Lab 5: Mini-Final Project

Introduction:
My research question for this lab was: If I lived in Suffolk County, NY, where would be a good place for me to live? I thought this would be an interesting question to ask as I do not live in the US, as I am British and live in the UK. After spending a year in the Midwest I thought it would be interesting to do a vector analysis in another part of the US. The reason for choosing Suffolk County it is relatively close to New York City, but not in New York City. This is similar to where I live in the UK, I live close to London but not in the London or the Greater London area. I thought New York City would be an ideal place to live outside, as it is an international hub, making it easy for family and friends to come visit me.

The objective of this project was to find a suitable place for me to live in Suffolk County, using a criteria decided by me. My criteria included a place with a population of more than 20,000, a place within 1 mile of a highway, a place within 1.5 miles of a railway, a place within 10 miles of an airport, and a place at least 5 miles away from a river. My intended audience for this project was my family and friends as I wanted them to understand where I would live if I lived in Suffolk County, NY. This information would be used by my family and friends to understand how to get to me from New York City and for other internationals thinking of living near New York City, but not in New York City.

Data Sources:
In order to answer my question of where I should live in Suffolk County, NY, I needed data regarding the criteria (as specified above) I had set. I found all the data I needed on the campus drive on the UW-Eau Claire computer system, in the ESRI database. Feature classes I used from this database include ESRI.DBO.counties, ESRI.DBO.cities, ESRI.DBO.highways_usa, ESRI.DBDO.rail100k_usa, ESRI.DBO.airports_usa and ESRI.DBO.dtl_riv_usa. The main concern I had about all the data was how up to date it was, and therefore would my answer to my question be different if the data was more up to date. In particular I was not satisfied with the data provided in the ESRI.DBO.cities feature class, as the population data was from 2007, making it highly probable this data was unreliable as it was 7 years old.

Methods:
For this lab I decided to create 2 maps, one regarding the vector analysis I had done to answer my question and a locator map to enable the reader to understand where the analysis took place. To create these two maps I used ArcMap 10.2. I put the main map and locator map in separate data frames.  

Before I could answer my question, I had to prepare the data I wanted to use to help me answer this question. Firstly, I had to create a shapefile of Suffolk County. To do this I added the ESRI.DBO.counties feature class to a blank map in ArcMap 10.2. Then I used the select by rectangle tool to select Suffolk County in the state of New York. I exported this selected data, and named the new feature class Suffolk_County. After creating this feature class, I was able to remove the ESRI.DBO.counties feature class. Before adding data to this feature class I changed the projection of the data frame to NAD 1983 NSRS2007 StatePlane New York Central FIPS 3102 (Meters). To the Suffolk_County feature class I added the following feature classes: ESRI.DBO.cities, ESRI.DBO.highways_usa, ESRI.DBDO.rail100k_usa, ESRI.DBO.airports_usa and ESRI.DBO.dtl_riv_usa. To make this data relevant to Suffolk County, I clipped all the features, only keeping data within the county boundary.

Now I was able to start my vector analysis. To start my analysis I used the select by attribute tool on the cities_clip feature class to find a city with more than 20,000 people. This querying answered the first part of my criteria, creating the feature class cities_clip_select. Next I used the buffer tool to find a city within 1 mile of a highway. Within the buffer tool, I set the distance field to 1 mile. To this new feature class (highways_buff) I used the dissolve tool to remove all intersecting boundaries creating the feature class highways_final. For the other 3 feature classes (airports_clip, dtl_riv_clip and rail_clip) I used the buffer tool, setting the distance field to the distances specified in the criteria, and dissolving all internal boundaries.

From this analysis I intersected the following feature classes: cities_clip_select, highways_final, rail_final and airports_final creating the feature class ra_hi_ai_ci_int. As I wanted to live at least 5 miles away from a river I used the erase tool, allowing me to exclude areas too near to a river. Within the erase tool, I set the input feature to ra_hi_ai_ci_int and the erase feature to dtl_riv_final. After this tool had run, all that was left was the ideal place for me to live (final_place feature class).

After completing the vector analysis, I created a data flow model (Figure 5.1) and two maps; a main map with the vector analysis and a locator map. In my main map I used the following feature classes: Suffolk County, airports, railways, highways, rivers and ideal place. In a separate data frame I created the locator map, including the state (had to create using ESRI.DBO.states feature class), county and ideal place feature classes. Once I had made both map, I placed them on a single layout using layout view, with the main map being significantly larger than the locator. For both maps I added a legend and checked the projection was NAD 1983 NSRS2007 StatePlane New York Central FIPS 3102 (Meters). To make the maps look more cartographically pleasing I changed the symbology of the represented data. To the main map I added a title, north arrow and scale. For the overall project (containing both maps) I added the source of my information, the projection used and my name.

Figure 5.1

Results:
Figure 5 shows the ideal place for me to live in Suffolk County, New York. According to Figure 5 the best place for me to live based on the criteria I set is Holbrook. After doing some further research I found out that Holbrook is just over an hour from New York City by car and 1 hour and 30 minutes by train, making it an ideal location to live.

Figure 5

Evaluation:
I thought this project was interesting to do as it brought together all the skills we had learnt over the semester in GIS I and applied it to a real scenario. I enjoyed this project as it allowed me to pose a question, and answer the question using vector analysis with minor consulting from the professors to check my data flow model was correct. If I was asked to repeat this project instead of doing the analysis at the county level, I would want to do this analysis at the state level. I think this would be better as it would give me a wider variety of places to live in New York state rather than just one. I think if I were to repeat the process, I would set one of my criteria to live within 35 miles of New York City. This in an important factor for me as it would make commuting and working in New York City viable, whilst living somewhere relatively affordable in comparison to New York City. The main problem I faced in this project was not having up to date data. If I were to do this project again I would try to find more up to date data either using the US Census Bureau or NYSGIS Clearinghouse. However, even with this slight problem I thoroughly enjoyed doing the project as it gave me the freedom to explore something of interest to me and use the skills I had acquired from this class.

Sources
US Department of Commerce, US Census Bureau. (2014) American Fact Finder Advanced Search. Retrieved from http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t.
New York State, NYSGIS Clearinghouse (2014) Advanced Search. Retrieved from https://gis.ny.gov/gisdata/search.cfm.
Google. (2014) Google Maps. Retrieved from https://www.google.com/maps/dir/Holbrook,+NY/New+York,+NY/@40.7480997,-73.8106997,10z/data=!3m1!4b1!4m13!4m12!1m5!1m1!1s0x89e84816d2dd97db:0x88da38cf7c965967!2m2!1d-73.0784429!2d40.8123205!1m5!1m1!1s0x89c24fa5d33f083b:0xc80b8f06e177fe62!2m2!1d-73.9780035!2d40.7056308.
Google. (2014). Google Maps. Retrieved from https://www.google.com/maps/dir/Holbrook,+NY/New+York,+NY/@40.7555407,-73.8106997,10z/data=!3m1!4b1!4m14!4m13!1m5!1m1!1s0x89e84816d2dd97db:0x88da38cf7c965967!2m2!1d-73.0784429!2d40.8123205!1m5!1m1!1s0x89c24fa5d33f083b:0xc80b8f06e177fe62!2m2!1d-73.9780035!2d40.7056308!3e3.
ESRI.

Lab 4: Vector Analysis with ArcGIS

Goals and Background:
The goal of this lab was to learn to use various geoprocessing tools for vector analysis in ArcMap 10.2. The aim of Lab 4 was to determine suitable habitat for bears to live in, in Marquette County, MI, and to create a map based on this vector analysis.

Methods
For this lab I had to create a map regarding the vector analysis I had done on bear habitat in Marquette County, MI. I also had to include a locator map to enable the reader to understand where the analysis took place. To create the main map and locator map I used ArcMap 10.2. I put the main map and locator map in separate data frames. Each data frame used the basic study area shapefile provided to us. The locator map also used the state of Michigan shapefile and Marquette County shapefile.

Before I started my vector analysis, I had to give the bear_locations_geog$ excel sheet XY coordinates to be able to use the data in ArcGIS. I was able to do this by adding the XY coordinates as an "event theme" in ArcMap 10.2. I exported this 'new' data to my lab4 geodatabase, titling the feature class bear_locations. In ArcMap 10.2 I added all the feature classes in the bear_management_area feature dataset to the data frame with just the study area shapefile, and changed the symbology of the landcover feature class to a unique colour map based on the MINOR_TYPE field.

Now I was able to start my vector analysis. To start my analysis I joined the bear_locations and landcover feature classes using a spatial join. I then used summarise to determine the top 3 land cover types in which bears were found; these were mixed forest land, evergreen forest land and forest wetlands. After finding out the types of land cover the bears lived in, I wanted to find out if bears lived near streams or not. In this lab, near was defined as within 500m. To find out this data I used the buffer tool on the streams feature class. Within the buffer tool, I set the distance field to 500m and dissolved all intersecting boundaries. This new feature class (streams_buff) was intersected with the bear_cover feature class to determine the percentage of bears within 500m of a stream. In total 49 out of 68 bear locations were within 500m of a stream (72.1%), making it an important habitat characteristic.

Once this information had been processed I had to find the most suitable areas for bear habitat. I already knew the top 3 land cover types for bears, however I needed to create a separate feature class. I created this feature class (landcover_select) using the select by attribute tool. To find out if any of these suitable areas were within 500m of a stream, I used the intersect tool. I intersected the landcover_select and stream_buff feature classes. To remove internal boundaries in this newly created feature class (suitable_habitat) I used the dissolve tool.

After gathering this information, I had the task of finding the most suitable bear habitat within the Michigan DNR management lands. To find out this information I used the clip tool. Within the clip tool, I set the input feature as the dnr_mgmt and the clip feature as suitable_habitat (feature class made in objective 3), creating the feature class dnr_mgmt_clip. I used the dissolve tool on the dnr_mgmt_clip feature class to remove internal boundaries, creating the dnr_mgmt_habitat feature class.

The final task of this lab was to find bear management areas away from urban or built-up land. In this lab, bear management areas had to be at least 5km from an urban or built-up area. For this final task, I first used the select by attribute tool to determine where the urban and built-up areas of land were. This data was made into a feature class (landcover_select2). To determine areas further than 5km away from an urban or built-up area, I used the buffer tool on the landcover_select2 feature class, creating the feature class landcover_select2_buff. Finally to find the ideal bear habitat I used the erase tool, as this would exclude areas within the buffer zone. Within the erase tool, I set the input feature as dnr_mgmt_habitat and the erase feature as landcover_select2_buff. After this tool had run, all that was left was the ideal bear habitat (final_dnr_mgmt_habitat feature class).

After completing the vector analysis, I created a data flow model (Figure 4.1) and  two maps; a main map with the vector analysis and a locator map. In my main map I included the study area, bear locations, stream, ideal and all bear habitat feature classes. In a separate data frame I created the locator map, including the state, county and study area feature classes. Once I had made both maps, I placed them on a single layout using layout view, with the main map being significantly larger than the locator map. For both maps I added a legend and used the NAD 1983 HARN Michigan GeoRef (Meters) projection. To make the maps look more cartographically pleasing I changed the symbology of the represented data. To the main map I added a title, north arrow and scale. For the overall project I added the source of my information and my name.

Figure 4.1

 

Results
Figure 4 shows the ideal areas for bear habitat in Marquette County, in which the DNR could establish bear management areas. Figure 4 shows that there are very specific areas in the county where there are ideal bear habitats. The majority of bears according to my map are found in neither ideal nor all bear habitat areas, which is slightly concerning as it reduces the protection of the animal.

Figure 4

 

Source

Michigan Department of Technology, Management & Budget. (2002). Michigan Geographic Data Library. Retrieved from: http://www.mcgi.state.mi.us/mgdl/.



Lab 3: Introduction to GPS

Goals and Background
The goal of this lab was to learn how to use a GPS unit (Trimble Juno), how to create and upload a geodatabase onto a GPS unit and learn to collect data in the field using the Trimble Juno GPS unit. The aim of the lab was to create a map based on the data collected in the field (UWEC campus), as well as creating a cartographically pleasing map using digitising if necessary.

Methods
For this lab I had to create a geodatabase to collect the necessary data in the field as well as a map representing this data.

I created my geodatabase in ArcCatolog 10.2 with point, line and polygon feature classes (one practice and one real feature class for each type). Each feature class used the coordinate system NAD 1983 HARN Wisconsin TM (Meters). I also imported a shapefile of the buildings on campus and a raster image of UWEC campus to the geodatabase. Once I had created the geodatabase I opened ArcMap 10.2. In ArcMap 10.2 I imported the geodatabase created in ArcCatalog 10.2. To identify the different feature classes, different colours were assigned to each symbol.

To be able to use this geodatabase in the field, I used ArcPad Data Manager (Get Data for ArcPad button) in ArcMap to transfer ('check out') the geodatabase onto the Trimble Juno GPS unit. Once the data had been transferred onto the Trimble Juno GPS unit, I was able to go outside and collect the data. To make sure I was collecting the data correctly, I did a practice run, once satisfied, I collected the data as instructed by the professor. I collected 4 polygons, 1 line and 6 points whilst out in the field and entered attribute information for every feature I collected e.g. P1 for Polygon 1 and T1 for Tree 1.

After finishing collecting data in the field, the collected data was transferred back to ArcMap 10.2 using ArcPad Data Manager. This 'checked in' data was used to create my final map. Each feature was assigned a different symbol and colour for example a tree and the colour green for the tree feature class (3 of the points). To improve the quality on the polygons, I digitised them. I decided to change the basemap, to a more updated version of UWEC lower campus. I found this updated basemap using Add Data from ArcGIS Online, typing in the keyword Eau Claire. Once satisfied with the map, I placed the map on a single layout using layout view. To this map I added a title, scale, north arrow, legend, date, source and my name.

Results
Figure 3 shows the map I made for this lab. Figure 3 shows the problem with GPS, as data collected is not always that accurate. This is especially true for the polygons I recorded, as they were all slightly off the basemap below. To reduce this level of error I digitised some of the polygons to make them more precise, however they still are not perfect (as shown in Figure 3).

Figure 3

Sources

GPS Data Collected by Amelia Fitzpatrick 14/4/2014

Basemap: UWEC Campus



Lab 2: Downloading GIS Data

Goals and Background
The goal of this lab was to learn how to use the US Census Bureau and to be able to download and map data from this website. The most important aspect of the lab was learning to download data from the US Census Bureau and converting this data to a ArcGIS friendly format. The aim of Lab 2 was to create two maps, one following instructions provided to us (by our professor) and the other using data of our choice.

Methods
For this lab I had to create 2 maps on data regarding Wisconsin's counties. The first map I made was on total population, whilst the second map I made was on housing units. To create these two maps I used ArcMap 10.2. Each topic e.g. total population was put in a separate data frame. Each data frame used the basemap Light Gray Canvas and had the state of Wisconsin with counties shapefile (downloaded from the US Census Bureau).

To get the data needed to make these maps I went to the US Census Bureau website. For the first map in the Topics option section I choose People, Basic Count/Estimate, Population Total. To make the data specific to Wisconsin, I changed the Geography option section to County - 050, Wisconsin, All Counties Within Wisconsin. I choose to download the Total Population 2010 SF1 100% Data. This information was downloaded in tables as a zip file. To access the data I unzipped the data. To make the data in the total population table readable in ArcMap I saved the data as a Microsoft Excel Workbook. To be able to make a map I had to link the data table (total population) with the shapefile table using the GEO#id. Once these two tables had been linked I could map the data. To show this data I decided to use the graduated colour map (various shades of pink) in the Symbology tab of the Layer Properties tab. I decided to change the classification to Quantile as I felt this better represented the data.

After completing the first map I created another map as required in a separate data frame. For this map I decided to download housing units data. To find this data I changed the Topics option section to Housing, Basic Count/Estimate, Housing Units. I kept the Geography tab the same. I choose to download the Housing Units 2010 SF1 100% Data. After choosing the data I wanted to download, I followed the same process as above to create the map. Again I decided to change the classification to Quantile as I felt again this represented the data better.

Once I had made both maps, I placed them on a single layout using layout view. For both maps I added a title, scale, north arrow and legend. For the legend on both maps I decided to change the units to have no decimal places (only whole numbers) and used thousands separators to make the numbers easier to read. For both maps I changed the projection to NAD 1983 (2011) Wisconsin TM (Meters) as I wanted the maps to be projected using a more local projection. For the overall project I added the source of my information, the year of the source data and my name.

Results
Figure 2 shows the maps I made in this lab. The total population map shows that Wisconsin is more densely populated in the South than in the North of the state. In counties where there are major cities such as Wasau there are higher populations. From further research the North of the state is predominantly forests explaining why the North of the state is less populated than the South of the state, fitting the pattern suggested above. The housing units map show that most houses are found in counties where there is a large city e.g. Milwaukee. However, compared to total population there are a lot more housing units in the Northern counties. This is probably due to many Wisconsinites having a second home, a cabin in this region.

Figure 2

Sources

US Department of Commerce, US Census Bureau. (2014). American Fact Finder Advanced Search. Retrieved from http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t.

Google. (2014) Google Maps. Retrieved from https://www.google.co.uk/maps/place/Wisconsin/@45.9383265,-89.981085,8z/data=!4m2!3m1!1s0x52a8f40f9384e3af:0xf2d5d5b8f88649d6.



GIS I Lab 1: Base Data

Goals and Background
For this lab I was working as an intern for the company Clear Vision Eau Claire. Clear Vision Eau Claire have created a partnership with local developers, UW-Eau Claire and the Eau Claire Regional Arts Center to create a new development called the "Confluence Project". The Confluence Project will include the following facilties: student housing, a community arts centre, a commercial retail complex and public parking, located in downtown Eau  Claire with construction beginning late 2013/early 2014. The goal of Lab 1 was to learn about spatial data sets used in land use, public land management and administration and to be able to create maps showing this various information specific to the Confluence Project.

Methods
For this lab I had to create 6 maps on various information to do with the Confluence Project including civil divisions, census boundaries, PLSS features, Eau Claire city parcel data, zoning and voting districts. To create these maps I used ArcMap 10.2. Each topic e.g. civil divisions was put in a separate data frame. Each data frame used the basemap Imagery and had the proposed site on it. For every data frame I put in the class features using ArcCatalog, using the data provided to us.

For the Civil Divisions map I used the civil divisions and county boundary (Eau Claire) feature classes . To show the different civil divisions I used the unique values map (using the municipality type value field) and set it to 70% transparency to be able to see the basemap below. To show the county boundary I used the colour 40% grey. For me to be able to see the whole city of Eau Claire I used the scale 1:40,000, and added a callbox to make the proposed site clear.

For the Census Boundaries map I used the tracts and BlockGroups feature classes. For the BlockGroups feature class I decided to use population per square mile (2007) to show the density of the area surrounding the site. For this data I used a graduated colour map with the natural breaks classification. To be able to see the basemap I set the transparency to 30%. For the tracts I used the colour black, to make them show up clearly.

For the PLSS Features map I used the PLSS Quarter Quater Section feature calss. To be able to see the basemap underneath I hollowed out the PLSS Quarter Quater Section feature calss and used a bright green colour as an outline to still be able to see the sections.

For the Eau Claire City Parcel Data map I used the centerlines, parcel area and water feature classes. To be able to see the centerlines and parcel area I used contrasting colours (pink and yellow). For the water I used a dark blue and set the transparency to 40%, to still be able to see the river underneath.

For the Zoning map I used the centerlines and zoning areas feature classes. To show the various zones I used the unique values map and the value field zoning_cla. I grouped the zones together based on the starting letter e.g. C = Commercial. Each zone on the map has a different colour. To contrast the zoning classes colours, I used yellow for the centerlines.

For the Voting Districts map I used the voting wards 2011 feature class. I changed the voting wards colour to orange, the outline to black and the transparency to 60% to be able to see the basemap below. I added the ward numbers using the labels tab and added a halo to the numbers to make them clear. For me to be able to see the proposed site I added a callbox.

Once I had made all 6 maps, I placed them on one a single layout using layout view. To every map I added a title and scale. For every map except Voting Districts I added a legend as well. For the overall project I added the source of my information and my name.

Results
Figure 1 shows the maps I made for this lab. The Civil Divisions map shows the Confluence Project site is located in the city of Eau Claire with towns surrounding it (according to the map). The Census Boundaries map shows that the Confluence Project site is situated  in a densely populated part of the city per square mile. The PLSS feature class identifies what PLSS Quarter Quarter the proposed site sits in. The Eau Cliare City Parcel Data map shows the relationship between the Confluence Project and other parcels in the city of Eau Claire. The Zoning map shows the Confluence Project site is located near the CBD and public properties district meaning this site would bring a new zone to the area (residential). The Voting Districts map shows that the Confluence Project site is located on the edge of voting district 31.

Figure 1

 

Sources

University of Wisconsin - Eau Claire. (2013, October 23). Frequently Asked Questions: The Confluence Project. Retrieved from http://www.uwec.edu/News/more/confluenceprojectFAQs.htm.