Geography 291: Geospatial Analysis with Desktop GIS

Chapter 4 Summary

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

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

Chapter 5 Summary

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

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

Chapter 6 Summary

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

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

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

 

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

 

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

 

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

 

CAD layer on top of the actual feature layer. 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

Multivariable analysis of age of arrested individuals 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

Hammer-Aitoff world map projection. 

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

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

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

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

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

2.2 New York city land use map. 

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

 

 

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

 

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

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

i read the chapters 🙂

CHAP 4

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

CHAP 5:

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

CHAP 6:

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

Chapter 4: Mapping Density

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

 

Chapter 5: Finding What’s Inside

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

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

 

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

 

Chapter 6: Finding What’s Nearby

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

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

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

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

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

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

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

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

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

Chapter 4:

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

 

Chapter 5:

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

 

Chapter 6:

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

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

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

Chapter 5 spatial data

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

Chapter 6

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