GIS 4035: Photo Interpretation and Remote Sensing: Module 5A – Intro to ERDAS Imagine

Module 5A:  Intro to ERDAS Imagine and Digital Data 1

This week’s lab involved the calculating of wavelength, frequency, and energy of EMR, locating and using the basic tools in ERDAS Imagine, using the Viewer to view data in ERDAS Imagine, creating a subset data in ERDAS Imagine, and making a map with the subset data in ArcGIS.

The map below represents the selected subset data from ERDAS Imagine with corresponding classes and area (acreage).

GIS 4930: Special Topics in GIS Module 2 - Prepare Week

Module 2:  Mountain Top Removal – Prepare Week

This week’s lab involved the use of a Python script for geoprocessing of raster datasets with the Mosaic Raster Toolset to define an area and the Hydrology Toolset to create a hydrology dataset.  In addition, ArcGIS Online was used to create a Story Map and a Map Journal.  The mountain top removal study area for Group 4 covers the Appalachian Coal Region of West Virginia.

The link below represents the Six (6) Stages of Mountain Top Removal Story Map:

The Six (6) Stages of Mountain Top Removal

The link below represents the DRAFT of the Mountain Top Removal Map Journal:

DRAFT Mountain Top Removal Map Journal

The map below represents the study area:

GIS 4035: Photo Interpretation and Remote Sensing: Module 4 – Ground Truthing and Accuracy Assessment

Module 4:  Ground Truthing and Accuracy Assessment

This week’s lab involved the plotting of 30 systematic points for ground truthing and accuracy assessment with Google maps as compared to the previous classification of features in a natural color aerial photograph of Pascagoula, MS using the USGS Standard Land Use Land Cover Classification System.  The features were initially identified on the ground based size, shape, color, pattern, shadows, and association.

The map below represents the accuracy assessment as described above.

GIS 4930: Special Topics in GIS Module 1 - Report Week

Module 1:  Network Analyst – Report Week

This week’s lab involved designing a pamphlet with evacuation routes/information from Tampa General Hospital for the general public, designing emergency supplies delivery route maps with turn-by-turn driving directions, and designing informational maps for the media to inform the public of evacuation routes and shelter locations.

The maps below represent some of the scenarios described above for Tampa, Florida.

GIS 4035: Photo Interpretation and Remote Sensing: Module 3 – Classification Mapping

Module 3:  Land Use Land Cover Classification Mapping

This week’s lab involved the classification of features in a natural color aerial photograph of Pascagoula, MS using the USGS Standard Land Use Land Cover Classification System.  The features were identified on the ground based size, shape, color, pattern, shadows, and association.

The map below represents the land use land cover codes applied to the features as described above.

GIS 4035: Photo Interpretation and Remote Sensing: Module 2 – Visual Interpretation

Module 2:  Visual Interpretation

This week’s lab involved the identification of features in aerial photographs by several visual attributes such as:

·         Tone

·         Texture

·         Shape and Size

·         Shadow

·         Pattern

·         Association

In addition some features were identified based on true color and compared against a False Color IR image.

The maps below represent the features identified as described above.

GIS 4930: Special Topics in GIS Module 1 - Analyze Week

Module 1:  Network Analyst – Analyze Week

This week’s lab involved using Network Analyst to create various evacuation and supply route scenarios using flooded streets as restricted segments and considering elevation and anticipated levels as Scaled Costs.  Scenarios include:

•               Nearest shelter zones

•               Supply routes from National Guard armory to shelters (schools)

•               Evacuation routes from Tampa General Hospital

•               Evacuation routes from downtown Tampa,

The map below represents the scenarios described above as Hurricane Preparedness Guide for Tampa, Florida.

  

GIS 4930: Special Topics in GIS Module 1 - Prepare Week

Module 1:  Network Analyst – Prepare Week

This week’s lab involved organizing and preparing data for a flood zone study area of Tampa, Florida.  The data was clipped, re-projected, and reclassified to create a basemap and map package including metadata files.

The map below represents flood zone study area of Tampa, Florida.

GIS 4048: Final Project

Final Project:  Identify Locations with Special Circumstances for Informational Campaign

This final project involved performing location analysis of American Survey Community block groups in the City of Oxnard, Ventura County, California with special circumstances (selected criteria) to be considered in the crafting of an information campaign for a new citywide tax district.  The selected criteria included:

·         Areas far from City Hall.

·         Areas far from City maintenance yard.

·         Areas with a large population.

·         Areas with a high rate of population below the poverty line.

·         Areas with a high rate of limited English language proficiency.

The location analysis included the use of the Weighted Overlay tool resulted in two maps representing all criteria individually, all criteria weighted equally against each other, and the poverty and limited English criteria preferred over other criteria. 

Final Thoughts:

The project was interesting and applicable to the work I do as a consultant assisting public agencies to create and manage tax districts.  I am satisfied with the results.  In hindsight, I would have liked to have used more current data (instead of 2013) and if possible it could have been obtained privately at extra costs.  The next deliverable on this project would have been a close up version of the selected ACS block groups with suggested locations for public informational meetings nearby such as public schools.  This analysis could be performed with network analyst to determine the most accessible locations for the meetings within or near the selected ACS block groups.

Great class!  Learned a lot, but I am looking forward to the break between Summer and Fall semesters – I am happy to have my weekends back!

Link to my final project presentation is found below:

Click Here for Final Project Presentation

GIS 4102: GIS Programming Module 11

Module 11: Sharing Tools

This week’s lecture covered methods for distributing tools, considering licensing issues when distributing tools, using a standard folder structure for sharing tools, working with paths, finding data and workspaces, creating a geoprocessing package, embedding (importing) script tools, setting tool password protection, and creating documentation for tools.

This week’s assignment involved editing a script tool, embedding the script into the tool, and sharing it. The assignment included:

·         Creating variables with sys.argv[] expressions (instead of GetParametersAsText()) to capture parameters from the tool dialog.

·         Editing the tool’s Item Description to add documentation that shows in the tool dialog.

·         Embedding (importing) the script into the tool.

·         Setting a password to prevent an unauthorized user from viewing, editing or exporting the script.

·         Sharing the Toolbox (without the script as it was embedded into the tool).

Below are screenshots of the Tool’s dialog window in ArcMap and successful map result after running the Tool.

GIS 4102: GIS Programming Participation Assignment No. 2

Participation Assignment No. 2

GIS as a Job Growth Area for IT Professionals

World of Computer Science and Information Technology Journal (WCSIT)

ISSN: 2221-0741

Vol. 5, No. 6, 98-111, 2015

Link: http://oaji.net/articles/2015/567-1431504311.pdf 

This study focuses on GIS as a job growth opportunity as companies take advantage of the benefits of geospatial data.  Careers in the field of GIS require knowledge of geography, cartography, and information technology (“IT”).  In addition, geospatial data requires storage, management, analysis, and dissemination.  These skills provide opportunities for IT personnel in the GIS industry as network, database, cloud computing, and development technologies change and grow.

The GIS field has not been known as a growth area to IT professionals, in part because job postings do not clearly match the skills of IT professionals with the skills required for GIS job openings.  This study evaluates job postings in Monster.com, Indeed.com, CareeBuilder.com, GJC.org, and GISJobs.com for four positions in the GIS field.  The postings were rated and compared to similar postings in other job boards. 

The results varied significantly even among similar job postings.  The inconsistency on job postings may mislead prospective candidates into thinking they don’t have the skills required for the position when they often do.  This study highlights the need for the industry to set standard job descriptions and requirements for positions in the GIS field to help those seeking to match their skills set with the job opportunities. 

GIS 4102: GIS Programming Module 10

Module 10: Creating Custom Tools

This week’s lecture covered creating custom tools, editing tool code, setting tool parameters, working with tool messages, and customizing the tool progress information.

This week’s assignment involved creating a script tool from a Python script that clips multiple layers at once.  The assignment included:

·         Creating a new custom Toolbox.

·         Adding a Script Tool from the Python script.

·         Setting the parameters of the newly created Tool.

·         Modifying the script to reflect the new Tool parameters.

·         Sharing the Toolbox, Tool and Script.

Below are screenshots of the Tool’s dialog window in ArcMap and successful results window in ArcMap after running the Tool.

GIS 4102: GIS Programming Module 9

Module 9: Working with Rasters

This week’s lecture covered the use of the List and Describe functions with rasters, using raster objects in geoprocessing, using map algebra in operators, and working with classes to define raster tool parameters. 

This week’s assignment involved writing a script that creates a raster output that identifies areas with a particular set of parameters (slope, aspect, land cover type).  The final raster output highlights areas with the following traits:

·         Forest landcover with classifications 41, 42, and 43.

·         Slope between 5 – 20 degrees.

·         Aspect between 150 – 270 degrees.

Below is a screen shot of the final raster.

GIS 4048: Applications in GIS Module 9

Module 9:  Urban Planning: GIS for Local Government – Scenario 1 & 2

This week’s lab involved obtaining and editing a parcel, working with parcel zoning, learning about the public land survey, using advanced editing techniques, and using data driven pages to create a map book.

The map below represents 1 page from my map book created with data driven pages.

GIS 4102: GIS Programming Module 8

Module 8: Working with Geometries

This week’s lecture covered working with geometry objects, reading and writing geometries, and working with multipart features.

This week’s assignment involved writing a script that creates a .txt file and writes to it the coordinates and Object IDs for vertices in a shapefile by using geometry objects, using a search cursor, and nested loops.

Below is a screen shot of the records written to the .txt file by the script.

GIS 4048: Applications in GIS – Participation Assignment Number 2 - Part II

Urban Planning – GIS for Local Government

The map below depicts county assessment land values and easements in the West Ridge Place subdivision in Escambia County, Florida.   Properties with similar characteristics should have similar assessment values.

A few important characteristics that affect value include lot size, improvement (building) size, age, condition, location, proximity to waterfront, and land use.  The map below is being used to look for inconsistencies for the review of assessment land values in the West Ridge Place subdivision.

According to the official subdivision plat map, the small triangle shape parcel in the middle (090310422) will be used as a sewage lift station and is not available for development.  In addition, the subdivision includes parcels designated as retention ponds and conservation easements not available for development.  These parcels are depicted on the map below at the top left corner of the map (090310410 with a value of $10,269 and 090310420 with a value of $167) and the bottom right corner of the map (090310421 with a value of $95).

The value on the following parcels should be reviewed:

1. Parcel 090310410 with a value of $10,269 mentioned above.  Value seems excessive as compared to parcels in the subdivision with similar characteristics.
2. Parcel 090310421 with a a value of $95 on the top left area of the map.  This parcel fronts Lamont Road and may be available for development but the value seems low as compared to other similar parcels in the subdivision.
3. Parcel 090310165 with a value of $33,250 on the top area of the map.  Value seems a bit high as compared to parcels in the subdivision with similar characteristics.

GIS 4048: Applications in GIS – Participation Assignment Number 2 – Part 1

Urban Planning – GIS for Local Government

  

1.     Does your property appraiser offer a web mapping site?  If so, what is the web address?  If not, what is the method in which you may obtain the data?

Yes, the County of Riverside, California Assessor’s Office (same as the appraiser) has the following portal: http://pic.asrclkrec.com/

Searching for Assessor’s Parcel Number (property identification number) 961-450-008 returns the following information:

Clicking on “View Parcel Map” returns the following map:

Otherwise data could be obtained by contacting the Assessor’s Office via phone at 951-955-6200 or via email at accrmail@asrclkrec.com

2.     Most property appraiser’s websites offer a list of recent property sales by month.  Search for the month of June for the current year and locate the highest property sold.  What was the selling prices of this property?  What was the previous selling prices of this property (if applicable)? 

The highest property sold during the month of June 2015 was a 55 acre commercial/industrial property at 14950 Meridian Pkwy, Riverside, CA (Assessor’s Parcel Number: 294-640-024) sold on June 2, 2015 for $97,682,500.  It last sold on April 3, 2014 for $76,693,500.

Below is a screen shot of the list of commercial/industrial properties sold within the County in June 2015 from the County website:

The County website did not have prior sales information available, but I was able to find online from other sources:

3.     What is the assessed land value?  Based on land record data, is the assessed land value higher or lower than the last sale price?

The assessed land value for the property above is $9,500,000 with a structure value of $26,000,000 for a full assessed value of $35,500,000 which is lower than both the most recent and previous sales price.

4.     Share additional information about this piece of land that you find interesting.  Many times, a link to the deed will be available providing more insight to the sale.

I was not able to find additional information on this particular property except for the Google Earth Map below:

GIS 4048: Applications in GIS Module 8

Module 8:  Homing in on Alachua County, Florida

This week’s lab involved making location decisions, creating a basemap layer, using the euclidean distance tool, reclassifying data tool, weighted overlays tool, and model builder.

The maps below depict suitable locations by census tract in Alachua County, Florida for a homesite meeting the following criteria:

1.     Near the North Florida Regional Medical Center (NFRMC);

2.     Near the University of Florida (UF);

3.     A high percentage of people 40-49 years old;

4.     High house values (a high percentage of homeowner-occupied homes).

The first map depicts various maps representing each criteria above individually.  The second map depicts various maps representing the criteria weighted equally vs. the criteria weighted higher for proximity to NFRMC and UF.

GIS 4102: GIS Programming Module 7

Module 7: Explore/Manipulate Spatial Data

This week’s lecture covered checking for, describing, and listing data, working with lists and dictionaries, working with search cursors, update cursors, insert cursors, running SQL queries, and validating table and field names.

This week’s assignment involved writing a script to do the following:

1.     Create a new geodatabase.

2.     Copy all data from a data folder into the new geodatabase.

3.     Populate a dictionary with the names and population of every county seat city in New Mexico.

4.     Printing messages before and after each process.

Below is a screen shot of the results in the Interactive Python window.

GIS 4048: Applications in GIS Module 7

Module 7:  Homeland Security – Protect

This week’s lab involved the summarizing of data in a table, performing the Generate Near Table analysis, Clipping the data frame, managing elevation data, working with LiDAR data, using the LAS Toolbar, the LAS Dataset to Raster Conversion Tool, the Hillshade Function, the Viewshed Analysis, using the 3D Analyst Extension, creating Lines of Sight, and working with ArcScene Viewer.

The maps below depict critical infrastructure, security checkpoints, surveillance locations, and lines of sight in the vicinity of the finish line for the Boston Marathon so that homeland security planners can implement protective measures such as securing the perimeter and stepping up surveillance posts at ingress and egress points. 

GIS 4102: GIS Programming Module 6

Module 6: Geoprocessing with Python

This week’s lecture covered using Python for geoprocessing tools, working with toolboxes, using functions and classes, using environment settings, working with tool messages and ArcGIS licenses, and accessing Help for syntax and sample code.

This week’s assignment involved writing a script to do the following:

1.     Add XY coordinates to a layer by using the AddXY Tool.

2.     Creating a buffer around features by using the Buffer Tool.

3.     Dissolving the buffer layer into a separate, single feature by using the Dissolve Tool.

4.     Printing the messages after each tool.

Below is a screen shot of the results in the Interactive Python window.

GIS 4048: Applications in GIS Module 6

Module 6:  Homeland Security – Prepare MEDS

One of the goals of the Department of Homeland Security (DHS) is to maintain a comprehensive geospatial database prepared, ready, available, and accessible to communities so they can prevent, prepare, respond and recover from a catastrophic event.  The Minimum Essential Data Sets (MEDS) is a geospatial dataset for homeland security planning and operations managed by the Homeland Security Infrastructure Program (HSIP).

MEDS data themes include:

Orthoimagery

Elevation -

Hydrology

Transportation

Boundaries

Structures

Geographic Names

Data should be two years current for urban areas and five years for large areas.  The data is used by various governmental agencies to prepare, prevent, respond and recover from a catastrophic event such as a terrorism attack.

This week’s lab assignment involved the compiling and manipulation of data to prepare a MEDS for the Boston Metropolitan Statistical Area (a tier 1 urban area) in anticipation of the Boston marathon.  Tier 1 urban areas are the largest, most populated metropolitan centers in the country. 

The starting datasets were downloaded from the USGS’s National Map Viewer and clipped to the Boston area of study.  A geodatabase was created and the data frame mapping environment was set including the creation of eight group layers to match the MEDS data themes above.  Datasets were manipulated, created and placed into the applicable group layer.

Some of the tools and procedures used in this lab include joining a table by attributes, setting labels to show only at specific scales, setting group layers, selecting by location to export data, working with various types of symbology and styles (like transportation specific style of symbology), setting layers to display at certain scales, extracting by mask from a raster, using colormaps, adding XY coordinate data as a layer, and saving to layer files to preserve symbology for future re-use.

GIS 4102: GIS Programming Module 5

Module 5: Geoprocessing in ArcGIS

This week’s assignment covered creating a toolbox, creating a tool using Model Builder, setting Model Parameters, exporting a script from Model Builder, updating that script to work outside of ArcMap and creating a script tool from it.

The tool does the following:

1.  Clips the Soils layer to the Basin features.

2.  Selects Not Prime Farmland from the Clip.

3.  Erases this selection from the original Basin layer.

4.  Creates a new Basin layer without the Not Prime Farmland clipped/selection.

Below is a screen shot of the output shapefile (the new Basin layer without the Not Prime Farmland).

GIS 4048: Applications in GIS Module 5

Module 5:  Homeland Security – DC Crime Mapping

This week’s lab involved the creation of two maps based on January, 2011 crime data for Washington, DC.  The lab covered the geocoding of addresses by creating a custom address locator, creation of graphs and reports, various types of symbology, the use of the multiple ring buffer tool, spatial joins, and the spatial analyst toolbox and kernel density.

The first map represents crime rate per police station and within half-mile, one mile and two miles of a police station to determine the need for a police substation near the 7^th District station where crimes are occurring but the closest station (7^th District) is over two miles away.  The Multiple Ring Buffer tool was used to create the buffers.  The graphs were generated in ArcMap.  Spatial Joins were performed to determine the crime rate per buffer and per police station.

The second map represents three types of selected crimes (burglary, homicides, and sex abuse) in relation to population density.  Population density is based on U.S. Census data by census block for 2004.  Crime density was calculated by using the Kernel Density Tool with a 1,500 sq. km. search radius.  The population density was symbolized as graduated symbols in contrast to graduated colors used for crime density making the map easier to read.  The analysis indicates that population density generally has a relation to crime density, but that is not always the case as with homicides and sex abuse.

Below are the two maps depicting crime analysis for Washington, D.C.

GIS 4102: GIS Programming Participation Assignment

Participation Assignment No. 1

Tracking Hotspots: Putting Sewer Cleaning on the Map, WaterWorld Magazine.

Click for WaterWorld Magazine Article

This article discusses the use of ArcGIS and ArcPy in particular to track distressed areas of the sewer system that need frequent cleaning.  A rise in sanitary overflows and sewer stoppages at the Town of Mooresville, NC in 2010, due to an aging infrastructure and the tripling of the population, resulted in the need for distressed spots (hotspots) to be identified for maintenance. 

Monthly data, stored in Excel, containing the manhole location and last time the corresponding sewer main was cleaned needed to be converted into a map to identify anticipated hotspots.  Python scripting was used to process the batch of Excel data to generate a comprehensive dataset.  The resulting dataset was joined with the features of the town’s sanitary sewer system which had been recently created in ArcGIS.  Reports were created by using the ArcPy function in Python to present the results.

Python was used to automate the analysis process in ArcGIS resulting in color coded maps representing cleaning frequency and identifying anticipated hotspots.  With Python, the Analyst Frequency Tool was used to calculate the number of sewer mains and length cleaned in a given month and the process was automatically repeated by Python for each month and merged into a single comprehensive layer.

The resulting map facilitated the identification of areas that require more frequent inspections and increased pumping/cleaning particularly for food service establishments thus remedying problem grease interceptors in multiple occasions.  Productivity increased and main-line stoppages decreased resulting in cost savings including less overtime.

GIS 4048: Applications in GIS Module 4

Module 4:  Hurricanes

This week’s lab involved the creation of two maps related to Storm Sandy that hit the New Jersey shore in October 2012.  The lab covered the use of Excel data, creation of custom symbology, advanced labeling, map grids, the effects toolbar, setting geodatabase attribute domains, and editing features using domain properties.

The first map depicts the status and path of Storm Sandy as it hit the New Jersey shoreline.  The path was created by using the Points to Line Tool in ArcToolBox thus creating a polyline feature class from a points feature class containing data for Storm Sandy.  The hurricane specific symbology was created with Character Marker Symbols and saved to my user profile for future use.  The meridians and parallels were added to the map by setting a new grid under the data frame properties.  Maximizing map space became a little challenging as the longitude and latitude markers take print area space.  I was able to re-align the latitude readings vertically to save space.

The second map depicts structural damage to one side of street in the Tom’s River Township based on pre and post storm imagery.  The Slider Tool of the Effects Toolbar was used to swipe (peel on/off) the imagery to determine structural damage based on the before and after imagery.  A new point feature class was created along with Attribute Domains to limit possible values for the various damage assessment fields thus reducing data entry errors. 

Below are the two maps depicting Storm Sandy’s path and damage assessment.