There are also two different options of representing density as density of features or feature values. As an example, density of features would show the areas with oversized rats while feature values would show the number of oversized rates in the areas. In addition, there are two manners of mapping density either by area or by surface, each with their own uses and drawbacks depending on the type of data you have and what you want to do with it. Some data can be best represented using individual points while other data is best represented using shades of color. For example, for relatively isolated incidents of the oversized rat phenomena, a dotted map could be used to examine relationships between rat outbreaks. However, if incidents of oversized rats become a prevalently explored and recorded phenomena, then a shaded map would become better suited to facilitate pattern recognition. GIS has the computing power to be able to make calculations with density data to generalize and present density data in a manner conducive for seeking out patterns within the data.

Chapter 5

When mapping a subject within the boundaries of an area, patterns within the area may be assessed or internal patterns can be compared to the patterns within other areas. Using feral cats as an example, feral cat information could be tracked with Delaware City’s Township and compared with other townships- within or outside of the county- to track patterns in where feral cats are the most prevalent. With that information, trap, neuter, vaccinate, and release programs can be sent out to places with the most need in order to manage cat populations. This information could also be used to find cats or kittens that could possibly be integrated into human households. The power of GIS allows users to use pre-established boundaries- i.e. counties, townships, zip codes, etc., or created boundaries to assess data depending on the intended span and use for the data. Determining whether the features are continuous or discrete is important when mapping data within. Discrete data would be the number of feral cat colonies whereas continuous data encompasses things such as elevation, vegetation types, temperatures, precipitation, etc. that are continuously present. GIS can be used for lists of features, the numbers of features, and for summaries, and GIS can be used to cut off certain data that is outside of a drawn boundary. From that point, there are three methods of finding what’s inside: drawing boundaries to show features outside and within, specifying an area, or to create a new layer that overlays the original. Each method, like the others, has its own uses depending on your goals for your data and analysis. Summaries for numerical data can also be implemented such as sum, mean, median, and standard deviation depending on the relevance of each for the best representation of data. To best express the need for managing feral cat populations within the city of Delaware,  I may want to use the sum of cats or average number of feral cats per square mile to stress the gravity of the problem. 

Chapter 6

As a counterpart to Chapter 5’s finding what’s inside is Chapter 6’s finding what’s nearby within a certain distance or range. An example of using range would be notifying people within a ten-mile radius of a colony of rabid bats. This can be used for distance but also cost through time, money, or effort. Maybe I wanted to let everyone know about the rabid bats that are a breezy five-minute walk away from their community park. With this knowledge of the proximity of rabid bats, I could better raise community awareness and issue warnings for pets, children, and nighttime park-dwellers to be wary of the rabid bats and remind them of the mortality rate of rabies. An alternative would be to determine the travelling pattern of my rabid bats to best alarm those in the path of rabidity. GIS has the power to draw my ten-mile bat radius and measure my five-minute walking time. Depending on my goals for rabid bat analysis, I could calculate distance if the Earth was flat, using the planar method, or incorporate the Earth’s curvature, using the geodesic method. The planar method would be ideal for a smaller area of interest- a city, county, or state, but the geodesic method would be necessary for any larger analyses- such as if my rabid bats spread from my little town to all contiguous US states. Just as with mapping what’s inside, data can be represented using lists, counts, or summaries depending on need. I would like a list of the addresses within a ten-mile radius of my rabid bat colony to ensure their awareness of the bats and to ensure that they have not yet gone rabid. In the aftermath, I would perhaps seek a count of the rabies cases in a post-rabid bat town, and perhaps I would seek statistics or graphs to easily review the impact of my rabid bats on pets, children, and those nighttime park-dwellers. The three manners of finding what’s nearby include straight-line distance- such as a ten-mile radius, distance or cost over a network- such as sidewalks, and cost over a surface- such as for the travel cost to reach my rabid bats.

Chapter 4

Chapter 4 “Mapping Density” explains how mapping density allows you to see “patterns of where things are concentrated”. While some maps emphasize specific locations of features, Density maps focus on the patterns of certain features. I thought this was interesting because in the previous chapters we were looking at specific features, like individual crime scenes. With a density map it is more of a broad way of showing where for example the most crimes occur, and through this type of map you can see patterns of where the most crime is located or where there isn’t as much of a concentration of crimes occurring. Density maps can provide you with a density measurement per area. Raster layers are used to create density surfaces. Based on the reading the raster layers allow us to see concentrated features. I think if I’m understanding this correctly, you can have a cell of a map and if you take a radius around that cell you can figure out the  amount of features within that radius and that number is assigned to the cell and after you do that with all of the cells that is where you get your smoothed area of concentrations. Search radius can be large or small. Larger search radiuses show more generalized patterns, while smaller search radiuses show more local variation.

Chapter 5

I am having trouble understanding how all of these maps are different. I think they are different based on the features and also how the features are being analyzed. This chapter seems to be talking about what is going on in an area. You can monitor what’s happening this way, or you can use this information to compare different areas based on what is happening to them. They give the example of potentially mapping the affected area of a toxic plume. If not for this information appropriate action could be taken by the public or those with the ability to handle the situation. You can show boundaries of certain things such as buffers around streams, soil types in a parcel of land, and floodplains. I think that this is an interesting thing that I haven’t thought about before while looking at maps. On the topic of discrete or continuous features, discrete features are features that you can easily identify and they are unique. They are locations, addresses, crimes, etc. With continuous features you can summarize the features for each area.

Chapter6

In the chapter, Finding What’s Nearby, I learned that you can set distances and you can figure out what is going on within these distances of the certain feature you are looking at. You can label the nearness of a feature using distance or travel cost. I have gathered that it is important to know what information you will need because that will help you choose the best way to carry out your analysis. There are three ways of finding out what is nearby. Straight line distance, distance or cost over a network or, cost over a surface. Straight line distance is what you choose a specific source feature and the distance and then the area is found within the distance that you specified. When it comes to layers you need the source feature and then a layer with the distance to form what you are desiring. Straight line distance allows you to create boundaries around a source.  With Distance or cost over a network, I see this as when you put an address in your google maps and it shows you all of the routes and which one is the fastest. At least this is what I have gathered from the reading. Cost over a surface is when you have locations of source features and a travel cost. Adn from that “ “The GIS creates a new layer showing the travel cost from each source feature.” Cost over surface is more for overland travel, while cost or distance over a network is if you are traveling in fixed infrastructure. And for straight line distance this would be used for estimates of travel range.

 

Chapter 4 focuses on the importance of file geodatabases (FGDBs) in GIS, particularly for efficient storage, organization, and querying of spatial data in ArcGIS Pro. Before reading this chapter, I didnt’ have a full grasp of the significance of FGDBs. However, I quickly learned that spatial data in GIS has geographic features, making it more complex than simple tables. FGDBs allow for fast queries and spatial relationships, which reminds me of databases in bioinformatics. One of the most valuable insights was that FGDBs allow the storage of multiple layers efficiently, and they provide better performance compared to traditional shapefiles. I now understand how much more flexible and organized FGDBs are, especially for large datasets. If I see a .gdb folder, I now know it holds many classes, raster datasets, and tables. The chapter also provided hands-on tutorials, such as how to create geodatabases and import shapefiles. One of the tutorials involved summarizing crime incidents by neighborhood, which I found particularly interesting from a Data Analytics perspective. Additionally, I found Python’s integration with ArcGIS valuable and would love to explore how it can automate geodatabase tasks. Some lingering questions include whether FGDBs have limitations for large environmental datasets, which I hope to explore further.

Chapter 5 deepens my understanding of how maps can reveal hidden patterns and serve as powerful tools for decision-making. Before this chapter, I hadn’t fully appreciated the significance of mapping areas and how it can uncover insights that raw data tables cannot easily reveal. For example, maps can show where the highest crime rates are in a city or identify hospitals within a 5-mile radius of schools. By condensing and summarizing complex data, GIS helps make sense of vast amounts of information. I was already familiar with latitude and longitude but had no prior knowledge about map projections and coordinate systems, which initially felt like learning a new language. I learned how selecting the wrong projection could misalign datasets and severely distort analysis, which made me realize the importance of choosing the right projection for accurate results. Additionally, the chapter introduced vector and raster data, which I found especially intriguing. I related raster data to microscope images, where grids and pixels have different intensities. The tutorial demonstrated how critical it is to align datasets properly using the correct coordinate system to avoid errors. Surprisingly, I enjoyed working with coordinate systems, even though I had struggled with geometry in high school. Real-world applications of GIS, like the ability to analyze geographic data for decision-making, made me appreciate its power even more. I also learned about spatial data interoperability, which refers to how different datasets can work together seamlessly. My lingering question is about how datum transformation might affect the precision of GIS analyses.

Chapter 6 emphasizes the importance of proximity and spatial relationships for decision-making, such as finding the nearest hospital or analyzing wildfire risks. I appreciated how practical the chapter was, showcasing how GIS tools can automate workflows and solve real-world problems efficiently. One of the key takeaways was learning about geoprocessing tools, such as dissolving features to merge school districts into larger regions, clipping data, and merging datasets—tasks that are essential for handling large environmental datasets. What stood out to me was spatial intersections, where two datasets are overlaid, and the affected area is extracted. This concept was mind-blowing and made me realize how powerful GIS is for analyzing spatial relationships. A significant real-world application I found particularly fascinating was how emergency services use geoprocessing to assign fire stations to fire zones while ensuring response times meet requirements. The chapter also touched on calculating straight-line distances, which represent the shortest possible path between two locations. While this is simple, it’s often unrealistic in real-world scenarios, as factors like driving routes, sidewalks, and crosswalks must be taken into account. GIS isn’t just about mapping; it’s about solving practical problems in everyday life. I also began to consider how GIS can handle large datasets during geoprocessing. I wonder if this process would slow down with larger data sets or how GIS can integrate road issues and other real-world factors to calculate more accurate analyses. Overall, this chapter gave me more confidence in using ArcGIS Pro. Initially, I struggled with understanding the purpose of GIS, but now I feel much more equipped to apply these tools for meaningful analysis and decision-making.

Just overall all that I have learned about GIS so far has been fascinating and the amount of detail that all of this entails has been super interesting for me to learn.

Chapter 4: 

Chapter four opens discussing density mapping. The purpose of this style of mapping is to more accurately portray clustered information who’s data would be hindered rather than elevated had it been individually mapped. It utilizes a standard unit of measurement, such as square miles, to provide a map with a clearer distribution. One of the main examples for when density mapping is useful is census tracts and counties. The book explains that due to their often arbitrary boundaries, these divisions of land can inaccurately represent data.

The chapter discusses the different ways that density mapping can be carried out. Using a dot map or calculating a density map for each area are the two ways given on page 110. A dot map is exactly what it sounds like, a map that you add dots to to represent the data. Rather than one dot representing each individual data point it represents a ‘specified number of features’  (e.g. 1 dot = 100 households). These dots are distributed randomly within each area meaning they don’t represent the data’s specific location either. The closer these dots are together the higher the density of the feature being represented is in that area. In order to calculate the density of an area, “you divide the total number of features, or total value of the features, by the polygon. Each area is then shaded based on its density value.” There are many map comparisons throughout the explanations that show how density surface becomes more effective when comparing mapped individual features.

The chapter also discusses the importance of your search radius. A larger search radius allows the GIS to consider more features when making calculations. While smaller search radiuses allow the GIS to represent more localized variation. It also makes an important emphasis on the point that the radius and density units do not have to be the same. 

 

Chapter 5:

The explanation and example of why to map what’s inside of an area made it much more interesting to read about. It took me a moment to comprehend what was different about “mapping inside” of an area vs creating another kind of map. However, the way in which this chapter explains this to be used as an ever-evolving tool puts it into a much better perspective. Mapping inside of a specified area allows us to monitor what’s occurring inside it, or to compare features from inside several areas. This can often shift the need for action or not. Distinguishing whether or not you need to map inside one or multiple areas will determine how much work is required to create these maps. Mapping inside single areas provides you with a lot, such as; A service area, a buffer, an administrative or natural boundary, a boundary you create, and other features. When mapping instead several areas, you are able to compare the findings, thus comparing these multiple areas. 

Discrete and continuous features were a topic that was very informative. Discrete features exist in well-defined boundaries. They represent things that occur at very specific locations. These would be things such as roads, buildings, etc. Continuous features exist over a broader scale. These are things that take large amounts of distance to change. These are things such as temperatures or elevation. 

I really appreciated the A summary of a numeric attribute section. There is nothing better than a book that can be explanation-dense just giving the reader a list of definitions. Thankfully all of the most common ones listed are basic concepts from the Statistics class I am also taking this semester. One of the very helpful aspects that has been very strong in this chapter is the What the GIS does headings. As i’m writing this I am going back over one of these sections that refers to overlaying areas with continuous values. Hearing the processes of the software helps me understand the actual uses for these actions.

 

Chapter 6:

Chapter six opens with discussing mapping ‘nearby.’ This is something that I guess I have never really considered to be as extensive as the chapter lays it out being. One part of mapping ‘nearby’ that intrigues me is how you define what is ‘nearby.’ As my interests usually lie in urban planning, this often looks different depending on the type of location you’re operating in. According to page 182, “Deciding how to measure ‘nearness’ and what information you need from the analysis will help you decide which method to use.” The term area of influence also caught my attention whilst reading. The idea of mapping a feature’s impact and scaling it is an extremely useful and exciting feature. Mapping the distances between places like schools and corner stores that sell nicotine products cannot always immediately point out the issues. 

I had a somewhat difficult time understanding what the text was referring to when the phrase costs were used to reference the measurement of something that’s nearby. For a minute I really thought we were talking about gas pricing or how much a subway card costs, when the discussion of time being a cost began on page 184 it started to make a lot more sense. This addresses one of my main concerns about ‘mapping nearby.’ As the United States has been bulldozed for vehicles, I was concerned that ‘nearby’ would be confined to a physical closeness.

Chapter 4

Chapter Four covered Map density and its purpose. By mapping density, we can see where the highest concentration of a feature is. Mapping density is important to show the number of people per census tract. Density maps are something I am familiar with and have used before in different classes. What I found interesting was that you can map the density of features or feature values. For example, you can map business locations or the number of employees at each business. You can also map density graphically, using a dot map, or calculate a density value for each area. The operation of creating a map by a defined area or by density surface varies greatly. The density surface method may provide better information however it requires more effort. The chapter concludes by reviewing the methods and features of each. It explains what GIS is doing when creating density surface maps, like how the software will total the number of features that fall in an area and divide it by the area of the neighborhood. The calculations GIS creates are determined by the parameters specified by the user. Such as cell size, search radius, calculation method, and units. After reading this chapter, I have learned a lot more about the niche parts of GIS. There are so many important factors that go into map making and it is important to be diligent.

Chapter 5

Chapter 5 started by discussing why we should map what’s inside an area. This is important to mapping because it tells us if we need to take action on a certain topic. Many organizations and people rely on this type of mapping to complete their jobs and make communities better. When mapping, you must determine if you are looking for something inside a single area or each of several areas. Single areas include a service area around a central facility and a buffer defines a distance around a certain feature. As well as a natural boundary, an area you draw manually, result of a model (ex.boundaries of a floodplain). When finding something in several areas you are able to compare them to each other. These areas include contiguous (ex. zipcodes), disjunct (ex. state parks), and nested (ex. 50-100 year old floodplains). You can either have discrete or continuous features in your chosen area. Discrete features are unique and identifiable like locations. Whereas continuous features represent seamless geographic phenomena like precipitation or elevations. There are three ways of finding what is inside a given area by drawing areas and features, selecting features inside the areas, and overlaying the areas and features. Each method explains what it is good for and what information you need for the method . I found it interesting that there were so many options when finding what is inside and what the tradeoffs are between them. They discuss how to choose the best method and how to complete each. Each method has its own criteria and process different from the others. Finally, the tools in GIS can help us create summaries for our results, including a count, frequency, and a summary of a  numeric attribute (most commonly a sum). A count is the total number of features in an area and the frequency is the number of features with a given value or within a range of values. You can display frequency in the form of a bar chart in order to read the data better. I found this helpful to know for the future, because it may be easier to read for me. The summary of a numeric attribute (sum) can just be the overall total of something in the map.

Chapter 6

This chapter begins with asking why map what’s nearby. When using GIS you can discover what is in traveling distance and find out what is occurring within a distance or feature.  I found the example of the wildlife biologist interesting because that is something I resonate with. It discussed that they may want to know what is in a certain traveling range around a stream. They would use the features in this area to determine prime deer habitat.  I found the following paragraphs to be interesting.  It discussed that distance is one way of defining how close something is but nearness does not always use distance. You can actually measure something nearby with cost, for example time, money, and effort expended. By mapping costs rather than  distance you are able to find a more precise measure of what’s nearby. This was interesting to me because I have noticed that sometimes somewhere that is 15 miles away is actually closer than somewhere that is 10 miles away. So seeing that being measured and explained through a GIS standpoint was intriguing. Next the chapter went over three ways of finding what’s nearby, straight line distance, distance or cost over a network, and cost over a surface. Each method is good for certain things and requires different data. I found it very helpful that this chapter gave a list of guidelines for choosing the best method. It helped me better understand why you would use different strategies for different problems. This chapter built on various previous concepts which made the question of why map what’s nearby much easier to understand.

Chapter 4: Mapping density explores techniques used in GIS to visualize data concentration and distribution. Mapping density shows where the highest concentration of features are, making it useful for identifying patterns. Areas with many features may be difficult to analyze visually, so density maps allow measurement using units like hectares or square miles to better understand distribution. This is useful in mapping things like census tracts or counties. The chapter highlights two main ways for density mapping, by defined area and by density surface.  Defined area mapping uses dot density maps, where each dot represents a specified quantity of a feature. A shaded density map can also be used, where polygons are colored based on density values. Density surface mapping is created in the GIS as a raster layer. Each cell in the raster layer receives a density value based on the number of features within the radius. When deciding how to map density, it is important to consider the features being mapped and the information needed. Density mapping can focus on features or feature values, which can lead to different interpretations. Displaying density surfaces effectively requires careful classification of data values. Common classification methods include: Natural breaks, quantile, equal interval, and standard deviation. Choosing the right number of classes is important- too many can make patterns hard to distinguish, and too little may oversimplify. Density surfaces are typically displayed using a single color gradient, with darker shades representing higher density.  Contours can also be combined with shaded density surfaces. Overall, chapter 4 provided a good understanding of density mapping and its significance in GIS analysis. 

 

Chapter 5: Finding what’s inside discusses how GIS allows users to analyze what is inside a specific area, which is important for monitoring and comparing multiple regions. Mapping what is inside an area helps identify patterns, summarize key features, and support decision-making processes. The chapter introduces three primary methods for determining what is inside a given area: drawing areas and features, selecting features inside an area, and overlaying the areas and features. Drawing areas and features is the simplest method, allowing you to create a visual boundary around an area and examine what features are inside or outside. However, this method is purely observational and lacks quantitative data. Selecting features inside an area is a more detailed approach, because GIS can generate lists, counts, or summaries of the features within a defined boundary. The most comprehensive method is overlaying areas and features, where GIS combines the area and its features into a new layer with attributes from both. The chapter also talks about discrete and continuous features. Discrete features are individually countable items, such as businesses or crimes, while continuous features represent measurements that vary over space, such as elevation or weather.  The choice between vector and raster overlay also impacts the accuracy of the analysis. Vector overlay provides precise areal measurements but requires more processing, while raster overlay automatically calculates areal extents but may be less accurate.

 

Chapter 6: finding what’s nearby focuses on what is near a specific feature. This type of analysis is essential for monitoring surrounding areas, measuring distances between features, and understanding spatial relationships. GIS allows for finding what is nearby by using three main methods: straight-line distance, distance or cost over a network, and cost over a surface. Each of these methods has its own practical applications and limitations, making it important to choose the right approach based on the type of data and analysis being conducted.

Straight-line distance is the simplest method and is commonly used to create a boundary around a feature. This technique is useful when a fixed range is required, such as identifying all homes within a 500-foot radius of a proposed construction site. However, it does not account for real-world barriers like roads, rivers, or elevation changes, which can affect actual accessibility. Distance or cost over a network is a more advanced method that considers travel constraints such as road networks or transit systems. This is particularly useful for measuring travel time to a location, such as determining emergency response times for a fire station. Unlike straight-line distance, this approach provides a more accurate representation of accessibility since it factors in infrastructure. Cost over a surface takes analysis a step further by incorporating the effects of terrain and environmental conditions. Instead of following fixed pathways like roads, it measures travel costs based on real-world conditions, such as steep slopes, water bodies, or different land covers. This method is commonly used for overland travel analysis, such as identifying suitable areas for hiking trails or wildlife movement. Overall, Chapter 6 builds on previous GIS concepts by shifting the focus from what is inside an area to what is nearby. By using different proximity analysis methods, GIS provides valuable insights for decision-making in urban planning, emergency response, environmental monitoring, and transportation analysis. Understanding how to find what is nearby is important for making informed spatial decisions.

Chapter 4: Identifying Clusters

Chapter 4 explains how to identify clusters, which happen when features are found in close proximity. By pinpointing, it can help to determine cause of clusters in that particular location. Statistics are used to determine if there are reasons for the clusters or if they happened by chance. Cluster of features with similar attribute values can be brought up using discrete features, spatially continuous data or data summarizing. They are interval or ratio values. Depending on what you are trying to pinpoint, you may have to put a specific period of time, or even a specific date. Clusters are almost always defined by straight-line distance. This could work, unless you are trying to find distance in travel time. The nearest Neighborhood hierarchical clustering specifies the distant features that can be found from each other in order to pbe part of a cluster. It also determined the minimum number to be able to consider it a cluster. It can also show the clustering at different geographical scales. To see the orientation of individual clusters, GIS may calculate the standard deviational ellipse for the points. To find the causes of clusters, you would want to compare clusters to a control group. To do this the control group and clusters can be mapped together, or you can creat clusters for the control group and compare them with the original clusters being analyzed. Clusters can also be identified on whether they are similar to their neighbors or not. Basically, if high values are surrounded by high values, they were similar, and vice versa. Using Moran’s I, means you are interested in local variation. A large positive value for Moran’s I indicates that the feature is surrounded by features with similar values, and a negative value means the feature is around dissimilar features. The G-statistic shows where cluster of high and low values are. There are two different methods. The Gi statistic helps you determine the effect of the target feature and what is going on around it. Gi* is the where you can find hot and cold spots.

Chapter 5: Analyzing Geographic Relationships

Chapter 5 begins with examples of how GIS is used in different fields. Some of the fields mentioned were transportation analysts, environmental lawyers, archeologists, state police, and wildlife biologists. In stats, attributes are the variables. Two analyze the relationships between attributes, you can use a defined area, sample point, or raster cell. The variables from different layers need to be associated with the same geographical unit. A ratio needs to be used if the locations are two different sizes. For different sets of features, they need to be combined somehow. To do this, you can either do a polygon layover or create rasters of the areas, making them the same size. Variables can also be created to represent spatial interaction between features. These features could be distance, travel time, or travel cost. Statistics is a huge thing when coming to analyzing geographic relationships. spatial autocorrelation is one. It violates the assumption that observations are independent. It brings the redundancy into analysis. To study the relationship between two variables as well as the nature, you measure the extent to which they vary together. Values have a direct relationship is the they both increase when one of the also increases. If one decreases while another increases, that is an inverse relationship. Other than that, there won’t be a relationship. There are also posiitive and negative correlations.

CHAPTER 4

density maps are something I have always taken an interest in, particularly in the ecological field. It’s always interesting to see the population density maps of local species. I also don’t usually think of density maps like the book demonstrated. They use business density instead of points. The book gives a great definition of when to use mapping density; ” mapping density is especially useful. Mapping density is especially useful when mapping areas such as census tracks or countries that vary greatly in size. As mentioned previously, this is how I viewed density maps. The color gradient makes density maps easy to follow and understand. The map Mitchell uses to show logging roads on page 108 does an efficient job at showing the density of the logging roads. However, it is confusing to follow. While reading, I was initially confused about the difference between map features or feature values. The book defines features as locations of businesses and feature values as the number of employees at each business. The visual figure on page 109 does a good job of demonstrating the differences, leaving me with no further questions on the differences. My previous understanding of density maps was strictly limited to the color gradient style that we are most familiar with. While reading Mitchell’s book, I was informed on the use of density maps as dot maps for this example. They still use businesses but one dot equals five businesses to show the density. One interesting point about dot maps was the dogs are distributed randomly within each area. They don’t represent an actual feature location however, the closer that together the dots are the higher density of features in that area.

CHAPTER 5

Chapter 5 is about why map what’s inside. Mitchell says on page 134 by monitoring what’s going on in an area, people know whether to take action. He uses the example of a district attorney who would monitor drug related to arrests to find out if an arrest within 1000 feet of a school if the arrest occurred within 1000 feet, stiffer penalties would be applied. The nice part about the density map that I learned about is you can find out what’s inside a single area or inside of several areas. When I originally read about surveying multiple areas, I was initially confused about the logistics behind it, but the book uses a great example, such as ZIP Codes or watersheds. The book has a good section on whether the features inside the map are discreet or continuous. It goes on to describe discreet features as unique, identifiable features you can list,  count, or summarize. A numeric attribute is associated with them. Continuous features represent seamless geographic phenomena. The example the book uses is especially continuous categories or classes such as vegetation type or elevation range *topographical maps. The section on page 147 under comparing methods was extremely insightful. While reading through chapter 5 i found myself wondering when to use which methods the three-layered tables lay out all three methods: drawing areas and features, selecting the features inside the area, and overlay the areas in features. Was extremely useful on what each method is good for the types of features that uses and the trade-offs associated with them. another thing I found interesting in Chapter 5 was the ability to take the data from the map and put it on paper. a count is the total number of features inside the area. this was taking was we collected and allows us to use it in a data set. 

CHAPTER 6

As I read in chapter 6, I think all of the learning objectives which were: Y map what’s nearby, defining your analysis, three ways of finding what’s nearby, using straight line distance, measuring distance, or cost over a network, calculating cost over a geographic surface were addressed fully and to the greatest extent. The first section wide map what’s nearby was extremely applicable to your daily life as they mentioned you can find out what’s occurring within the set distance. You could also find out what’s within traveling range. One thing I wouldn’t have thought to take into account about measuring distance was measuring flat plane, or the curvature of the earth. A recurring concept that we have seen through all six chapters now was needing to know the specific information you are looking for from the analysis i.e. do you need a list count or summary? Once again, I found the comparing methods table to be extremely useful a compared the three methods straight line distance, distance or cost over a network,  and cost over surface. The table gave the appropriate uses the rhyming features. You should look for the measures the pros and the cons and the very next section called choosing a method put the table into action and made it very straightforward. Much later in the chapter 6, it gets very in depth on making maps using distance using costs how arc GIS works specifying network layers stops, and turns travel parameters. It’s quite impressive. How much computing you can have arcGIS do.