Andrew Williams DHP 207: GIS for International Applications May 28, 2013

Assignment 6: Project Data Preparation and Basic Spatial Analysis

1. Folder structure for project data:

2. Projection Information: Note: Because I worked with conflict data across , it seemed to make sense to set the projection to a UTM zone, in this case, WGS 1984 UTM Zone 31N. Then again, because Algeria is so vast, and most political violence occurred along the coast, and if I had decided to focus on a smaller area nearer the coast, I might have chosen a different projection (see below).

a. Data Set 1: Algeria Admin Boundaries (levels 0-2)

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i. Name: DZA_adm2_Project ii. Projection type: WGS_1984_UTM_Zone_31N iii. Linear units: Meters iv. Screen print of the Source tab's coordinate system section (including linear units):

b. Data Set 2: Landscan Population 2005 (I first projected the raster layer, then clipped the projected version to the polygon shape of the adm_0 layer) i. Name: lspop2005_ProjectRaster_Clip1 ii. Projection type: WGS_1984_UTM_Zone_31N iii. Linear units: Meters iv. Screen print of the Source tab's coordinate system section (including linear units):

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Note: I was not able to obtain screenshots of the other datasets’ source labels prior to leaving campus.

3. Clipped Data Sets (screenshots)

4. Calculate Area for Polygon Data sets

I was not able to use Calculate Geometry tool without access to ArcGIS, but GADM polygons have embedded area information, to which I joined population data from the 2008 and 1998 Algerian censuses:

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ONS_c pop_1998 pop_2008_ce OBJECTID ode adm1_unit_long Adm1_name area_sqkm _census nsus 1 001 Wilaya d'Adrar Adrar 439,700 311,615 399,714 2 044 Wilaya de Aïn Defla Ain Defla 4,891 660,342 766,013 3 046 Wilaya de Aïn Temouchent Ain Témouchent 2,379 327,331 371,239 4 016 Wilaya d' Alger 1,190 2,562,428 2,988,145 5 023 Wilaya d' Annaba 1,439 557,818 609,499 6 005 Wilaya de Batna Batna 12,192 962,623 1,119,791 7 008 Wilaya de Béchar Béchar 162,200 225,546 270,061 8 006 Wilaya de Bejaïa 3,268 856,840 912,577 9 007 Wilaya de Biskra 20,986 575,858 721,356 10 009 Wilaya de Blida 1,575 784,283 1,002,937 11 034 Wilaya de Bordj Bou Arréridj Bordj Bou Arréridj 4,115 555,402 628,475 12 010 Wilaya de Bouira Bouira 4,439 629,560 695,583 13 035 Wilaya de Boumerdes Boumèrdes 1,356 647,389 802,083 14 002 Wilaya de Chlef 4,795 858,695 1,002,088 15 025 Wilaya de Constantine Constantine 2,187 810,914 938,475 16 017 Wilaya de Djelfa 66,415 797,706 1,092,184 17 032 Wilaya d' El Bayadh 78,870 168,789 228,624 18 039 Wilaya d' El Oued 54,573 504,401 647,548 19 036 Wilaya d' El Tarf 3,339 352,588 408,414 20 047 Wilaya de Ghardaïa 86,105 300,516 363,598 21 024 Wilaya de Guelma 4,101 430,000 482,430 22 033 Wilaya d' Illizi 285,000 34,108 52,333 23 018 Wilaya de Jijel 2,577 573,208 636,948 24 040 Wilaya de Khenchela 9,811 327,917 386,683 25 003 Wilaya de Laghouat 25,057 317,125 455,602

5. Spatial Analysis Tools and Commentary

a. Tool 1 – Kernel Density Tool i. Question: In which areas of Algeria were violent events most prolific? ii. Methodology: Having already selected a subset of violent events in Algeria, namely battles between insurgents and the government and attacks on civilians, between 1997 and 2002, and projected the relevant administrative layers, I ran the density tool. I did so several times, experimenting between different search radii, and settled on the radius of 50,000 km, and displayed the data using quantiles and five classes. I then reclassified the dataset to obtain a layer with separated classes for analysis.

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iii. Why answer(s) might be wrong: Had I chosen a different search radius and/or different methods of symbolizing the density raster, I may have ended up with a drastically different reclassified density layer for analyzing my five factors. I chose settings rather arbitrarily, based on visual impact, and leaning on my experience with the Hot Spot Analysis lab, as well as Sarah Charlton’s methodology as described in her project paper. iv. Technical difficulties: my goal for this layer was to find a radius that would adequately display a visually stimulating result at the countrywide level, i.e. with a 7 or 8 inch map of the massive country of Algeria, wherein attacks were concentrated very much in the north. Technical difficulties included running out of drive space after running several density analyses, and getting a bit turned around with the various Symbology options, prior to reclassifying the final raster. In addition, I

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neglected to properly set the Geoprocessing Environment tools, specifically the mask tool, and thus ended up with a density map that was not clipped to Algeria’s administrative boundary level 0. I solved this problem in exporting the map by altering the View options, and displaying datasets clipped to that layer. This setting, however, resulted in a much slower ArcGIS experience in that map.

b. Tool 2 – Euclidean Distance i. Question: How far, in meters, from major roads did most conflicts occur? ii. Methodology: Having defined the correct geoprocessing environment for the roads dataset, I ran the Euclidean Distance tool with 10 classes and manually defined breaks of 10,000 meters between them, from major roads. My intention, as with all factor data layers, was to use these layers as the factors upon which to overlay the aforementioned conflict density layer.

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iii. Why answer(s) might be wrong: Again, the arbitrary nature of the settings I selected in the breaks between distances, for example, indicates the extent to which visual impact played a primary role in my project. Other settings may have resulted in drastically different results both with the roads layer alone, as well as with the conflict density overlay. iv. Technical difficulties: Initially, incorrect geoprocessing environment settings prevented me from running this tool properly.

c. Tool 3 – Spatial Statistics as Table i. Question: How might my five geographic, demographic, and political factors (elevation, population density, distance from major roads, land cover type, and election results) coincide with the reclassified conflict density layer? In other words, how will factor values correlate with mean conflict density values? ii. Methodology: Using each of the aforementioned factor layers, I designated the conflict density raster as the analysis layer, and elected to analyze using mean conflict density values. Then, I exported the resulting tables as database files, edited and formatted them with Excel, ranking each table by the mean conflict density value.

iii. Why answer(s) might be wrong: These answers could have become conflated as I formatted and edited tables both in ArcGIS and in Excel. iv. Technical difficulties: Occasionally, I selected the wrong tabular heading (or raster layers) when calculating spatial statistics, ending up with either a blank table, or a massive table with values for every raster pixel.

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