1.   1.  The most populated countries in order are: China, India, United States, Indonessia, Russia, Brazil, Pakistan, Japan, Bangladesh, Nigeria. I arrived at this by opening the countries attribute table and ranking them by population by clicking on the  POP_CNTRY field name.

2.   2.  15 I did this by doing a select by attribute query system = amazon and then opening the selected features in a table which told me there were 15.

3.    3. There are 60 cities with 500 km. I did this by selecting the rivers and then selecting all cities within a 500 km search radius via a select by location query

4.     4. 516490670-64193450= 452297220 Is the answer. Got the population by the statistic tab of the result of the spatial query minus the population of Iran

5.     5. The least populated is Vatican city and the most populated is Ethiopia. I got this by  doing a select by attributes landlock = y. this returned all the landlocked countries which I then ranked by population.

6.    6.  Solvenia, Bosnia &Herzegovina, Croatia, Slovakia, Austria, Czech Republic, Yugoslavia, Romania, Poland. I did this by doing a spatial query, Verspezm being the selected feature, cntry02 the target layer and cities the source layer with a 300 km distance

7.     7. Cameroon, Central Africa Republic, Libya, Niger, Nigeria, Sudan. I found this by doing a spatial query in which Chad was selected and then I chose to select from the cntry02 layer any country that shares a border from the drop down.

8.   8.  Russia, 97; United States 93; Thailand, 72; turkey, 67; Poland, 50. I found these by doing a spatial join with cntry02 being my target features and cities being my join feature. From there I opened the cntry02 table and ranked them by the join count.

9.     9. The rivers running through Sudan are the Blue Nile, 1893 km; Nile, 3023km; and the White Nile, 5046 km. This totals 9962 km. I arrived at this by doing a spatial query for all rivers that intersect Sudan. Once these rivers were selected I projected them onto a sinusoidal projection in order to have the GIS program be able to calculate the lengths of the rivers. From there I added a new field to the attribute table of the newly projected selected rivers titled ‘length’. I then used the calculate geometry school.

10                Russia, 1537; Canada, 1355; united States 759; China 226; Sweden 173; I did this by doing a spatial join between the lakes and cntry02. Then I opened the spatially joined files attribute table and sorted by join count.

11.                         Canada 443,509 km/sq; USA 196,856; Russia 138250 km/sq;  Kahzakstan 70,869; China 51,272; I first projected all of the lakes onto a sinusoidal projection so that the program could measure their area. I then added a new field named ‘area’ in the new projection attribute table. I did a spatial join between countries and lakes. This didn’t work had no clue how to do it the right way so I looked at the cumulative area of the countries with the most length amongst some others and thus came up with the answer J.

Lab 3

Part 1

Part 2

Week 2 Lab

This lab focuses on the 50 closest Taco Bell locations to my apartment. I found the addresses of these Taco Bell’s on their website in a location finder. On the map above you can see all 50 locations, a ½ mile buffer zone around each, the mean center of the Taco Bell’s, a point showing the location of my apartment and major highways. I chose this topic because Taco Bell is my favorite fast food and being as I don’t have a car, it is important that I know where a near Taco Bell is. Additionally I wanted to know some information regarding the clustering of Taco Bell.

Upon making this map I soon realized that Taco bell’s in Los Angeles are practically everywhere. This led me to my first question: ‘How many of these Taco Bells are within one mile of each other?’ in order to answer this question I created a ½ mile buffer zone around each Taco Bell. When the buffer zones intersect it means that the two corresponding Taco Bells are within a mile of each other. I found 11 Taco Bells that were within a mile from another Taco Bell. After doing this I made a buffer layer with dissolved boundaries to better show which fit the criteria.

Additionally I thought it was interesting to see that many of the Taco Bells were pretty far from my apartment. This then made me wonder where the mean center of these 50 Taco Bells was. Using the spatial statistics tools, I was able to measure geographic distributions and plot the mean center on the map.

Week 1 Lab