LAB4_GEOG5181C6081C_AU23

Prof. Kim Geography 5181C/6081C: Lab Assignment 4 Understanding ModelBuilder & Generating Spatial Features Assigned Date: Monday, October 30, 2023 Due Date: Sunday, November 5, 2023, Labs are marked down 10% for each 24 hours late. Goals: The first three parts of this lab are to learn [1] how to use ModelBuilder to make a blueprint of your analysis, [2] how to create repetitive spatial analysis using ModelBuilder, and [3] how to utilize ModelBuilder in decision-making situations. The last part of this lab is to create spatial features based on raw coordinates. Notes: 1. Any ArcGIS Pro documents opened and manipulated for the exercises, as well as other necessary data, should be saved in your student folder. 2. To understand ModelBuilder, please read over Chapter in GETTING TO KNOW ArcGIS. Must: 1. Know how to look for different GIS functions in Toolbox (Help/Search). 2. Know how to project files. 3. Use critical thinking to answer some of the questions. Attention: For this lab, you don’t need to zip and upload the data and results of the part I (as the file size is quite large), but you do need to zip and upload the data and results of part II and part III to the blackboard. PART I. Exploratory Spatial Data Analysis (ESDA) Although many functions such as catalog and display of geographic data are very useful, the true power of GIS lies in its capabilities for spatial analysis. Step 1 : Examine the datasets (under data\part1 folder). The first file is a polygon shapefile of owned parcels in Hamilton County with an attribute for PARCELID , and the second is a point of sold parcels with appraisal values and sales values in the same area. In Catalog panel you should now see two spatial datasets: parcel_sale, parcel_poly.  Drag “parcel_sale” and “parcel_poly” into ArcGIS Pro.  Right click “ parcel_sale ” -> Open Attribute Table -> Right click on the “ FINVALTOT ” column -> statistics. 1

Prof. Kim Q1. How many points are there? What is the maximum value? What is the mean value? What is the sum value of all the parcels in this dataset? (40 points) Q2. Do the exact same thing for “SALEPRICE”. What is the maximum value? What is the mean value? What is the sum value of all the parcels in this dataset? (30 points)  Selection menu -> Select By Attributes. With the Select By Attributes window open, separately query for (1) FINVALTOT > 250000; (2) FINVALTOT > 1000000; (3) FINVALTOT > 2000000; and (4) FINVALTOT > 10000000. Make sure these are individual queries rather than cumulative ones. Q3. How many parcels for sale that are in East of I71 are valued over $1,000,000? Are there any parcels for sale over $1,000,000 that are in West of I75? How many parcels are valued over $10,000,000 in the entire region (Hamilton County)? (30 points) Q4. Does there appear to be a relationship between FINVALTOT and ANY VARIABLE? Where are most of the clusters of expensive parcels? (20 points) Step 2 : Create Subsets.  Join the parcel_sale to parcel_poly according to the common field “PARCELID”  Right click parcel_poly -> Data -> Export features. Save the output in yourdatafolder and name the output feature class as parcel_sale_p.shp. This might take several minutes.  Customize menu -> Extension -> Check the box of Geostatistical Analyst  Customize menu -> Analysis -> Tools -> Toolboxes -> Geostatistical Analyst  Geostatistical Analyst -> Utilities -> Subset Features  Input features: parcel_sale_p polygon  Output training feature class: yourdatafolder\parcel_ha_2  Output test feature class: yourdatafolder\parcel_ha_98  Size of training feature subset: 2  Subset size units: PERCENTAGE_OF_INPUT  Click OK and ArcGIS will randomly subset the polygon. This could take a while so be patient. The new datasets will be added to the layout automatically. Q5. Why did you subset the feature instead of using the whole thing? What is the purpose of a “training” set and a “testing” set? Why is random sampling so important? (30 points) 2

Prof. Kim  Answers to Question 1 through 5 (150 points) PART II. ModelBuilder ModelBuilder is a graphic design tool for diagramming solutions to spatial analysis problems. The workflow diagrams are called models (Note that model here is different from scientific model in general). A GIS analysis model is a sequence of steps or functions used as part of a GIS analysis. These sequences of geoprocessing steps can also be represented in ArcGIS with ModelBuilder. The user can repeat the same procedure over and over if necessary. ModelBuilder offers 3 main benefits: 1) It records all of the steps involved in a procedure; 2) It allows the procedures to be easily repeated and shared with others; and 3) It provides a visual representation to help with understanding what is going on in the procedure. Step 1 : Predictive model with ModelBuilder In this part, you will use ModelBuilder for exploratory analysis designed to aid in the creation of a predictive model for Mayan archaeological sites. In the context of archaeology, a predictive model is “a tool that indicates the probabilities of encountering an archaeological site anywhere within a landscape“ (from Minnesota Archaeological Predictive Model). Developing a predictive model essentially consists of trying to determine the logic and preferences in site selection of the people who built the archaeological sites in question. Using GIS, one can examine a set of environmental factors to see if any of their possible combinations seem to be repeatedly associated with a type of archaeological site. These insights can then be used to predict where other sites are likely to exist on the landscape. To illustrate, imagine you want to find new archaeological sites with artifacts from the Chumash tribes. Say that through exploratory analysis of previously-known sites you discover that they seemed to prefer occupying locations that are near the ocean and close to glades where with sage tends to grow. You could then analyze a region to see which areas in it meet those criteria, and direct your field searches to them in order to increase the likelihood that your archaeological dig will in fact find something. Before moving on to the ModelBuilder procedures, we must first prepare some of the data sets to be used.  In ArcGIS Pro, add all the data in “ Maya ” geodatabase under …\data\part2 folder to your map.  Highlight “ arch_sites ” in the Contents.  Customize menu -> Analysis -> Tools -> Geostatistical Analyst  Geostatistical Analyst -> Utilities ->Subset Features  Input features: arch_sites  Output training feature class: …\Maya.gdb\arch_sites_14  Output test feature class: …\Maya.gdb\arch_sites_86  Size of training feature subset: 14  Subset size units: PERCENTAGE_OF_INPUT 4

Prof. Kim  Click Run. Step 2 : Save in Geodatabase  In the Contents, only check and display “ county_bnd ” layer.  Right click “ county_bnd ” -> Open Attribute Table.  Select the record that COUNTRY field is “ GUATEMAL ”.  Export the selected feature: right click “ county_bnd ” -> Data -> Export features -> Set “Export” as selected features and save the Output feature class to ….\Maya.gdb, and name it as guatemal.shp .  Remove “ country_bnd ” from the Table of Contents to avoid clutter, as we will no longer need it. Step 3 : Creating the ArcToolbox ModelBuilder is accessed through ArcToolbox. It is one of the ways through which users can customize ArcToolbox for their own needs. A ModelBuilder can in fact be thought of as an ArcTool created by the user. The basic procedure for creating a new, empty Model is to first create a new Toolbox, and then create a new Model within that Toolbox.  In Catalog Window -> right click your data folder -> New -> Toolbox  Rename the new created toolbox as “ Maya_Toolbox.tbx ”  Right click “ Maya_Toolbox.tbx ” -> New -> Model  ModelBuilder window shows up, you will drag tools from ArcToolbox into this window to build the Model. Step 4 : Building the ModelBuilder  Toolboxes -> Analysis Tools -> Extract -> Clip  Select the Clip function, directly drag it into ModelBuilder window, you will get the following:  Double click the “Clip” rectangle to open up its setting window.  Input Features -> “rivers”  Clip Features -> “ guatemal ”  Output Feature Class -> …\Maya.gdb\ rivers_clip . 5

Prof. Kim  Double-click the Near rectangle to open up its setting window.  Input Features -> “ arch_sites_14 ”  Search Radius -> 5000 meters  Click OK. The functionality of this tool: The Near tool will determine which points in “ arch_sites_14 ” are within a 5km distance of a river. For points that are 5 km or less from a river, the specific distance (in meters) between the point and the river will be entered in the attribute table of “ arch_sites_14 ” under the new NEAR_DIST column. For points that are not, a value of 0 will be entered in the same attribute table column instead. Step 6 : Combine vegetation and soils feature classes The next few steps in constructing our Model involve determining which environmental attributes surround each of our archaeological sites. Conceptually, we simply need to find out in which vegetation, soils, and aspect polygons the sites reside. In practice, however, things are a little more complicated than this. First, there is no single ArcGIS geoprocessing tool that can determine the answer we need in just one step, so our analysis requires a multi-step procedure. Second, all of these layers must be combined into a single feature class before we can analyze them with the archaeological sites data. Third, although you could use the Union tool (or even Intersect) to combine the environmental attribute layers with one another, you must use the Identity tool when you bring the archaeological sites data into the analysis. Q1. Compare and contrast the Identity tool with Intersect and Union. Why is Identity the necessary tool to use with the archaeological sites data? (30 points) We will first combine the forest vegetation and soils layers into a single feature class. The Identify tool will be used in this and the following step for simplicity and consistency.  Add Identity function (Overlay  Analysis Tools) into the Model window.  Double-click on the Identity rectangle to open up its setting window.  Input Features -> “ guatfor ”  Identity Features -> “ soils ”  Output Feature Class -> …\Maya.gdb\gfor_soils  Click OK. “ gfor_soils ” will now be shown as the active output of Identify in the window. Step 7 : Add aspect data  Add Identity function (Overlay  Analysis Tools) into the Model window again. 7

Prof. Kim  Draw a connection between “ gfor_soils ” and Identity (2) rectangle and select Input Features .  Double-click on the Identity (2) rectangle to open up its setting window.  Identity Features -> “ aspect ”  Output Feature Class -> …\Maya.gdb\gfor_soils_asp  Click OK. This step will combine the aspect layer with the output from Step 6 into a single feature class. Q2. What is aspect? Provide the equation and explain how it is calculated. Explain it with respect to elevation. (20 points) Step 8 : Determine site attributes Now that the three environmental attribute layers have been combined into a single feature class, we can determine the surrounding attributes for the archaeological sites.  Add Identity function (Overlay  Analysis Tools) into the Model window again.  Draw a connection between “ arch_sites_14 (…) ” (the layer generated by Near function) and Identity (3) rectangle and select Input Features .  Draw another connection between “ gfor_soils_asp ” and Identity (3) rectangle and select Identity Features .  Double-click on the Identity (3) rectangle to open up its setting window.  Output Feature Class -> …\Maya.gdb\arch_sites_ID  Click OK. Step 9 : Simplify the output table At this point, we are effectively done with all the necessary processing to get our desired information about the archaeological sites. However, if we were to examine the attribute table of “ arch_sites_ID ” at this stage, we would find it a little difficult to pick out the specific information in which we are interested. Due mainly to using the Identity tool, the attribute table now has many more attribute columns in it than the four that we want to see (distance from a river, forest vegetation type, soil fertility, and aspect). To make things easier, we can use the Frequency tool to extract only those attributes that we want and save the results in a separate (non-spatial) table. Frequency also counts up the number of incidents of each case which is very helpful when dealing with datasets that are too large to count up manually.  Add Frequency tool (Statistics  Analysis Tools) into the Model window.  Draw a connection between “ arch_sites_ID ” and Frequency rectangle and select Input Table .  Double-click on the Frequency rectangle to open up its setting window.  Output Table -> …\Maya.mdb\ arch_sites_ID_Frequency  Under Frequency Field(s), scroll down and checkmark NEAR_DIST, DESC_, R_FERT, and ASP_CODE.  Click OK. 8

Prof. Kim  Click the Run button on the ModelBuilder tool bar to run the Model.  A dialog box will pop up with a progress bar and scrolling display listing each step as ArcGIS runs through them. In the ModelBuilder window each tool function rectangle will turn red while it is being processed. When the Model run is completed, click Close on the dialog box. Step 12: Examine results  The " arch_sites_ID_Frequency " table should now appear in the Contents -> List By Source. (If it does not, add it to the display from the "Maya" geodatabase.)  Right-click " arch_sites_ID_Frequency " -> Open.  Look through the table records. FREQUENCY lists the number of occurrences of each configuration of the four other attributes (e.g. a value of 2 means that two of the sites from " arch_sites_14 " had the attribute configuration listed in that row).  NEAR_DIST contains the proximity of the given site to a river -- either the distance to the nearest river in meters, or a -1 if the site is not within 5 km of any river.  The three environmental variables derived from our Identity operations are DESC_ (forest vegetation type), R_FERT (soil fertility), and ASP_CODE (aspect).  You will notice that while the DESC_ field has meaningful descriptions of the vegetation type, the latter two have only cryptic numbers in their records. Here are keys (i.e. metadata) explaining the meaning of the values in R_FERT and ASP_CODE: Table 1 Soil Fertility Codes (R_FERT) 1 High 2 Moderate 3 Low 4 Infertile Table 2 Aspect Codes (ASP_CODE) 1 flat 2 North (0-22 deg.) 3 Northeast 4 East 5 Southeast 6 South 7 Southwest 8 West 10

Prof. Kim 9 Northwest 10 North (337.5-360 deg.) Try to develop some conclusions regarding what combinations of characteristics (if any) seem to be the most commonly occurring. How useful the Frequency column itself will be in this is unfortunately unpredictable -- it depends entirely on the variability of your particular sample. Moreover, because the NEAR_DIST field mostly contains specific, detailed distance measurements there will probably be very few recurring identical values in the column, and thus you will get many Frequency counts of 1. You will probably find it more useful to sort the individual columns and count up the number of occurrences manually. Do this by right-clicking on the column headings in the table and selecting either Sort Ascending or Sort Descending. Remember, however, not to look at each environmental attribute in complete isolation! Although you do need to start by looking at the attribute columns individually, your ultimate goal is to find combinations of attributes. Q3. Write an analysis of the environmental characteristics of the Mayan archaeological sites in the sample (minimum length of one pages, double spaced, 12 fonts size). Your analysis must address the following: (100 points)  What are the most common clusters of environmental attributes? Does there appear to be any recurring pattern or tendency in the data?  Give possible explanations for why certain combinations of attributes seem to be more prevalent. Why might the Mayans have preferred sites with these characteristics? What are the shortcomings of this analysis -- its assumptions, weaknesses, etc.?  Are some of these environmental attributes interrelated? What could be done to offset these possible problems?  In light of what your analysis has uncovered, come up with a general outline for a predictive model of these sites. What factors and ranges of their values would you suggest using? Figure 2. Produce graphics showing your output table of the Frequency function. (20 points) For the Part II, you need to turn in the followings:  Figure 1 and 2 (50 points)  Answers to Question 1 through 3 (150 points) PART III. Generating Line Features You will assume the role of a recently hired GIS Analyst for Bearcat Airways in providing important spatial information for major US airports, flight routes, and flight distances. You are required to generate flight routes for 25 major 11

The FID number of the Origin cities The FID number of the destination cities Prof. Kim  Go to Contents (List By Source) -> right click the generated point_dis  In this table, the INPUT_FID field is the FID value of the origin cities, and the NEAR_FID field is the FID value of the destination cities. Now we need to add the XY coordinates of origin cities and XY coordinates of destination cities to generate lines between origins and destinations.  In the point_dis table, Add field, use this function to add the following fields to this table:  Name: o_xcoor; Type: Double  Name: o_ycoor; Type: Double  Name: o_id; Type: Short Integer  Name: d_xcoor; Type: Double  Name: d_ycoor; Type: Double 13

Prof. Kim  Name: d_id; Type: Short Integer  Name: id_line; Type: Short Interger  Join usa25cab_pro.shp to point_dis table Right click point_dis table -> Joins and Relates -> Join, set up as following: Input Join Field: INPUT_FID Join Table: usa25cab_pro Join Table Field: FID  Open the point_dis table, use Field Calculator to calculate the field values for o_xcoor, o_ycoor, o_id separately:  point_dis.o_xcoor = [usa25cab_pro.X_COORD]  point_dis.o_ycoor = [usa25cab_pro.Y_COORD]  point_dis.o_id = [usa25cab_pro.ID]  Right click point_dis table -> Joins and Relates -> Remove Joins -> usa25cab_pro  Join usa25cab_pro.shp to point_dis table again but with different common field Right click point_dis table -> Joins and Relates -> Join, set up as following, notice that we use “NEAR_FID” as common field this time Input Join Field: NEAR_FID Join Table: usa25cab_pro Join Table Field: FID  Open the point_dis table, use Field Calculator to calculate the field values for d_xcoor, d_ycoor, d_id separately:  point_dis.d_xcoor = [usa25cab_pro.X_COORD]  point_dis.d_ycoor = [usa25cab_pro.Y_COORD]  point_dis.d_id = [usa25cab_pro.ID]  Right click point_dis table -> Joins and Relates -> Remove Joins -> usa25cab_pro  Open the point_dis table, use Field Calculator to calculate the field value for id_line:  id_lines = [OBJECTID] Now we need to use this table to generate the lines between those 25 cities according to the x,y coordinates of the origins and destinations.  Data Management Tools -> Features -> XY to Line  Input table -> point_dis  Output Feature Class -> …\part3\Data.gdb\Data.gdb\OD_lines  Start X Field -> o_xcoor  Start Y Field -> o_ycoor  End X Field -> d_xcoor  End Y Field -> d_ycoor  Line Type (optional) -> GEODESIC  ID (optional) -> id_lines  Spatial Reference(optional)->GCS_North_American_1983 ( This is important!!! )  Click OK. 14

Prof. Kim According to this table, you can calculate the overall numbers of flow-ins and flow-outs respectively for each city. Q1. Describe the exact procedure how you associate those interaction among cities into the OD_lines_pro.shp? (30 points) Q2. Describe the exact procedure how you can calculate the overall numbers of flow-ins and flow-outs respectively for each city? (30 points) Map 1. Create a nice map showing 25 cities with lines (48) coming in and out of Cincinnati, OH . Label the flows on the arcs and cities in the points. Make sure 48 states polygons are projected in NAD 1983 UTM Zone 15N. Do not include Hawaii and Alaska. (50 points) Map 2. Create a nice map showing 25 cities with lines whose interaction has more than 25,000. Clearly label the flows on the arcs and cities in the points. (50 points) Step 5 : Calculate the length of lines. Open the attribute table of OD_lines_pro, add a new field named Arc_Length with data type as double . Right click this field and select “Calculate Geometry”. Click “Yes” if any warning window pops up. In the “Calculate Geometry” window, select Mile as the unit, click OK. Q3. Now you realize that distances that you have in your lines are different from the ones in “ INTERACTION.dbf” file. Explain why we end up with different distances? Is this really critical in terms of analysis? Calculate differences between your distance and my distance, and add all (600) of them. Provide the numbers that you come up with. (30 points) 16

Prof. Kim Step 6 : Now we are interested in visualizing the network in ArcScene. Make 3 different layers. [1] all the arcs coming out of Cincinnati , which have more than 5,000 interaction, [2] all the arcs going into Atlanta , which have more than 5,000 interaction, [3] all the arcs coming out of Denver , which have less than 5,000 . Step 7 : Add those layers in ArcScene. Make Cincinnati the top layer, Atlanta as the mid layer, and Denver as the bottom layer ( Refer to the slides provided by TA ). Q4. Explain the procedure how you visualize those 3 layers in ArcScene? (20 points) Q5. Experiment with different orders among three cities (airports). Discuss which order is best to visualize the patterns in ArcScene? (20 points) Q6. Discuss how 2D and 3D mapping are different regarding Q5? (20 points) Map 3. In ArcScene , Create a nice map of three layers showing 25 cities (from/to Cincinnati, Atlanta, and Denver). (50 points) For the Part III, you need to turn in the followings:  3 maps (150 points)  Answers to Questions 1 through 6 (150 points) 17