Lab 5a - Intro to ERDAS

Lab 5a - Intro to ERDAS

This week's lab introduced us to the ERDAS Imagine software.   This is a very powerful piece of sofware that we will use through the remainder of the course to better understand remote sensing.

This week we took some of the data we worked on in ERDAS, exported it out to ArcGIS and created this map.

Ground Truthing

Ground Truthing

This week, we took the data we collected last week and used Google Street view to "Ground Truth" it to perform an Accuracy Assessment.   On this map you will see where the green dots are, the Google Street view matched my classification.   The red dot indicate places where my map differed from what I saw in Google Street view.  

Network Analyst - Prepare Week

Network Analyst - Prepare Week

Here is my base map that I created for this week's assignment.   This map will be used for follow up studies of the Network Analyst tools in order to help prepare and evacuation route for the Tampa area in the event of a hurricane.   Based on the ESRI samples I looked through while preparing for this assignment, I can tell that I will really enjoy the Network Analyst tools.

GIS4035 Photo Interpretation and Remote Sensing Lab 3

Here is my map showing the Land Use and Classification of a study area.   This project taught me some good tools for data editing within ArcGIS.   This particular map only addresses Level I and Level II classifications.   It is easy to see how the complexity grows as you go deeper into subcategories....

Special Topics in GIS - Lab 2 Statistics -Analyze

This week's assignment involved doing some statistical analysis on the data we put together last week in relation to meth lab locations.  We used Ordinary Least Squares Regression which was a first for me.   I am not sure I mastered it, but hopefully I'll get some good feedback on my assignment to learn more about this powerful tool that GIS users have at their disposal.

For the blog, we are asked to post our OLS Results table and StdResiduals Map.   Here they are:

Remote Sensing - Module 2 Lab

In this week's lab we had to create 2 maps.   The first map was a study in Tone and Texture.   You can see the different intensity levels in both tone and texture in this map:

 

The next map looked at identifying features based on shape/size, shadows, patterns, and associations.

GIS 4930 Project 1 - Statistical Analysis with ArcGIS

For our first project of this new semester, we will be looking for information to help law enforcement determine potential hotspots for meth activities.

Or task this week was to create our Base Map of the Study Area, as well as generate outrIntroduction/Background information.   We also had to do a Process Summary and provide the results of that work. 

Here is my base map:

Here is Section 3 of my Process Summary:

Prepping the Census Data: Programming with Python
Include notes in this section about how and why to prepare your script in python.
1) Added the CensusTracts data to a new MXD
2) Opened the attribute table
3) Added field pcntPopGro as Float
4)  Used the field calculator to set this to (( [POP2010] - [POP2000]) / [POP2000]) * 100
5) Added field pcntWhite as float and used field calculator to set it to ( [WHITE] / ( [WHITE]+ [BLACK]+ [AMERI_ES]+ [ASIAN]+ [HAWN_PI] + [OTHER] + [MULT_RACE] + [HISPANIC])) * 100
6) Added field Roomates as float ans set to be( [HOUSEHOLDS] - [HSEHLD_1_M] - [HSEHLD_1_F] - [MARHH_CHD] - [MARHH_NO_C] - [MHH_CHILD] - [FHH_CHILD])
7) Next we opened up the AddCalcFieldsShell.py script in IDLE
8) Set my workspace to point to S:\GIS4930-GISSpecialTopics\Project1\Project1Data.gdb so the whole line is :   env.workspace = r'S:\GIS4930-GISSpecialTopics\Project1\Project1Data.gdb'
9) Prepared the last 5 lines as follows:
# pcnt40_49
arcpy.CalculateField_management(censusFC,"pcnt40_49","([AGE_40_49]/[POP2000])*100", "VB")
# pcnt50_64
arcpy.CalculateField_management(censusFC,"pcnt50_64","([AGE_50_64]/[POP2000])*100", "VB")
# pcnt65_UP
arcpy.CalculateField_management(censusFC,"pcnt65_UP","([AGE_65_UP]/[POP2000])*100", "VB")
# M_F_Ratio
arcpy.CalculateField_management(censusFC,"M_F_Ratio","([MALES]/[FEMALES])", "VB")
# pcntUnEdu
arcpy.CalculateField_management(censusFC,"pcntUnEdu","([NotHSGrad]/[EduPop]) * 100", "VB")
10) Saved the MXD, closed ArcGIS and ArcCatalog then ran the script.
11) I had one typo in my script so fixed that and reran it.
12) Opened up ArcCatalog to make sure it looked right.   I wasn’t sure if we needed to multiply by 100 on the male female ratio since it is a ratio rather than a percentage.    I decided I can always multiply by 100 down the road if the lab leads me that way.
13) Looks like all fields were created and populated


Part 2: Join Meth Labs to Census Blocks
Include notes in this section about how and why it was important to join the meth lab and census data. Explain any edits you had to make to the data.
1) Opened up my MXD Again
2) Opened the Spatial Join tool from the Analysis toolbox
3) Verified results
4) This created CensusTract_SpatialJoin layer which has the Join_Count field in it
5) Created a new field called LabDensity as type Float
6) Set this using Field Calculator to equal [Join_Count]/ [SqMile]
7) I found the properties page to be an easier way to toggle fields on and off: