Vector Analysis 2

This week is the second phase of vector analysis; the first part of this analysis does not require a blog post. I learned how to use two of the most common modeling tools in ArcGIS - Buffer and Overlay. I also created three buffer zones using ArcPy scripts. The primary deliverable for the vector analysis is a map showing the possible campsite locations, roads, lakes and rivers in De Soto National Forest in Mississippi. Using Buffer & Overlay I displayed the locations that were within 500 meters of a river, 150 meters of a lake, and 300 meters of a roads. Conservation areas were excluded from the site selection.

What is the Overlay tool-set? This is a set of tools that allow an analyst to combine, erase, modify or update the feature and information from multiple thematic layers to create a new layer for continued analyses. I also used the Union tool to combine two different layers, the Intersection tool to identify areas common to two layers rather than combining them, and an Erase tool to remove unwanted areas from a layer.

What is the Buffer tool? This tool is used to create a buffer area around an object (a point, line, or polygon).Specific distance (buffer distances) around an object (a point, line, or polygon) can be set for analyses purposes. In the De Soto National Forest below, I apply buffers to the roads and water objects in order to deduce the areas common to both elements.

The map above shows the possible campsites in the De Soto National Forest in Mississippi.

Mod9 - Flow Line Mapping

Flow Line Map of Immigrants to the United States in 2007.

I used the accurate placement method to create this Flow Line map. Accurate placement methods is style of creating flow line map that involves the placement of arrow headed lines that depict the direction of flow of people, ideas and commodities.

This map shows a Distributive  flow map of Immigrants to United States of America from all the regions in the year 2007.

To create this map, I used the imported base map from ESRI and an Excel spreadsheet of immigrants statistical data from the U.S. Department of Homeland Security's Office of Immigrant Statistics. 

I import the ESRI base map into Adobe Illustrator and created additional Layers from Flow Lines, Map Title, Legend, Region Names, and After Effects.

I then design the map to the standard specified in the lab instruction using tools available in AI. After creating the flow lines pointing towards the U.S. choropleth map, I then open the Excel document containing the Immigrants' statistical data, calculate the square root of the 2007 immigrants in each region in one column, and the width of line symbol in another column.

I then proceed to use the result of the calculations to re-size thickness of the flow line such that the flow lines are proportional to the amount of people from each region. I finalize the map by adding the Drop Shadow and Inner Glow effects to the map heading and flow lines respectively.

I really enjoy making this making this map and feel fulfilled using AI successfully after many years of avoiding it.

Data Search Lab 7 & 8

Week 7&8 Lab was a fun and challenging data search and download project. The primary objective of this lab is for students to learn where to access and download data (free data sources), project the layers, and how to clip both Vector and Raster data. The following maps vector, raster and environmental datasets.

My major takeaway from this lab is what ArcTool clipping tool to use for Raster and Vector datasets. I clipped the Vector datasets using Clip(Analysis) tool and Clip(Data Management) the Raster datasets.

The lab required students to download nine data layers sets for the creation of 1-3maps for individually assigned County in Florida. I was assigned Okechobee County.

After downloading all the required datasets, I then proceeded to clip all layers to the Okeechobee County. I obtained the aerial imagery from LABINS.org and other map data came from past class labs and FGDL.gov.

Map 1 shows the elevation model (DEM) of the entire Okechobee county.

I am surprised at the number of invasive species at the "mouth" of Lake Okeechobee on the lower half of the map on the right. Studies conducted by the US Army Corps of Engineers revealed that "there are approximately 50,000 non-native species and approximately4,300 are considered invasive." Source: http://www.saj.usace.army.mil/Missions/Environmental/InvasiveSpecies.aspx

Map 2 comprises of two maps in one. Invasive Species in Okeechobee Co. and the county's hydrology

                         

Projection Part 2

Week 6 of Intro2GIS Lab focused on how to download datasets, project and define the downloaded datasets in order to obtain a uniform layer definition and projection. I also learn how to manipulate Tabular XY data in Excel format and incorporate the XY data into my map via AcrMap in order to create shapefile.

I acquire the necessary datasets from the following sources:
               1. www.LABINS.org
               2. Florida Geographical Data Library web portal, and
               3. Florida Department of Environmental Protection (the XY data)

This Lab further strengthened my knowledge of how to download data; where to fine the right data; how to project or re-project where the need arises, and how to define and redefine.

I had trouble differentiating Projecting from Defining but after careful repeated practice, I grasp the difference between both and the purposes of it.

I carefully examined the provided examples in Part 1 and Part 2 of the lab and then proceeded to to Part 3 where I downloaded the data needed for the county. I then proceeded to project all layers to a uniform Coordinate System(NAD83 State Plane FIPS 903 Feet). I verified the coordinate system to ensure that the reprojection was executed as expected by the system.

The final map created contained the following:

• Basemap
• STCM sites
• Major Roads
• County Boundary
• Aerial Imagery (for Quad5659 Century, FL. (DOQQ: q5659ne, q5659nw, q5659se, q5659nw)) 
• MRG5659

Multi-Layered map in ArcGIS

Defining

Projecting

STCM (Tabular XY Data)

Mod5 - Spatial Statistics

Spatial Statistics

This week lab assignment focused on Statistical analysis of Spatial data sets. The objectives of this lab assignment are: 

1.  Learn how to analyze web downloaded spatial data using the Spatial Statistical Toolbox and Geostatistical Analyst Extension, in calculating the mean, median, and generating the directional distribution, and trend analysis graph of the identical patterns in the given data.
2. I also learn how to use histogram, normal QQ plot, semivariogram cloud and voronoi map to find outliers in a data set.
3. Learn key statistical/mapping terms such as: Brushing, Linking etc.
4. Learn to visually analyze the data.

The Spatial statistics lab was completed using the data and instructions in ESRI's "Exploring Spatial Patterns in Your Data Using ArcGIS" training.

To complete this lab, I downloaded the data folder to my S: drive, install the executable file and ran the it. I then open the map in ArcMap, searched for Mean Center using the Search Tool in ArcMap. The Mean Center generate the average location of the data set. I followed the same process to determine the Median Center. As can be in the above map, the green dot and red pentagon represent the Mean Center and Median Center respectively. 

In the spatial statistic tool under ArcTool box in ArcMap, I use the Ellipse tool to create the black-banded-yellow ellipse over the data set. This ellipse is the Directional Distribution of the data set. The data sets are east-west oriented.

I further explored the Geostatiscal Analyst tool and created a Voronoi map, semivariogram cloud, a histogram and normal QQ plot. The histogram, voronoi map and semivariogram cloud produce a visual detail of a normally distributed map that will aid me to identify the outliers in the data set.

This lab shows me that an entropy voronoi map display to local variation in the data set, the normal QQ plot display how closely the data sets display the characteristics of the data distribution are, while the histogram display the frequency of the data value. 

Below are sample Histogram, Normal QQ plot, Moroni map, Semivariogram/Covariance Cloud and Trend analysis maps for the western and central Europe temperature analysis.

Histogram

Normal QQ plot

Vonronoi Map

Trend Analysis

Semivariogram/Covariance Cloud

Map Projection Part 1

The above map shows the three different map projection. Why do we project map? Maps are projected because of the need to represent the surface of the earth as accurately as possible. Different methods of Map projections have been developed and adopted over time but this week the class project focuses on Albers, UTM 16 N, and State Plane using Florida counties as the case study for the projections.

We zeroed in on four counties in Florida (Alachua, Escambia, Miami-Dade, and Polk) and compared the area size and shape that the three projection formats generated.

Sharing Top 10

This week lab project required that I generate a Top 10 List and then share the location based list on three different map formats - Google Earth, ArcMap, and ArcGIS Online.

As stated in the objective of this Lab assignment, the first thing I did was to search for and locate internal data and external data from ArcMap. The major internal data search for and use in this project is the World Streets base map. I also created and external data - my Top 10 List. I created an Excel file of the "Top 10 List of Engineer Program in Florida Colleges" with the 500 miles radius of zip code 32514. I categorized the list by Name, Rank, Zip Code, URL, and Address of the schools. I created all the external data in the Excel file using information gathered from the U.S. Department of Education website and respective Colleges and Universities web portal on my list.

After creating the Excel file of the Top 10 List, I created a geocoded map layer - a shape file of the 10 locations on my list. I went ahead to publicly share my map via ArcGIS Online after designing in ArcMap.  A KML (kmz) file was created in order to share the Top 10 List map publicly via Google Earth.

I really enjoy this lab because I learn how to create external data, integrate it into a map. I also learn how to share map on three different formats but most importantly, I learn about the characteristics of each format.
Some format are Functional, and can be easily shared while others offer limited flexibility and control due in part to licensing and propriety factors.

Here is the link to my Top 10 List of Engineering Programs shared via ArcGIS Online:

http://arcg.is/20MMOQW