These tutorials assume that you have access to ArcGIS either in our computer lab or installed on your computer so there won't be any instructions on how to install. They are written for someone with basic knowledge of ArcGIS's interface, that wants a walkthrough how this tool can be used to help them ask and answer their spatial research questions.
For tutorials starting at a beginner level I recommend those on ESRI's website or if you have a New York Public Library card, they have several great tutorials for newcomers to ArcGIS on their Lynda.com portal.
The data I'll be using in these tutorials comes from felony drug arrest statistics by county that I got from the New York State Division of Criminal Justice Services, spatial data from the New York State GIS Portal and data from the Law Enforcement Support office at the Defense Logistics Agency concerning the 1033 program. In brief, the 1033 program lets local law enforcement agencies request decommissioned military equipment ranging from office furniture, to night-vision, to weapons to Mine-Resistant Ambush Proof Vehicles (MRAPs).
While the data I'm using is real from a project that I did, it isn't going to be updated so please go back to the original sources mentioned if you'd like to explore it further. I picked these data sets because they were handy and they are large and complex enough to show how to work with different kinds of data but not too complicated to get in the way of explaining the process. The maps made in these examples are not necessarily scholarly rigorous but hopefully the process of making them will get you familiar enough with ArcGIS Pro so that when you map your own data, you'll have more time for scholarly rigor!
When displaying point data, you can have the marker's position display location information, you can have its color display qualitative or quantitative information and you may want to let the size of the marker convey further quantitative information. If you're mapping area schools, the color could mean whether they were public or private and the student to teacher ratio could be displayed by how large or small the dot showing the location of the school is. You can do this with graduated symbols that change based on numerical quantities associated with a point, much as how the changing colors of the counties in the previous exercise showed differences in arrest-rates between counties.
Another scenario that will be explored in this exercise is the situation where you don't necessarily have a one-to-one ratio between the point you have on your shape-file and the additional information you want to add from an attribute table. For instance you may have a shapefile with different points representing addresses and then on your attribute table a different line for that address for every person who lived there that you found from census records. In that case you'd want to summarize your attribute table to get the total count of people at that address.
You'll learn how to both use graduated symbols and the summarize function on your data tables in this exercise using data I have downloaded regarding individual law enforcement agencies and shipments made to them via the 1033 program.
Below I'll include the New York State Base Map.mxd that I created in a previous exercise and 1033Data+PoliceStations.csv. The table in the csv contains information on what equipment was received by law enforcement agencies through the 1033 program. Each of the law-enforcement agencies is associated with a latitude and longitude of one of the police stations in that agency since the program's records don't have a specific shipping address. You'll use this data to map the different quantities received by each police station
With the sheet as it is now, all you'll be able to do is place a point over each police station. You can make the point proportionally larger each time for the value of the item received or the quantity of items received in a shipment, but if there are many shipments of one, you'll need to use the Identify tool in order to get more information on what was received. However once you create a layer with these points on it, you'll have more options on how to edit it.
There are multiple records for each of the law enforcement agencies, one for each shipment of equipment but since all that's currently being visualized in the Symbology tab is the location, only one symbol will be displayed.
The additional data is available on the layer, you'll just need to change what the symbols are linked to in order to do it. First though, before you forget, add the metadata to your layer so that your viewer can check the description and see where your data for your symbols comes from.
You may be working on projects with many layers, and you'll want to know where the information on those layers came from. Adding a description and credits to your layer first thing is a very good habit to get into so that when you cite the sources on your map, you will just be able to get that information in the description of the layer, instead of digging back through your computer to find the source you got your information from. If you upload your map online, it is best practice to have the source of your information cited as well so viewers will know where you got it from
Since you've added an informational layer, you'll want to add metadata to this layer explaining where you got this information from and what it is.
Information on the 1033 shipments to law enforcement agencies courtesy of Defense Logistics Agency, Law Enforcement Support Office from the 2017 dataset "LESO Property Transferred to Participating Agencies" from: http://www.dla.mil/DispositionServices/Offers/Reutilization/LawEnforcement.aspx.
This is the place where I got the information that I included on 1033Data+PoliceStations.csv which you used to map the police stations.
Defense Logistics Agency, Law Enforcement Support Office
To total up amount of items received in total, or the total in acquisition value of all items, you'll need to alter the sheet using Summarize. However, in order to do this you'll first need to export the map you made as a new layer.
You'll notice, by default 1033Data will revert back to just displaying a small dot for each police station. You can change it back to display the graduated symbols, or first, you can edit and use the spread sheet in order to come up with totals for each law enforcement agency both of quantity of items and of acquisition value.
To be clear, you can also create these totals in a csv or excel file before you upload that file into ArcGIS, but it's good to know how to use the Summarize function. You can take any field on an attribute table and summarize, count or average the values associated with the records containing that field. This can be helpful to you if you have multiple records associated with a single place, and some of the fields on your attribute table are numerical.
Perhaps you are mapping 311 complaints from apartments using a table where each complaint is its own row . You have fields with information including the number of residents in the apartment making a complaint and the possible fine to the landlord for that violation. Instead of just having the one dot over a building so that your viewer would have to use the Identify tool in order to see the list of violations associated with that building, you could use the Summarize function on the attribute table to
These new quantities can then be symbolized so that the symbols on your map can give additional information to whoever is looking at your map other than that buildings with the symbol have had at least one 311 complaint made against them.
In this scenario, you're looking to get the total in quantity of items, in acquisition value and the count of shipments associated with each law enforcement agency, and I'll show you how to use Summarize to do this.
As you'll note there isn't a Latitude and Longitude associated with this table, so in order to graph this, you'll need to join this table to your layer that contains locational data for the police stations.
When you have geographic data on one layer, and additional attribute data for the points, cities, areas, etc in an independent table you can join the table to the layer, and so display that data on your map.
With the new data coordinate data attached to your table you can now make a point for each police station that displays the data about the totals associated with it.
Now with these summaries for the total acquisition value and total quantity received added to locational data for the law enforcement agency receiving it, you can visualize the amount received by each police station, not as individual transactions but as a total.
When you hit OK your map will look something like this.
When you have a lot of data fields on the attribute table for a layer, you have a lot of options for what you can visualize and how you symbolize it. For any given layer though, you can only pick one symbol system. That's why it's handy that ArcGIS will let you copy a layer, and paste back into your Table of Contents with all its associated data intact.
You can also use the summarize function on your original table to create a new table with information such as the average acquisition value of each purchase. With a large amount of varied data there are a lot of things you can do with it.
I've attached my finished map document below if you want to see how I constructed it. This will also be useful for you in the next exercise.
In order to see how many datapoints fall within a town or county or other geographic unit, you can join two layers of your map not based on the data on their tables, but based by whether or not they intersect with a region on the map. This is called a Join by Location.
Scenarios that this is useful for include ones where you have datapoints associated with a specific place or event that you want to provide additional context for based on the data available about their surrounding towns, cities, counties, states or other regions. Maybe you're investigating a past outbreak of a disease and want to take the datapoints you have representing documented cases and associate it with zip codes so you can instead of looking at every given point and trying to draw a conclusion, visualize which zip codes had the most cases. Maybe you're doing a study on urban farming and want to visualize which census blocks have the most farms. Maybe in both of those cases you have underlying data about those zip codes or blocks that you'll want to use as context to understand why there are more sick people or urban farms in those places, like median income or population density. These are cases where spatially joining your data points in one layer to geographic features in the other can come in handy.
In this case, you have individual data points on the map related to receipts by certain law enforcement agencies of former military equipment via the 1033 program. You have data about felony drug-arrest rates but only on a county-level basis. In order to ask the question if law enforcement agencies in counties with high felony drug-arrest rates request larger values of equipment, you'd need to determine which counties these agencies were located within. Since you have the boundaries of these counties already drawn on your map, with the Join By Location function you can add a new layer that compiles all the data for points falling within each county.
For this exercise you'll be using the map we created in the last exercise, but if you didn't do that one, the map is below.
For the map you want to wind up with that visualizes the total acquisition value for all items acquired by the law enforcement agencies within the county 103Data_Original is the layer that you'll want to be joining to the Counties layer in your basemap. Since there isn't a separate field in this table that contains data about which county this law enforcement agency falls into, you'll need to do this based on these datapoint's spatial location.
The new TotalsByCounty shapefile will be automatically added as a layer to your map, but it will originally just appear as a flat symbol. You'll have to go in and change the symbology of the layer to have it display the total of value of the equipment acquired in each county.
When you finish, your map will look something like this
A reason to make the background underneath the graduated cells transparent is so that you can then put other county-level data beneath it, like population, economic or demographics data, or in the case of the attribute table that is associated with the County shapefile in this document, per capita drug arrests.
You'll get a map that looks something like the below, which allows you to display two kinds of data at the same time, but because it uses different methods of visualization it's still pretty clear to read. You can tell by looking at a county how much the items it received were valued at and the level of drug arrests per capita that it had.
However, look closely at the scale for the symbols. You're not quite done yet. Notice how the low end of the first scale is set to 0. Is that because they didn't receive any equipment or because they received equipment that didn't have an acquisition value? Currently, you don't know, however, there is a way to correct this so only items that received any equipment from the 1033 program show up on this map and that is by using a Definition Query on your layer.
A Definition Query lets you define which parts of your data will be shown on the map. This can be useful to you when you're working from a large data set where only a subset of the data in it is of interest to your project. In this case, you only want to display dots on counties where they received at least some equipment from the 1033 program. You could have tackled this by creating an invisible symbol to be used for the class of $0 in acquisition value however, it is possible that some counties only received equipment that didn't have an acquisition value assigned to them, and you would be leaving out areas pertinent to your study. What you need is to find the column that will tell you if any of the LEA totals at all were located within that county, and use that to build a query.
Save your map now. Remember, always be on the lookout for inconsistencies in your map. Are you leaving somewhere in that doesn't apply to your research question? Are you leaving something out that does?
You can see the map as I've configured it saved below.
When you've uploaded a sheet with multiple fields, it can get a little daunting to figure out what you want to be visualizing about your places of interest. If you have a table you downloaded from the census with population, economic and demographic information for an area across a five year span, you might only be interested certain years, or certain measures or certain counties. You can make decisions on the fly in ArcGIS about which maps are pertinent to your research question, without editing this data in a separate program before uploading it .
In this exercise you'll learn how to select only certain categories of information, to export those selections as their own layer, and by using data frames be able to switch between different maps created by selections from a single table.
As mentioned in the previous exercise, the drug arrest rates by county that I have visualized on my county map are from 2012, but the data I have about what law enforcement agencies acquired from the 1033 program comes from many different years. What if you just wanted to visualize the total value of items acquired by the different counties in 2012 and 2013? Instead of creating new sheets in Excel with just the data for those years, in ArcGIS you'll use the Select by Attributes function to look at just shipments made in those years and export a new layer with only that subset of your data. Then, by putting each in their own data frame, you'll create the groundwork to create a layout with a side-by-side comparison in the next exercise.
Start from this modified selection of data created previously below
Since the data we have in our counties on the basemap has the drug felony arrest rates for 2012, the useful data to see if there is any relation between a high drug arrest-rate for New York State and more costly equipment being sent to that county would be to look at the shipments sent in 2012 and 2013. We'll want to pull the data for just these years and use them to create separate layers for the map.
"Ship_Year" = 2012 OR "Ship_Year" = 2013
In this instance you are looking for numerical information but this doesn't need to always be the case. If you instead only wanted to select items with a certain text value in the field you could do that instead, like if you only wanted items coded MOTOR VEHICLES,CYCLES, TRAILERS in the FederalSup field, you could have made that selection. It also doesn't need to be a complete match if it is numerical information, you could select only items over a certain acquisition value. You can string multiple requests into the same query, combining several of these together to look only for vehicles valued over a certain amount shipped within a certain year range.
Select By Attributes is a very useful tool for you to customize your map to only show information that is pertinent to your research question. Definition Query serves a similar function, however when you Select by Attribute, the information not within the selection is still visible on the map, it just isn't highlighted. But back to this query -
Since these are the items you want on your new layer, these are the selections you'll export.
Now that you only have the data for 2012 and 2013 on the map, you'll be able to make a map that only contains those pertinent years.
Your map should look something like this if you've chosen the same options I did, but you can certainly vary the scales or colors you use or whether you'd rather display the quantity of items received.
If you've done the previous exercises you've already changed the symbology in the Counties later, so just do the same thing you did the last time you visualized the Arrests per 100,000 data for the Counties layer, but if not see below.
You now have a map that looks like the below, where a viewer can tell at a glance what the acquisition value of shipments for law enforcement agencies in a county in 2012 and 2013 were as well as what the per capita felony drug-arrest rates were in that county in 2012.
Please note however that without summarizing, your viewer would need to click on the symbols on the map to see the total items received across shipments, so if you want to visualize the total acquisition value for all shipments for the law enforcement agency, you'd need to use the Summarize function described in the first module for this tutorial. If you want to visualize the total acquisition value of all items received in the county, you'd need to do the Join by Location function described in the previous module.
Looking at this map, it's hard sometimes to tell if one of the larger dots from one of the years is obscuring a smaller dot from a different year. To be able to look at your data more clearly, you'd want to separate these into a layer for 2012 and one for 2013. In order to eventually be able to display a side-by-side comparison of the two, you'll also want to put each in their own data frame.
Now that you've created a data frame for each year you'd like to explore with your data, you'll change the layers in each so they only reflect 2012 for one and 2013 for the other.
A Definition Query is used on a layer to define the data it will be displaying amongst the data available for it to display. Similar to Select By Attribute, you can select based on what values are in a field, either from a list of acceptable values, or if the values are numbers, those above or below a certain threshold. You can turn it off or on at any time but while one is on, your map will only display a selection of your data.
.Now you can right-click and select Activate to switch between your two views, so the two will stop overlapping. The other benefit of data frames will be made clear to you in the next exercise, they can be used to create new layouts with multiple maps on them.
If you want to see how I constructed this, the map document is below.
While on some occasions you'll be presenting only one map that you have created with your data, on others, for example for poster sessions, you may be adding in multiple maps that represent your data. Rather than taking screenshots of individual maps, using Layout View and multiple data frames allows you to present your maps in a unified fashion while still being able to customize and fine-tune the geographic aspects of your maps.
In a tutorial in the first module, Visualizing Data on a Map, you learned how to create a map when you had only one data frame. After completing this one you'll learn how to create a layout with multiple dataframes, thus allowing you to create a layout containing multiple maps. You'll add individual map elements for each data frame and make sure that the scale is as consistent as possible across both so that you are creating a unified picture of what information each presents.
We'll be using the attached map document to start with which is a modified version of the maps created in the last exercise
At first you'll see the two maps overlapping something like this.
When you've finished that, it will look something like this
Click and drag the resulting box to the middle of that section at the top that you set aside for the title. Use the text toolbar up top to change the size and color of the font to make it larger and stand out. I changed the font size to 17 and changed the color to Dark Navy.
You also will need to add text letting your viewer know where you got your information from. This lets people know if you are using credible sources, and lets other scholars to engage with your work and do work that builds off of yours. Maybe they are working in a similar area and when they see that you have statistics on felony drug arrest rates or acquisitions of decommissioned military equipment by police departments they'll want to know where they can can find similar information for their own geographic area or time period that they're interested in. You can convey this information by adding source text
The way for people to understand what the different symbols mean is to insert a legend which will explain what the symbols in each layer represent. However the legend's information is going to come from the scale, the titling and the headings you have for your layers in your Table of Contents. To have clear explanatory text on your legend, click on the titles and headings on each of the visible layers in your Table of Contents, and change the language to make it very clear what the symbols in it are representing
You'll now create a separate legend for Total Value of Items Shipped to LEAs by County, 2012 to go under that map frame on the left, for Felony Drug Arrest Rates by County, 2012 to go between the two since the same scale is on both maps, and the last legend for Total Value of Items Shipped to LEAs by County, 2013.
The Legend boxes that you create do not contain layer information in their own right. They are dynamic. If you change the symbols or turn a layer off or on using the Table of Contents view, the legend's labels, titles, and symbols will change accordingly. This comes in handy because sometimes until you see all the legends together on your map you won't realize that something about them needs to change.
From a cursory look at the map with the scales the way they are, it looks like there was a lot more valuable equipment shipped in 2012 than 2013. Sure, more of the symbols on the right hand map are larger rather than smaller but it's not like they're giant in comparison.
Examine the scales for the symbols for the Total Value of Items Shipped for 2012 and 2013, specifically at the maximum and minimums of each scale. Notice anything?
In 2012, the smallest value of total equipment sent to law enforcement agencies in a county was 300.28 and that class capped out at 3,808. In 2013, the smallest value of total equipment sent to law enforcement agencies in a county was 10,000 and that class capped out at 18,712,47. The same sized symbol on each map for the minimum number represents very different amounts in terms of the value of equipment. The largest symbol on the map in 2012 represents a class that has a maximum of $360,084.96. The largest symbol on the map in 2013 represents a class that has a maximum of 1,449,307. Though these symbols both are the same size, because they are for scales drawn up for the variation between total amounts within their own maps, they are not to scale between each other and that can lead to a misleading picture.
Let's change the 2013 classifications so that they are a more accurate comparison to the 2012 numbers.
To make things easier on ourselves, let's just change the 2013 classifications. But first we'll need to take a look at the sizes given to the symbols in the Total Value of Items Shipped to LEAs by County, 2012 layer.
You can experiment with different class boundaries and symbol sizes to create your own picture.
These basic navigation aids are less crucial on thematic maps like these that won't be used for navigation. A reader, however, might still want to know the distance between different counties and whether the map is in the typical configuration with North being straight up so it is helpful to add these.
You've now created a suitable layout for your project, it should look something like the below.
The way I constructed it is attached below, but please explore different manners of configuring your symbols and legends and titles and navigation aids to construct the map a different way if you prefer a different look.