GIS 1, Lab 5

GIS 1, Lab 5

Goal and Background:
          The purpose of Lab 5 was to determine suitable bear habitat in Marquette County, Michigan by evaluating the criteria and implementing the appropriate vector geoprocessing tools to generate the qualified output. Lastly, basic python scripting was introduced in this lab to utilize the geoprocessing tools to determine ideal lake resort locations and an air pollution index for Wisconsin.

Methods:
          At the start of the lab, I unzipped the lab 5 data to my Lab 5 folder, where I also set my workand scratch space to in ArcMap. The file bear_location_geog$ was an excel file with X-Y coordinates of bear locations in the Marquette County study area, so I created a feature class of an XY table of the excel file. I set the X field to Point_X, Y field to Point_Y, and imported the projected coordinate system from another feature class from Lab 5 data so the new feature class had the same coordinate system as the rest of the data. I named the feature class bear_locations, saved it to my custom Lab 5 geodatabase, and displayed in ArcMap. I displayed the rest of the data from the bear_management_area feature dataset. I set the symbology for the landcover layer to a unique values map for its "minor type" field for later use.
          Since we want to know what landcover the bear was in when their position was recorded, I performed a simple spatial inside join to join landcover and bears, named the new feature class bear_cover, and summarized the data. The three most population landcover minor types were Mixed Forest Land, Forested Wetland, and Evergreen Forest Land. I created a new feature class of the top three land types and called it suitable_landcover. Since bears might be found near streams for food sources, I created a 500 meter buffer with the buffer tool in the proximity toolset around the stream layer called stream-buffer and selected bears by location contained within the buffer. 72% of the bears were located within the buffered region, indicating that land near streams are also suitable bear habitat. Next, I used the overlay tool, intersect, to overlay suitable_landcover over stream_buffer to get bear suitable habitat output, which I called suitable_habitat. I dissolved the new layer to get rid of the unnecessary internal boundaries.
          Now what we found suitable bear habitat, we needed to find areas where there could be DNR bear management zones. To do this, I displayed the DNR management areas to the map. Next, I intersected the study area and the DNR management areas with dissolve to get management areas within the study area as output, called suitable_management_zones. Then, I intersected this new layer with suitable_habitat to get a new layer I called bear_management_zones.
          An additional criteria for bear management zones was that it shouldn't be near Urban or Built-up land. So I created a 5 km buffer around the "major type" landcover Urban Built-up land and called it urban_built_up_exclusion. I then used the erase tool to erase bear management zones by the urban built-up exclusion and get output that was outside the exclusion. I named the final layer final_DNR_bear_management_zones. Finally, I created a cartographically pleasing map of the final DNR bear management zones, including a legend and a locator map (Figure 1). In addition, I created a work flow model of my progress of creating this analysis in model builder (Figure 2).

    Figure 2: A work flow model of the steps to analyzing DNR bear management zones in Marquette County.

          To begin the second part of Lab 5, I created a subfolder in my Lab 5 folder called python_Output and set by work and scratch spaces to it. In a new, blank map, I displayed the Wisconsin data from the Part 2 subfolder - Wisoncin_cities, Lakes, Interstates, and Counties. The objective of this part of the lab was to determine suitable areas in Wisconsin to establish a lake resort using python scripting. I opened the python window to write out a code to create a 10 mile buffer around Wisconsin_cities, importing python for ArcGIS first (Figure 3). Next, I wrote code to select by attributes of lakes that are larger than 5 square miles in area. Another line of code creates a layer out of the selected features (Figure 4). Lastly, I wrote code to use the clip tool to exclude lakes from the new layer that are not within the cities buffer (Figure 5). I created cartographically pleasing map of the potential lake resort locations (Figure 6).

     Figure 3: Python window displaying code to create a 10 mile buffer around WI_cities.

     Figure 4: Python window displaying code to select by attributes from Lakes that have an area greater than 5 square miles and create a new layer from selected features.

  Figure 5: Python window displaying code to clip lakes that are within the cities buffer.

          In a new, blank map and displaying the Wisconsin data, I opened Python once again to help create an air pollution index around interstates. After importing Python to ArcGIS, I wrote code to create a multiple ring buffer around interstates for six, one mile intervals (Figure 7). Finally, I created a cartographically pleasing map of the pollution hazard index using a unique values symbol scheme (Figure 8).

    Figure 7: Python window displaying code to create a multiple ringed buffer for six one mile intervals.

Results: 
          Figure 1 shows the final map of DNR bear management zones amongst suitable bear habitat in Marquette County, Michigan. A back drop of land use was displayed along with a locator map to help better represent the area. Coincidentally, most of the GPS bear locations are outside of the management area.
          Figure 6 shows the final map of the lakes in Wisconsin that meet the criteria created to be established into a resort. The lakes shown are larger than 5 square mile and are within 10 miles of a city.
          Figure 8 shows the final map created displaying an air pollution hazard index around Wisconsin interstates due to transportation. Values range from very high, closest to the interstates, to low, furthest away. Each interval in one mile. The Southeastern part of the state shows a grouping of cites within the hazard zones.

                   Figure 1: A map displaying proposed DNR bear management zones in Marquette County.

                   Figure 6: Map showing qualified lakes for the establishment of a tourist resort.

                   Figure 8: Map showing air pollution hazard indexes around interstates.

Sources:
Michigan Department of Natural Resources (2015) DNR data. Lansing, MI: Michigan DNR.
ESRI. Michigan State data. Redlands, CA: ESRI.
Price, Maribeth (2016) Mastering ArcGIS. 7th Edition data. Columbus, OH: McGraw Hill.
Wilson, Cyril (2012) Lakes data.

GIS 1, Lab 4

GIS 1, Lab 4

Goal and Background:
          The goal of Lab 4 was to assemble attribute and spatial query expressions to acquire information from the data.

Methods:
          The first step to Lab 4 was setting my geoprocessing workspace and scratch space to my Lab 4 folder in ArcMap. I added the counties data from the U.S. geodatabase in order to write queries for the first three criteria. The first question wanted to return counties with a population between 3000 and 4000 in 2010 and counties with a population density of at least 1000 persons per square mile in 2010. I wrote out the query in the select by attributes tab to say SELECT * FROM counties WHERE: (POP2010 > 3000 AND POP2010 < 4000) OR (POP10_SQMI >= 1000). I exported the selected attributes to my Lab 4 folder and displayed the data onto the counties layer. In layer view, I created a cartographically pleasing map of the counties that meet the criteria to include a scale bar, north arrow, legend, title, and a description on the selected features (Figure 1).
          The second question asked to return counties in Wisconsin, Texas, New York, Minnesota, and California where the male population is greater than the female population and the population of seniors is over 6500.After setting up a new map of the counties, I wrote out the query in the select by attributes tab to say SELECT * FROM counties WHERE: (STATE_NAME IN ('Wisconsin', 'Texas', 'New York', 'Minnesota', 'California') AND MALES > FEMALES) AND (STATE_NAME IN ('Wisconsin', 'Texas', 'New York', 'Minnesota', 'California') AND AGE_65_UP > 6500). I exported the selected counties to my Lab 4 folder and displayed it as its own layer on top of the counties layer. Switching from data view to layer view, I created a map with the same qualities as the previous one (Figure 2).
          The next criteria asked to return counties is Washington, Maryland, Illinois, District of Columbia, Nebraska, and Michigan where there are more than 30,000 housing units available in addition to the previous query. I opened the select by attributes tabs and assembled the query to read SELECT * FROM counties WHERE: (STATE_NAME IN ('Wisconsin', 'Texas', 'New York', 'Minnesota', 'California') AND MALES > FEMALES) AND "AGE_65_UP" > 6500) OR  (STATE_NAME IN ('Washington', 'Maryland', 'Illinois', 'Nebraska', 'District of Columbia', 'Michigan') AND HOUSEHOLDS > 30000). Once again, I exported the selected features to my Lab 4 folder, displayed the new layer onto the map, and created a cartographically pleasing map of the counties that meet the criteria in layer view (Figure 3).
           For the next part of the lab, I downloaded the Wisconsin data into my Lab 4 folder where I unzipped the data. In ArcMap, I added the Wisconsin counties, cities, and lakes data to the data frame in a new blank map.  My next task was to return cities with a 2007 population between 15,000 and 20,000, with an area of at least 5 square miles,  the female population is greater than the male population and are within two mile of a lake. This called for an attribute query and a spatial query. First, I opened the select by attributes tab wrote, SELECT * FROM cities WHERE: "POP2007" >= 15000 AND "POP2007" >= 20000 AND ("AREALAND" >= 5) AND ("FEMALES" > "MALES"). I applied the query and then opened the select by location tab. I specified the selection method to select from the currently selected features, target layer to be WI_cities, source layer to be lakes, and to be a distance of 2 miles from the source layer. The selected cities meeting the criteria were exported to my Lab 4 folder and displayed onto the map. I created a map of the new layer along with the roads data with the same qualities and features of the previous maps (Figure 4).
           The final task wanted to know the total length in miles of thirteen rivers: Chippewa R, Eau Claire R, Embarrass R, Fisher R, Hunting R, Kinnickinnic R, Maunesha R, Milwaukee R, Moose R, Namekagon R, Pelican R, Platte R, and Potato R. First, I set up a new map with the Wisconsin counties data. In the River_ data's attribute table, I created a new field called LENGTH and calculated the geometry length for the records. I then opened the select by attributes tab assembled a query to read SELECT * FROM Rivers_ WHERE: "PNAME" IN ('Chippewa' R, 'Eau Claire R', 'Embarrass R', 'Fisher R', 'Hunting R', 'Kinnickinnic R', 'Maunesha R', 'Milwaukee R', 'Moose R', 'Namekagon R', 'Pelican R', 'Platte R', and 'Potato R'). The query selected these rivers. In the River_ data's attribute table, I viewed the statistics for LENGTH to find out the selected rivers' total length. Lastly, I exported the selected features to my Lab 4 folder, displayed it to the Wisconsin counties layer, and added the roads data and lakes data to the data frame. In layer view, I created final map of the selected features like the previous maps (Figure 5).

Results:
          The following maps are the result of the previous queries in their respective order.

Figure 1

Figure 2

Figure 3

                                             

Figure 4

Figure 5

Sources:
USA data (2016) [downloaded] Price, Maribeth H. [March 29, 2017].
Wisconsin data (2011) [downloaded] ESRI. [March 30, 2017].
Wisconsin Lakes (2012) [downloaded] Wilson, Cyril. [March 30, 2017].

GIS 1, Lab 3

GIS 1, Lab 3
Goal and Background:
          The objective for completing Lab 3 in GIS 1 was to gain experience in obtaining data from the U.S. Census Bureau's website, transforming a standalone table into workable data, joining attribute tables together, and displaying the data appropriately as a static map and a dynamic map on the web.

Methods:
         To initiate the Lab 3, I downloaded the TOTAL POPULATION dataset from the U.S. Census Bureau's website under its 2010 SF1 100% Data. I specified the dataset for all counties in Wisconsin under the geography tab. After downloaded, I unzipped the data which included the metadata and tabular data amongst others. The tabular data, called DEC_10_SF1_P1_with_ann.csv, was manipulated slightly (DOO1 changed to POPN_10 and the second row was deleted), and saved as a MS Excel file so it could be utilized in ArcMap.
          Next, I returned to the U.S. Census Bureau's website to download Wisconsin spatial from the map tab in the geography window and saved it as a shapefile.
          I opened ArcMap and added the Wisconsin shapefile and the MS Excel sheet to the data frame. As the goal was to map population, I viewed the tabular data and the attribute table and identified the fields GEO#id and GEO_ID as key terms for a one-to-one cardinality join. A new attribute table was formed as a result, combining information from the standalone table and the shapefile. Afterward, I exported the joined table as its own shapefile and added it to the data frame. I removed the previous layers and displayed the new one, symbolizing the population as a graduated color map with monochromatic colors. I added 8 classes as I thought it displayed population levels per county well.
          I opened an additional blank map in ArcMap afterwards and repeated the previous step=]\
. This time, I chose the Housing Units dataset from the U.S. Census Bureau's website. Repeating the same steps, I joined the standalone table and the Wisconsin shapefile attribute table together using the same ID fields, exported and displayed as a new layer. The Housing Units field were then displayed as a graduated color map with monochromatic colors with 7 classes. I gave the layers a NAD 1983 Stateplane Wisconsin Central projection and did the same for the Total Population map.
          Once the two maps were completed, I switched to layer view and prepared each map with a title, scale bar, north arrow, legend, author, date, and sources, arranged in a cartographically pleasing manner (Figure 1 and Figure 2).
          The second part of the lab involved making my Housing Units map into a dynamic map. First in data view, I signed into ArcGIS Online using my information. There, I published my map as a feature service.
          Once that was completed, I signed into ArcGIS.com. Here, I added my layer to a topographical base map. I configured a pop-up window titled Housing Units per County that would show county and and number of housing units if the user clicked on a part of the map. At the end, I saved the pop-up and shared the dynamic map (Figure 3).

Results:
          Figure 1 shows the first map that was created which displays the population of Wisconsin counties in 2010 as a graduated color map. The most densely populated counties lie to the south eastern part of the state. Figure 2 shows almost the same pattern of density as a graduated color map but displays housing units in 2010 instead. Figure 3 is the final product of the housing units per Wisconsin county layer displayed as a dynamic map on ArcGIS Online over a topographic base map.

                                            Figure 1

                                           Figure 2

                                          Figure 3

Sources:
TOTAL POPULATION (2010) [Download] U.S. Census Bureau. https://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t. [March 2017].

Housing Units (2010) [Download] U.S. Census Bureau.https://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t.  [March 2017].

Price, M. H. (2016) Mastering ArcGIS. New York, NY: McGraw-Hill Education.

GIS 1, Lab 1

GIS 1, Lab 1

Goal and background:
          As a student in GIS 1, the purpose of completing Lab 1 was to incorporate the skills I have obtained accomplishing tutorials 1-3 from Mastering ArcGIS by Maribeth Price and analyze different map projections. Utilizing the knowledge gained about geographic and projected coordinate systems, data was examined for projection errors and corrected accordingly.

Methods: 
          To start the lab, I unzipped lab1_data.zip to release the files to my personal GIS folder for easy access during the lab. To further simplify access to the data, I set my current and scratch workspace to my Lab 1 folder and stored relative pathnames to data sources from the geoprocessing menu and file menu respectively.
           For Part 1, I added country and geogrid data from my World folder to the map, making sure the country layer was above the geogrid layer. I named this layer Geographic Projection and set its coordinate system to WGS 1984. I saved the map to my Lab 1 folder as a good habit for protecting work in progress. Next, I added a new data frame to the map and named it Mercator Projection. Once again, I opened the data frame's property window and switched the data's coordinate system from the coordinate system tab, this time to a projected World Mercator Projection. I repeated these steps for three more data frames - named Sinusoidal, Equidistant-Conic, and Azithumal Equidistant and displaying them with the projected coordinate systems that matched the data frames' name (all in under world projections).
          For part 2 of Lab 1, I created a sixth data frame and named it Wisconsin UTM and added the states shapefile from the USA folder to the data frame. I used the select features tool to highlight Wisconsin from the layer. I right-clicked the states layer and selected "selection - create layer from selected feature." I then exported the selected feature using the data layer's current coordinate system to my Lab 1 folder and named in Wisconsin. The new shapefile was added to the data frame and removed the states layer so that Wisconsin was the only layer. I changed the projected coordinate system to UTM, NAD 1983, Zone 16N to best represent the state.
          For section 2 of part 2, I created a seventh data frame called States and added the states and stroads_miv5a shapefiles to the data frame. Although these shapeless do not have the same coordinate system, the data overlaid each other in a on-the-fly projection. I used the project tool to change the stroads_miv5a shapefile coordinate system to match the state shape file's by importing its coordinate system. Afterwards, I projected the data frame onto a North American Lambert Conformal Conic projection.
          For part 3, I created a map displaying all seven of my data frames by manipulating them in the layout view format. I labeled them accordingly, added a title, north arrows, name, date of creation, and scale information (Figure 1). The completed map was exposed to my Lab 1 folder as a .jpg.
          For part 4, I opened a new blank map and added the Central_WI_CTS.shp to the map. I warning appears because this shapefile has no defined coordinate system. Using the shapefile's metadata, I defined its geographic coordinate system as a North American 1983 GCS and its projected coordinate system as NAD 1983 Stateplane Wisconsin Central FIPS 4802 because of the counties location in central Wisconsin. Next, I added the Lower_Chip_strms.shp to the data frame but it has no projected coordinate system. Therefore, I defined its coordinate system by importing Central_WI_CTS.shp's projected coordinate system into the Lower_CHIP_strms.shp so they would be in the same coordinate system. In the layout view, I customized the map by adding labels, a title, a legion, a north arrow, netlike, name, and date created (Figure 2). Lastly, I exported this final map as a .jpg to my Lab 1 folder.
Results: 

                 Figure 1: The final product of part 3, which includes all seven data frames         encompassing the work done in parts 1 and 2, displayed under different map projections.

                    Figure 2: The final product of part 4 showing Central Wisconsin counties and rivers in
NAD 1983 Stateplane Wisconsin Central FIPS 4802.

Sources:
Michigan Department of Transportation (2017) [downloaded file] http://www.michigan.gov/mdot/ [Feb. 17, 2017].


Price, M. H. (2016) Mastering ArcGIS. New York, NY: McGraw-Hill Education.