College. Money. Struggles. GIS.

I learned a lot in school, both in books and in life. And I’m very proud of my degree and what all has come from it, but one thing I am most proud of is the business I started with my best friend, Paxton.

We were poor college kids trying desperately to make ends meet. And when I say “poor” I truly mean it. We both, at separate occasions, were homeless for a period of time. He slept on the floor in my room for 6 weeks in 2013, and I slept on his couch for 5 weeks just six months later. I could literally fit everything I owned in my truck. And that’s where all my stuff stayed during the 5 weeks that I slept on his couch… I won’t get into it much further, and lord knows I could go on and on when writing about how much we struggled individually.

I could have been bailed out too.. I could have called my Dad, Mom, or grandparents and told them I had no money and that I was struggling to balance school and bills, but I didn’t.. I probably should have on a few occasions, but I’m just way too prideful. That’s not to take away from what they did do for me in my life, but this was a time I believe was very important for me to go through and experience.

ANYWAYS…. I wanted to write this blog to reveal a good way for college kids to make money during the warmer months, as well as show some neat maps I made. The solution is simple too, start a lawn care business.

Paxton and I came up with this genius idea to barrow a bunch of money and buy mowing equipment to mow lawns all over town. We were super excited, because we were going all in and we were going to do it big time. We also had the perfect name, “Grass Busters Lawn Care.” “Who ya gonna call?..” ….. lol

So we calculated that we could probably have our loan paid off within two months (L.O.L. to that btw). I was really excited so I called my Dad to tell him what we were planning on doing, hoping he’d be proud to hear my idea. And he was! But he said, “Don’t you dare go get a loan, that’s dumb. I’ll buy you everything you need and you guys can just pay me back at the end of the mowing season if you have the money.” Well I was even more ecstatic now that we had support from my father. He bought a trailer, two push mowers, a weed eater, and a leaf blower for us.

We started busting our rears right away. We established an LLC (bad idea btw), we opened up a joint business account, paypal account, got a business phone (bad idea), bought a monthly storage space to keep the trailer and equipment, made a billion flyers and used our school printing allowance to print them all. We went door to door to get customers, made a ton of craigslist ads, and even paid to have an ad in the paper. We were ready for success…

One thing to note real quick is that some people suck. don’t get me wrong, we dealt with a lot of incredible people, but we also dealt with a lot of really awful people. That’s one of the many lessons we learned. Dealing with the public can be annoying at times.

Anyways..

We started to acquire business, but at a very slow pace. It did work though! Eventually, we had a steady number of clients.

I remember being so busy and tired that summer. We were working two jobs and taking a 5-day per week Spanish class at OU. We couldn’t afford not to have two jobs either. Our lawn business didn’t generate enough income for us to pay our bills yet, and we were trying to stack money in our business account to hopefully pay my Dad off completely. It was quite the summer…

All of this is a terrible segway into showing off a few maps I threw together. I was going through old documents and found our old client lists. So naturally, as a mapping nerd, I generated some data to make maps from them. Check them out!

These were clients from our final year of being in operation.

GBLC

GBLC_2

 

 

 

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3 Reasons To Have Your Property Mapped With A Drone

1. ) Take measurements without having to be there.

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Take advantage of being able to measure things on your property without having to physically go there. This is extremely convenient, especially if you have several acres of land.

 

 

 

2.) See your property as it looks now, and in high resolution.

1Google earth can mislead you by displaying outdated imagery and in poor resolution. When you have your property mapped, this is no longer an issue. Any time you need to look at your property, it is displayed in high resolution. This also allows you to be more precise with your measurements.

 

3.) View your property in 3D.

Capture3d

Interactive 3D viewing is a great benefit of having your land mapped with a drone. Other than being awesome, the 3D model provides a very unique perspective.

 

 

 

 

It only takes one flight to generate this data, and viewing the finished product is as simple as clicking a link.

If you’d like Pollard GIS Services to map your property, visit pgiss.org. There, you will see additional information including pricing.

 

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PGISS practices safe flying and is commercially licensed via the FAA’s Part 107 rules and regulations.

 

We use the Phantom 4 Pro for our aerial mapping jobs. There’s a good deal on them at Amazon:

What Light Pollution Can Tell Us About North Korea

With the Pyeongchang Winter Olympics in full swing, I thought it might be an appropriate time to do some “geo-investigating” into the country that is located just north of South Korea. The best way to investigate North Korea from a geospatial perspective is to turn the lights off so we can see it. Nighttime remote sensing is powerful.

It’s obvious that North Korea lacks the ability to light up the night sky when we look at the volume of light pollution they produce, but what does this tell us?… Well I think the best answer to that is GDP (Gross Domestic Product). If you don’t know what that is, you clearly snoozed during a crucial lecture in your economics class. GDP, in layman’s terms, is the monetary value of goods and services a country produces. I believe light pollution data can be directly correlated to this. The more illuminated a country is at night, the more vibrant their economy must be. I did a project over this topic in college and served as the GIS researcher. Luckily, we had someone who was actually majoring in economics on our team to help us apply our data and research appropriately. Her expertise were crucial when drafting our final report and presentation. If I still had the report I would share it, but unfortunately I don’t..

The limited lighting over North Korea can tell us a lot about them, and I’m sure GDP is just one of the few. Below are a few maps to get a better visualization of North Korea.

Also, if you’re interested in this topic, look up “The Applicability of Nighttime Remote Sensing Data to Analyze Urban Dynamics: The Indian Scenario.” Avanesh Kumar Singh is the author.

Capture

North Korea Map

 

 

 

 

2017 Tulsa Homicides: Maps, Statistics, & Perspective

 

 

 

 

Parents, siblings, co-workers, and friends. 81 individuals lost their lives to murder in the city of Tulsa last year. 81 lives taken away at the hands of another. 81 people who lived life just the same as you do. 81 lifetimes of human experience no more or no less valuable than yours. 81 of us are gone.

We’ve become numb to hearing of these horrific tragedies. It’s an issue all over the world, but it’s important to acknowledge that it’s an issue right here in our home town. I believe we’re all guilty of ignoring the problem and living almost as if it doesn’t exist. We may think it’s not our responsibility to do anything about it, but I beg to differ… There’s an easy way for each and everyone of us to contribute. I believe it’s as simple as acknowledging the problem, accepting that it’s real, and being thankful for the women and men who dedicate their lives to confronting it. Our police officers, as well as all other first responders, live in this treacherous reality so that we don’t have to.. As citizens, we have the power to influence change by the effortless swipe of a pen. We can vote.. Any time there is an opportunity to vote with the implications of helping out our first responders, we should jump at it. When they express a need for funding, I’m confident in saying that it is only in our best interest to help give them what they need. They are the ones who can directly impact the volume of crime in our city. They are the ones who keep us safe. Why wouldn’t we help?

No life is more valuable than another.. It’s not 81 data points.. It’s 81 names..

NoNames

Names..

  • Altercation: 9
  • Gang Related: 3
  • Domestic: 12
  • Drug Related: 3
  • Robbery: 11
  • Dispute: 13
  • Child Death: 6
  • Officer Involved: 6
  • Other: 7
  • Unknown: 11

Tulsa’s NewsOn6 reported on this project. Link: Story from NewsOn6

The data used in this project was obtained from Tulsa’s NewsOn6 via the Tulsa Police Department.

All Realtors Need This..

I see it far too often.. “10 ACRES FOR SALE!” with a snapshot from Google Earth showing about where the property is located with some unofficial property boundaries drawn on the photo, likely using paint. I’m not being sarcastic, because I would do the same exact thing if I was attempting to sell some property without my current expertise..

1I’m writing this short blog because I want property owners,  real estate agents, and real estate companies to realize that it is incredibly simple and incredibly cheap to up your marketing game in this area. All you need to do is find a licensed drone pilot with some basic knowledge of GIS, tell them what property you want updated imagery for, and pay them when the job is done. With what I do, you wouldn’t just receive photos from a drone showing you the property, you’d receive a product similar to how google earth works. You can click, drag around, zoom in and out, and really see what the property looks like in the now. Imagine clicking on that “10 ACRES FOR SALE!” link and getting to look around the property in high resolution. That’s much more attractive than looking at a Google Earth snapshot, or even looking on Google Earth Pro!

It’s more than just up-to-date high resolution imagery though. If you had me go fly a property, you’d get the aerial, a digital elevation display, a 3D model, and a vegetation health display. It’d go something like this…

“Hi {x name}! I just finished processing your job request for {x property address} and it looks good! Here’s your link. You can share this link with whoever you’d like and they can see everything the way you can. If you have any questions don’t hesitate to call, text, or email me! I just shot you an email with your invoice. Thank you so much!  – Trent”

If you’re interested in this kind of product, shoot me an email. Just put your name and email in the form below. I’d love to go fly for you. I keep it simple and convenient. I believe it should be super seamless for people to get this kind of product when they want it. I promote the idea of my customers shooting me a text when they want something flown. It should be THAT easy. (btw, that hyperlink takes you to an example property)

 

 

I am indeed a licensed drone pilot as well as a GIS professional. I’m based out of Claremore, Oklahoma and am willing to do projects pretty much anywhere in Eastern/Northeastern Oklahoma. I know there are people like me elsewhere too so please find them if you’re too far for me. I want to be able to look at land for sale in random places and actually be able to see the land that is for sale haha. People like myself who can pair drones with GIS with real estate agents could revolutionize what it’s like to look for property.

Capture

PollardGIS Executes Soft Launch

Pollard GIS Services has officially launched in a minimal capacity. Although I have greater visions for PGISS down the road, I believe it’s time I put this small business into operation.

For now, PGISS will offer aerial mapping and photography via drone technology as well as GIS solutions to a wide variety of companies and industries. This will be the manner of operations for 3 to 5 years. During this window of time we will be striving to accomplish goals that have been set forth by myself and my father, Brent  Pollard. Upon accomplishing these goals, managing situational changes, and measuring the overall success of PGISS, we will then assess the business and plan out new objectives and ambitions.

The reason PGISS will not be operating at full capacity is simply due to the time and circumstances surrounding the company. My father, the man who will likely take this bull by the horns once he retires, works at a large oil & gas company. The job he currently occupies is a prestigious one to say the least, and he has been working at x company for 27 years. Although it’s not 100% known when his retirement will come, it’s no secret that he will indeed retire one day relatively soon. Whether that’s 10 years from now or tomorrow, it’s unknown. As for me, I was fortunate enough and blessed to land my dream job at an incredible company. My day job is a dream come true and it has made the 5 years I spent in college well worth the time and all the headaches. This job will always come first and will always be my number one priority professionally. So for these reasons, PGISS just isn’t quite ready to become what it will be one day.

I wanted to write this blog to explain PGISS in a brief manner for those that are interested in what PGISS is and what we are setting out to do.

If you or anyone you know might be interested in any of our services, please share PGISS with them!

Thank you for your time and interest. It’s sincerely appreciated.

– Trent

14 Years of Land Cover Change in Baghdad, Iraq

This was one of four projects I completed in my Digital Image Processing class my senior year of undergrad. I thoroughly enjoyed this project so I just wanted to share it on my blog…

___________________________

Abstract

This study observes the land cover changes in and surrounding Baghdad, Iraq from the year 2000 to 2013. The observed changes are expected to be due to war and conflict. These two years, 2000 and 2013, are before and after the U.S. bombed and invaded Baghdad post 9/11. Changes in urban density and vegetation can be observed.

This study was accomplished by using Landsat 5 TMR L0RP and Landsat 8 OLI/TIRS to acquire remotely sensed images for 2000 and 2013. Landsat 5 is the source for the 2000 images, and Landsat 8 is the source for the 2013 images. The path/row to be analyzed is 139/037. The Landsat scenes are 180km X 180km and 30 meters in resolution.

The objective of this study is to use remote sensing software (ENVI) to reveal these scenery changes in Baghdad by creating thematic maps of the study area. Three images were obtained for each year to create a yearly average for the region. Four thematic maps were created in the conclusion of this study. Two maps for each year, one using a maximum likelihood classification method and one using a minimum distance classification method.

Statistics are discussed and reflected upon.

 

Study Area

The area I have chosen for this study encompasses the city of Baghdad, Iraq. Baghdad is the capital of Iraq and is located on the Tigris River just 40 kilometers to the west of the Euphrates River. Baghdad’s location is depicted in figures 1a and 1b below for spatial context. This is roughly a 40 Kilometer Squared area that is being analyzed, and the time period for this analysis will span 14 years, 2000 through 2013. Baghdad, along with this time frame, was chosen as the study area due to the war and conflict the country experienced during this time period. The first image to be analyzed was captured in 2000. This is three years before the U.S. bombed and invaded Baghdad [Burnham, Gilbert, et al. “Mortality after the 2003 invasion of Iraq: a cross-sectional cluster sample survey.” The Lancet 368.9545 (2006): 1421-1428]. The second image to be analyzed was captured in 2013. This was 2 years after U.S. troops were completely withdrawn from the country. [Lindsay, James M. “George W. Bush, Barack Obama and the future of US global leadership.” International Affairs 87.4 (2011): 765-779]. It may be possible to observe war related damage using these two images. The city of Baghdad is located in a dry Middle Eastern desert environment where the average annual temperature is 22.6 degrees Celsius, and the average annual rainfall for the area is 157 millimeters [http://en.climate-data.org/location/86/]. Baghdad sits 34 meters above sea level and has a flat alluvial plain landscape type [http://www.britannica.com/place/Baghdad]. The estimated population of Baghdad in 1987 was 3,841,268, and this number grew to 6,150,000 by 2011 [http://www.citypopulation.de/Iraq.html]. This is a population increase of roughly 2,308,732. These years were chosen because they were the closest years with available population data for this study.

1a

Figure 1a

1b

Figure 1b

Data

The Landsat scenes acquired for this study came from Landsat 5 Thematic Mapper (TM), and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). These scenes were downloaded from earthexplorer.gov. Three images were chosen from both years, 2000 and 2013. The three images from 2000 were obtained from Landsat 7, and the three images from 2013 were obtained from Landsat 8. The scenes that were selected for this study were chosen for their clarity. It was important for each image to be extremely low in cloud cover, and these were not difficult to locate due to the desert climate of the Middle East. Within each year, three months were chosen. This is one image for each month chosen, and each of these months were from different seasons. This was done to encompass the seasonal changes of the region.

The Landsat scenes that were downloaded for this study are 180km X 180 km and 30 Meters in resolution. The path/row of these Landsat scenes are 139/037, the projection is UTM zone 38 north, and the datum is WGS84. Within these Landsat scenes, three relatively large bodies of water along with their tributaries can be observed. Much of the image consists of pure desert, especially to the west/southwest. The capital city of Baghdad is located on the east/northeastern part of the image, and is observed as a gray color if the image being viewed in true color bands. Something interesting to note at first glance is the significant buffers of vegetation that surround the rivers, and the urban areas that are completely surrounded by vegetation.

Path: 139

Row: 037

Projection: UTM zone 38 north

Datum: WGS84

Resolution: 30 Meters

Day 118 Year 2013 Day 198 Year 2013 Day 358 Year 2013
01 02 03

 

YEAR SENSOR DATE PATH/ROW
2000 Landsat 5 TMR L0RP 2000/04/01 139/037
2000 Landsat 5 TMR L0RP 2000/07/29 139/037
2000 Landsat 5 TMR L0RP 2000/10/01 139/037
2013 Landsat 8 OLI/TIRS 2013/04/28 139/037
2013 Landsat 8 OLI/TIRS 2013/07/17 139/037
2013 Landsat 8 OLI/TIRS 2013/12/24 139/037

 

BD1

Above: NIR Image: Baghdad, Iraq in 2000 containing 8,200,530 pixels.

1,427 columns and 1,025 rows

 

BD2

Above: NIR Image: Baghdad, Iraq in 2013 containing 8,200,530 pixels.

1,427 columns and 1,025 rows

 

Initial Observations of NIR images

In observing and comparing these two NIR images, it is easy to notice that the purple shade of the urban area is darker in the 2000 image than it is in the 2013 image. Vegetation is far more colorful in the 2000 image than it is in the 2013 image. I expect this to be due to war and conflict. Rivers did not change color from one year to the other, but the purple shades that directly surround the rivers are significantly lighter in the 2013 scene compared to the 2000 scene. Most greens from the 2000 image are yellow in the 2013 image. This is very noticeable near the airport on the southwest side of the city.

 

Results

Supervised Classification Training Data:

Classes: Urban, Light Urban, Desert, Cropland, Forest, Water, and Clouds (clouds only in 2000)

2000         2013

2000                                                                            2013

 

 

Methods

The two classification methods used to generate these thematic maps were maximum likelihood and minimum distance. Both methods were used for each year resulting in four total maps.

Maximum Likelihood vs Minimum Distance

  • The maximum likelihood method calculates the likelihood or probability for each pixel to belong to a specific class. This classification method assigns each pixel to a class based on highest probability. All pixels will be assigned to a class unless a probability threshold is declared [http://www.exelisvis.com/docs/MaximumLikelihood.html]. In this project no thresholds were specified. All pixels were assigned to a class.
  • The minimum distance method classifies all pixels to the nearest class by calculating the Euclidean distance to the mean vector. Unless a distance threshold is declared, all pixels will be assigned to a class [http://www.exelisvis.com/docs/MinimumDistance.html]. In this project no thresholds were specified. All pixels were assigned to a class.

 

2000-1

 2000-2

2013-1

2013-2

Maximum Likelihood Statistics (2000):

CLASS_NAME         AREA                         PERCENT

Unclassified                0.00                             0.000000

Desert                          1624701600.00           20.071423

Urban                          331227900.00             4.091961

Water                          69236100.00               0.855337

Cropland                     3301883100.00           40.791178

Forest                          119467800.00             1.475895

Light Urban                2112327000.00           26.095505

Clouds                         535757400.00             6.618701

Minimum Distance Statistics (2000):

CLASS_NAME         AREA                         PERCENT

Unclassified                0.00                             0.000000

Desert                          1111798800.00           13.735066

Urban                          1088132400.00           13.442694

Water                          416358000.00             5.143651

Cropland                     1348468200.00           16.658860

Forest                          542764800.00             6.705269

Light Urban                1776638700.00           21.948441

Clouds                         1810440000.00           22.366019

Maximum Likelihood Statistics (2013):

CLASS_NAME         AREA                         PERCENT

Unclassified                0.00                             0.000000

Urban                          382184100.00             5.178312

Light Urban                1584391500.00           21.467332

Deserts                                    1661079600.00           22.506399

Cropland                     3531463200.00           47.848712

Forest                          180773100.00             2.449342

Water                          40585500.00               0.549903

Minimum Distance Statistics (2013):

CLASS_NAME         AREA                         PERCENT

Unclassified                0.00                             0.000000

Urban                          424042200.00             5.745458

Light Urban                1358619300.00           18.408286

Deserts                                    2041870500.00           27.665834

Cropland                     2499678000.00           33.868787

Forest                          575322300.00             7.795191

Water                          480944700.00             6.516445

 

Statistics

No pixels went unclassified for each year. As documented earlier in this report, this is because no probability or distance thresholds were declared.

When comparing the statistics for both methods (maximum likelihood and minimum distance) for the 2000 image, there was not a dramatic difference overall, but a few to note are the differences between cropland and urban. Under the maximum likelihood classification method, cropland covered 40.791178% of the image, and urban covered 4.091961% of the image. This differs greatly from the minimum distance classification method. Under the minimum distance classification method, cropland covered 16.658860% of the image, and urban covered 13.442694% of the image. For cropland, this is a percentage difference of 24.132318. For urban, this is a percentage difference of 9.350733.

When comparing the statistics for both methods (maximum likelihood and minimum distance) for the 2013 image, there was little difference. The differences here are even smaller than they were for the 2000 image. The largest difference for the 2013 methods were within the cropland class. The maximum likelihood cropland class covered 47.848712% of the image, and the minimum distance cropland class covered 33.868787% of the image. This is a percentage difference of 13.979925.

 

 

Maximum Likelihood: Year 2000

Overall Accuracy = (169347/174481) 97.0576%

Kappa Coefficient = 0.9619

 

Ground Truth (Pixels)

Class            Desert         Urban         Water         Cropland      Forest 

Desert        52967            0            0            0            0

Urban            0        50756            0            0            0

Water            0           18         4693            0            0

Cropland            0           38            0        24135            0

Forest            0          301            0            0         4917

Light Urban           19         1019            3          169           86

Clouds            4            0            0            0            0

Total        52990        52132         4696        24304         5003

 

 

Ground Truth (Pixels)

Class         Light Urban     Clouds      Total 

Desert            0            0        52967

Urban         2984            0        53740

Water            0            0         4711

Cropland          427            0        24600

Forest           58            0         5276

Light Urban         9036            8        10340

Clouds            0        22843        22847

Total        12505        22851       174481

 

 

Ground Truth (Percent)

Class          Desert       Urban         Water         Cropland      Forest

Desert        99.96         0.00         0.00         0.00         0.00

Urban         0.00        97.36         0.00         0.00         0.00

Water         0.00         0.03        99.94         0.00         0.00

Cropland         0.00         0.07         0.00        99.30         0.00

Forest         0.00         0.58         0.00         0.00        98.28

Light Urban         0.04         1.95         0.06         0.70         1.72

Clouds         0.01         0.00         0.00         0.00         0.00

Total       100.00       100.00       100.00       100.00       100.00

 

 

Ground Truth (Percent)

Class         Light Urban   Clouds        Total 

Desert         0.00         0.00        30.36

Urban        23.86         0.00        30.80

Water         0.00         0.00         2.70

Cropland         3.41         0.00        14.10

Forest         0.46         0.00         3.02

Light Urban        72.26         0.04         5.93

Clouds         0.00        99.96        13.09

Total       100.00       100.00       100.00

 

 

 

Class   Commission     Omission          Commission            Omission 

(Percent)    (Percent)            (Pixels)            (Pixels)

Desert         0.00         0.04             0/52967            23/52990

Urban         5.55         2.64          2984/53740          1376/52132

Water         0.38         0.06             18/4711              3/4696

Cropland         1.89         0.70           465/24600           169/24304

Forest         6.80         1.72            359/5276             86/5003

Light Urban        12.61        27.74          1304/10340          3469/12505

Clouds         0.02         0.04             4/22847             8/22851

 

 

Class   Prod. Acc.    User Acc.          Prod. Acc.           User Acc. 

(Percent)    (Percent)            (Pixels)            (Pixels)

Desert        99.96       100.00         52967/52990         52967/52967

Urban        97.36        94.45         50756/52132         50756/53740

Water        99.94        99.62           4693/4696           4693/4711

Cropland        99.30        98.11         24135/24304         24135/24600

Forest        98.28        93.20           4917/5003           4917/5276

Light Urban        72.26        87.39          9036/12505          9036/10340

Clouds        99.96        99.98         22843/22851         22843/22847

————————————————————————————–

Minimum Distance: Year 2000

Overall Accuracy = (145464/174481) 83.3695%

Kappa Coefficient = 0.7894

 

Ground Truth (Pixels)

    Class            Desert        Urban         Water         Cropland      Forest 

Desert        48173            0            0            0            0

Urban            0        40418            0         1856          122

Water            0           21         4659            0            0

Cropland            0            0            0        17461            0

Forest            0          522           37         1221         4881

Light Urban            0        11097            0         3532            0

Clouds         4817           74            0          234            0

Total        52990        52132         4696        24304         5003

 

 

Ground Truth (Pixels)

Class            Light Urban   Clouds        Total 

Desert             0         1233        49406

Urban         3686            0        46082

Water            0            0         4680

Cropland          107            0        17568

Forest          369            0         7030

Light Urban         8254            0        22883

Clouds           89        21618        26832

Total        12505        22851       174481

 

 

Ground Truth (Percent)

Class            Desert        Urban         Water         Cropland      Forest

Desert         90.91         0.00         0.00         0.00         0.00

Urban         0.00        77.53         0.00         7.64         2.44

Water         0.00         0.04        99.21         0.00         0.00

Cropland         0.00         0.00         0.00        71.84         0.00

Forest         0.00         1.00         0.79         5.02        97.56

Light Urban         0.00        21.29         0.00        14.53         0.00

Clouds         9.09         0.14         0.00         0.96         0.00

Total       100.00       100.00       100.00       100.00       100.00

 

 

Ground Truth (Percent)

Class            Light Urban   Clouds        Total 

Desert         0.00         5.40        28.32

Urban        29.48         0.00        26.41

Water         0.00         0.00         2.68

Cropland         0.86         0.00        10.07

Forest         2.95         0.00         4.03

Light Urban        66.01         0.00        13.11

Clouds         0.71        94.60        15.38

Total       100.00       100.00       100.00

 

 

 

Class   Commission     Omission          Commission            Omission 

(Percent)    (Percent)            (Pixels)            (Pixels)

Desert          2.50         9.09          1233/49406          4817/52990

Urban        12.29        22.47          5664/46082         11714/52132

Water         0.45         0.79             21/4680             37/4696

Cropland         0.61        28.16           107/17568          6843/24304

Forest        30.57         2.44           2149/7030            122/5003

Light Urban        63.93        33.99         14629/22883          4251/12505

Clouds        19.43         5.40          5214/26832          1233/22851

 

 

Class   Prod. Acc.    User Acc.          Prod. Acc.           User Acc. 

(Percent)    (Percent)            (Pixels)            (Pixels)

Desert         90.91        97.50         48173/52990         48173/49406

Urban        77.53        87.71         40418/52132         40418/46082

Water        99.21        99.55           4659/4696           4659/4680

Cropland        71.84        99.39         17461/24304         17461/17568

Forest        97.56        69.43           4881/5003           4881/7030

Light Urban        66.01        36.07          8254/12505          8254/22883

Clouds        94.60        80.57         21618/22851         21618/26832

————————————————————————————–

Maximum Likelihood: Year 2013

Overall Accuracy = (316842/328090) 96.5717%

Kappa Coefficient = 0.9426

 

Ground Truth (Pixels)

Class            Urban         Light Urban   Deserts       Cropland      Forest

Urban        42604         3624            0          120          133

Light Urban         1309         6113         1188         3664          125

Deserts            0            0       189242            0            0

Cropland           24          244          609        68114           16

Forest            0            4            0          184         8081

Water            0            0            0            0            0

Total        43937         9985       191039        72082         8355

 

 

Ground Truth (Pixels)

Class               Water      Total 

Urban            0        46481

Light Urban            0        12399

Deserts            0       189242

Cropland            3        69010

Forest            1         8270

Water         2688         2688

Total         2692       328090

 

 

Ground Truth (Percent)

Class            Urban Light   Urban         Deserts       Cropland      Forest

Urban        96.97        36.29         0.00         0.17         1.59

Light Urban         2.98        61.22         0.62         5.08         1.50

Deserts         0.00         0.00        99.06         0.00         0.00

Cropland         0.05         2.44         0.32        94.50         0.19

Forest         0.00         0.04         0.00         0.26        96.72

Water         0.00         0.00         0.00         0.00         0.00

Total       100.00       100.00       100.00       100.00       100.00

 

 

 

Ground Truth (Percent)

Class         Water       Total 

Urban         0.00        14.17

Light Urban         0.00         3.78

Deserts         0.00        57.68

Cropland         0.11        21.03

Forest         0.04         2.52

Water        99.85         0.82

Total       100.00       100.00

 

 

 

Class   Commission     Omission          Commission            Omission 

(Percent)    (Percent)            (Pixels)            (Pixels)

Urban         8.34         3.03          3877/46481          1333/43937

Light Urban        50.70        38.78          6286/12399           3872/9985

Deserts         0.00         0.94            0/189242         1797/191039

Cropland         1.30         5.50           896/69010          3968/72082

Forest         2.29         3.28            189/8270            274/8355

Water         0.00         0.15              0/2688              4/2692

 

 

Class   Prod. Acc.    User Acc.          Prod. Acc.           User Acc. 

(Percent)    (Percent)            (Pixels)            (Pixels)

Urban        96.97        91.66         42604/43937         42604/46481

Light Urban        61.22        49.30           6113/9985          6113/12399

Deserts        99.06       100.00       189242/191039       189242/189242

Cropland        94.50        98.70         68114/72082         68114/69010

Forest        96.72        97.71           8081/8355           8081/8270

Water        99.85       100.00           2688/2692           2688/2688

————————————————————————————–

Minimum Distance: Year 2013

Overall Accuracy = (317523/328090) 96.7792%

Kappa Coefficient = 0.9460

 

Ground Truth (Pixels)

    Class            Urban Light   Urban         Deserts       Cropland      Forest

Urban        42835         3691            0          113          132

Light Urban         1091         6101         1117         3355          104

Deserts            0            0       189337            0            0

Cropland           11          190          585        68454           11

Forest            0            3            0          160         8108

Water            0            0            0            0            0

Total        43937         9985       191039        72082         8355

 

 

Ground Truth (Pixels)

Class              Water        Total 

Urban            0        46771

Light Urban            0        11768

Deserts            0       189337

Cropland            3        69254

Forest            1         8272

Water         2688         2688

Total         2692       328090

 

 

 

 

Ground Truth (Percent)

Class            Urban         Light Urban   Deserts       Cropland      Forest

Urban        97.49        36.97         0.00         0.16         1.58

Light Urban         2.48        61.10         0.58         4.65         1.24

Deserts         0.00         0.00        99.11         0.00         0.00

Cropland         0.03         1.90         0.31        94.97         0.13

Forest         0.00         0.03         0.00         0.22        97.04

Water         0.00         0.00         0.00         0.00         0.00

Total       100.00       100.00       100.00       100.00       100.00

 

 

Ground Truth (Percent)

Class              Water       Total 

Urban         0.00        14.26

Light Urban         0.00         3.59

Deserts         0.00        57.71

Cropland         0.11        21.11

Forest         0.04         2.52

Water        99.85         0.82

Total       100.00       100.00

 

 

 

 Class   Commission     Omission          Commission            Omission 

(Percent)    (Percent)            (Pixels)            (Pixels)

Urban         8.42         2.51          3936/46771          1102/43937

Light Urban        48.16        38.90          5667/11768           3884/9985

Deserts         0.00         0.89            0/189337         1702/191039

Cropland         1.16         5.03           800/69254          3628/72082

Forest         1.98         2.96            164/8272            247/8355

Water         0.00         0.15              0/2688              4/2692

 

 

Class   Prod. Acc.    User Acc.          Prod. Acc.           User Acc. 

(Percent)    (Percent)            (Pixels)            (Pixels)

Urban        97.49        91.58         42835/43937         42835/46771

Light Urban        61.10        51.84           6101/9985          6101/11768

Deserts        99.11       100.00       189337/191039       189337/189337

Cropland        94.97        98.84         68454/72082         68454/69254

Forest        97.04        98.02           8108/8355           8108/8272

Water        99.85       100.00           2688/2692           2688/2688

———————————————————————-

Histogram

Percent Urban: Year 2000 Percent Urban: Year 2013
30.187466% 26.645644%

 

Total Urban Area: Year 2000 Total Urban Area: Year 2013
10,571,970 2,609,733

 

According to the processes and methods applied in this study, Baghdad, Iraq has decreased in overall urban area within the 14 years being analyzed. Urban area was broken up into two different classes in this study due to the dramatic difference in NIR pixel color between denser urban areas and less dense urban areas.  As a result of this multi class urban area, the two classes were simply added together when analyzing overall urban statistics. The percentage of overall urban area decreased 3.541822% within the 14 years in Baghdad. This is a gradual decrease that may be cause by war, but this is not a fact. It may also be due to the historic nature of how Middle Eastern cities change. The Arab world has historically urbanized or deurbanized at slow paces, especially during the 19th and early 20th centuries [Ibrahim, Saad EM. “Over-urbanization and under-urbanism: The case of the Arab world.” International Journal of Middle East Studies 6.01 (1975): 29-45].

To gain a visual of the urban area lost, the two images below can be compared.

z1.jpg                               z2.jpg

Thematic map: Baghdad, Iraq 2000                         Thematic Map: Baghdad, Iraq 2013

 

There is a significant amount of urban area (gray colors) that is missing in the upper right portion of the 2013 image as compared to the 2000 image. Making this comparison supplies a visual to the urban area loss, but this also supplies a visual to a vegetation increase in 2013 (green colors).

 

 

Works Cited

Burnham, Gilbert, et al. “Mortality after the 2003 invasion of Iraq: a cross-sectional cluster sample survey.” The Lancet 368.9545 (2006): 1421-1428

Lindsay, James M. “George W. Bush, Barack Obama and the future of US global leadership.” International Affairs 87.4 (2011): 765-779

http://en.climate-data.org/location/86/

http://www.britannica.com/place/Baghdad

http://www.citypopulation.de/Iraq.html

Harris Geospatial Solutions: http://www.exelisvis.com/docs/MaximumLikelihood.html

Harris Geospatial Solutions: http://www.exelisvis.com/docs/MinimumDistance.html

Ibrahim, Saad EM. “Over-urbanization and under-urbanism: The case of the Arab world.” International Journal of Middle East Studies 6.01 (1975): 29-45.

Maps, Beets, Battlestar Galactica

My all-time favorite television show is The Office hands down. I was introduced to the show in the spring of 2012 by Paxton Biggs. I recall the night very distinctly because I had just enrolled at OU and was excited about this new coming chapter in my life.

The night I first watched The Office I was sleeping on a blow-up mattress in the living room of Tosha King’s house where lots of my friends lived at the time. This was in Norman, Oklahoma. My friends living there at the time were Tosha, Wes Hammons, Matt Denton, Ray King, and Paxton Biggs (kind of sort of).

So there I was laying on this blow up mattress when Paxton, who was on the couch, decided to turn The Office on for some casual entertainment as we fell asleep. I can still remember the exact opening scene of the very first episode I ever watched. I laughed so hard and was hooked immediately on the show.. The scene that I’m talking about is set in the conference room and Michael Scott is talking to his employees about some nonsense that they aren’t paying attention to, but Michael believes he really has their undivided attention because they are watching a television screen behind him. On this screen there is the bouncing cube that you would see if you left your DVD player untouched long enough. All the employees are in deep anticipation hoping this cube hits the very corner of the screen exactly. I feel like we can all relate to this…

Jim Halpert (a character on the show) does a great job explaining this: https://www.youtube.com/watch?v=SmFEK2gq4QQ

So then and there my love for The Office was born. It seems silly to write about my emotional attachment to a TV show, but what people don’t understand is that The Office was there to shine a small light in some dark times of my life. I have watched the office roughly 25 or 30 times through. At one time, I couldn’t go to sleep if I wasn’t listening to the show..

Recently I came up with an idea to create a map that showed where each branch of  the Dunder Mifflin Paper Company was. I did some research to make sure I plotted every single branch that was ever mentioned in the show. Luckily there were a few good sources online that discussed each branch that was mentioned. They even provided what episode the branch was mentioned in which was pretty impressive.

This was a fun little project I completed, and I hope my fellow fans of The Office get a kick out of the map.

TO

 

GIS & U.S. Presidents 

From time to time I get an urge to create geospatial datasets by doing half-hearted research online. It’s just a nerdy hobby of mine but it’s something I enjoy. With this research though, my goal is to produce maps that display my data in a variety of ways. I query my datasets to narrow down something that I think would be interesting to see spatially…

I became interested in following American politics and global events roughly 2 or 3 years ago. With my growing interest, I always come up with mapping ideas for current events and trending topics, but I never seem to act on them because I stay so busy with work.. Recently I did some simple research and created a table of data on our Presidents. The dataset I created is relatively extensive and I will likely create a few more maps from it, but these two are the two I have made so far. 

I think it’s interesting to see on a map where our presidents were all born. And in a morbid way, it’s also interesting to see where they have died. I think this mapping idea of mine was extremely simple but unique at the same time.