1 00:00:01,270 --> 00:00:03,150 - [Brian] The second half of the Module Three lectures, 2 00:00:03,150 --> 00:00:04,970 we'll look at methods for summarizing vector 3 00:00:04,970 --> 00:00:07,250 or raster data in one dataset 4 00:00:07,250 --> 00:00:10,000 that fall within boundaries defined by another dataset. 5 00:00:11,200 --> 00:00:12,750 You're probably scratching your head 6 00:00:12,750 --> 00:00:14,630 wondering what I mean by that. 7 00:00:14,630 --> 00:00:16,720 We've seen examples of this before 8 00:00:16,720 --> 00:00:18,910 but not on this level of detail. 9 00:00:18,910 --> 00:00:20,790 We'll look at both vector and raster approaches 10 00:00:20,790 --> 00:00:22,070 to summarizing data 11 00:00:22,070 --> 00:00:25,380 to resolving a common problem in spatial analysis. 12 00:00:25,380 --> 00:00:27,790 You've got two different datasets with different boundaries 13 00:00:27,790 --> 00:00:31,090 or different geometries or even different data models. 14 00:00:31,090 --> 00:00:32,640 And you want to know how much of one 15 00:00:32,640 --> 00:00:34,320 is contained by the other 16 00:00:34,320 --> 00:00:36,830 or how much of each type of one 17 00:00:36,830 --> 00:00:39,700 is contained by each boundary of the other. 18 00:00:39,700 --> 00:00:41,520 Still not making sense? 19 00:00:41,520 --> 00:00:43,050 Well, that's on me. 20 00:00:43,050 --> 00:00:44,390 Let's try looking at some pictures 21 00:00:44,390 --> 00:00:46,290 to see if that will clear anything up. 22 00:00:47,640 --> 00:00:50,100 Let's start by talking about vector data. 23 00:00:50,100 --> 00:00:52,250 Summarizing the values of one vector dataset 24 00:00:52,250 --> 00:00:55,130 within the bounds of another vector dataset. 25 00:00:55,130 --> 00:00:58,790 We'll look at the polygons inside polygon scenario first. 26 00:00:58,790 --> 00:01:00,440 Look at the scenario on the left. 27 00:01:01,400 --> 00:01:04,030 You have a single bounding polygon in green 28 00:01:04,030 --> 00:01:06,580 and a single summary polygon in blue 29 00:01:06,580 --> 00:01:09,400 that is fully contained inside the bounding polygon. 30 00:01:09,400 --> 00:01:11,520 That's about as simple as it gets. 31 00:01:11,520 --> 00:01:13,460 You could look at the attribute tables of each 32 00:01:13,460 --> 00:01:15,340 and determine what the percentage was 33 00:01:15,340 --> 00:01:17,924 or you could look at the attribute table of your blue circle 34 00:01:17,924 --> 00:01:20,710 and determine the area of that one polygon 35 00:01:20,710 --> 00:01:23,510 and equate that to the area of the summary feature 36 00:01:23,510 --> 00:01:24,973 within the boundary feature. 37 00:01:26,350 --> 00:01:27,740 What about the less symbol cases 38 00:01:27,740 --> 00:01:30,710 like the two we see in the images on the right 39 00:01:30,710 --> 00:01:33,070 where either the polygon you're trying to summarize. 40 00:01:33,070 --> 00:01:34,090 the blue one, 41 00:01:34,090 --> 00:01:36,820 is not fully contained by the bounding polygon 42 00:01:36,820 --> 00:01:39,750 or what if you're dealing with multiple boundaries 43 00:01:39,750 --> 00:01:41,353 and multiple input polygons? 44 00:01:42,370 --> 00:01:45,240 In this case, you have a few different options 45 00:01:45,240 --> 00:01:46,230 at least partially guided 46 00:01:46,230 --> 00:01:48,330 by your preferred geo-processing workflow. 47 00:01:49,220 --> 00:01:52,100 From the overlay toolkit, you could use tools like union, 48 00:01:52,100 --> 00:01:55,800 intersect or a portion polygon among others. 49 00:01:55,800 --> 00:01:58,780 Alternatively, you could look in the Summarize Data toolkit 50 00:01:58,780 --> 00:02:02,133 to find Summarize Within or Summarize Nearby as options. 51 00:02:03,070 --> 00:02:05,200 I highlighted my personal preferences in yellow 52 00:02:05,200 --> 00:02:07,400 but don't let that limit what you try. 53 00:02:07,400 --> 00:02:09,160 I've been doing some of these tasks the same way 54 00:02:09,160 --> 00:02:12,180 since I learned how to perform geospatial analysis 55 00:02:12,180 --> 00:02:13,880 and some habits are hard to break. 56 00:02:15,530 --> 00:02:17,080 What about summarizing line features 57 00:02:17,080 --> 00:02:19,310 within polygon boundaries? 58 00:02:19,310 --> 00:02:21,830 Once again, we have the simple case over on the left 59 00:02:21,830 --> 00:02:24,410 where a single line feature is fully contained 60 00:02:24,410 --> 00:02:25,833 within our single polygon. 61 00:02:26,760 --> 00:02:29,593 Scan right to see similar scenarios to the last slide. 62 00:02:30,430 --> 00:02:32,130 What are our options here? 63 00:02:32,130 --> 00:02:33,870 All the same as before? 64 00:02:33,870 --> 00:02:35,300 Not in this case. 65 00:02:35,300 --> 00:02:38,860 Remember, the Union tool requires polygon input datasets. 66 00:02:38,860 --> 00:02:40,313 So it's not applicable here. 67 00:02:41,190 --> 00:02:44,080 Intersect and spatial joint are still available to you 68 00:02:44,080 --> 00:02:46,760 but do proceed with caution if you're pursuing a ladder 69 00:02:46,760 --> 00:02:48,113 as your preferred approach. 70 00:02:49,760 --> 00:02:51,730 The last are a possible three combinations 71 00:02:51,730 --> 00:02:54,680 is summarizing points within polygons. 72 00:02:54,680 --> 00:02:55,960 Once again, we have the simple case 73 00:02:55,960 --> 00:02:57,620 over on the left-hand side. 74 00:02:57,620 --> 00:02:59,350 A point is either inside a boundary 75 00:02:59,350 --> 00:03:00,840 or it's outside a boundary. 76 00:03:00,840 --> 00:03:02,760 Those are the only two options. 77 00:03:02,760 --> 00:03:06,080 It's sort of a tie goes to the runner type situation. 78 00:03:06,080 --> 00:03:07,990 Assuming that you don't have too many features, 79 00:03:07,990 --> 00:03:10,500 a visual assessment might get the job done. 80 00:03:10,500 --> 00:03:12,080 But I'd probably still rely on the computer 81 00:03:12,080 --> 00:03:13,780 to get the count right. 82 00:03:13,780 --> 00:03:15,990 And once again, we see the same list of options 83 00:03:15,990 --> 00:03:16,840 available to us 84 00:03:16,840 --> 00:03:19,203 if we were analyzing lines within polygons. 85 00:03:21,050 --> 00:03:23,460 Let's get to some specific examples. 86 00:03:23,460 --> 00:03:25,130 We'll look at a couple of different approaches 87 00:03:25,130 --> 00:03:27,290 to get to the same endpoint. 88 00:03:27,290 --> 00:03:29,890 The examples will use trail buffers and town boundaries 89 00:03:29,890 --> 00:03:31,500 as input datasets 90 00:03:31,500 --> 00:03:33,169 and we'll compute how much of a town 91 00:03:33,169 --> 00:03:35,069 is comprised of a trail buffer 92 00:03:35,069 --> 00:03:38,170 before expanding the problem to compute how much of a town 93 00:03:38,170 --> 00:03:41,100 is of a particular kind of trail buffer. 94 00:03:41,100 --> 00:03:43,900 We'll start by looking at the union geo-processing tool. 95 00:03:45,980 --> 00:03:48,410 On the left hand side, we see the data we're working with 96 00:03:48,410 --> 00:03:50,010 for this example. 97 00:03:50,010 --> 00:03:52,727 In green, we've got buffered areas around trails 98 00:03:52,727 --> 00:03:54,590 and the gray with black outlines, 99 00:03:54,590 --> 00:03:56,200 we've got our town boundary units 100 00:03:56,200 --> 00:03:57,600 within the Mud River Valley. 101 00:03:58,710 --> 00:04:00,590 We want to calculate how much of each town 102 00:04:00,590 --> 00:04:03,250 is comprised of this 500 meter trail buffer, 103 00:04:03,250 --> 00:04:05,150 the green polygons. 104 00:04:05,150 --> 00:04:07,160 Before we get back to the Union tool, 105 00:04:07,160 --> 00:04:08,970 I'll highlight one technique that I like to use 106 00:04:08,970 --> 00:04:10,310 to keep track of my data 107 00:04:10,310 --> 00:04:12,130 when combining multiple datasets 108 00:04:12,130 --> 00:04:14,490 using an overlay operation. 109 00:04:14,490 --> 00:04:17,130 I include a variable that describes the dataset. 110 00:04:17,130 --> 00:04:21,580 So, for example, I created an attribute named IsTrailBuffer 111 00:04:21,580 --> 00:04:23,100 set a data type of short 112 00:04:23,100 --> 00:04:25,990 since it will only contain values of zero and one. 113 00:04:25,990 --> 00:04:28,413 And then I assign all the buffer polygons 114 00:04:28,413 --> 00:04:30,403 a value of one for this attribute. 115 00:04:31,370 --> 00:04:34,390 Remember, the Union tool combines all of the attributes 116 00:04:34,390 --> 00:04:35,910 of your input datasets 117 00:04:35,910 --> 00:04:38,560 and writes them to your output dataset. 118 00:04:38,560 --> 00:04:40,160 This makes it a bit easier to track 119 00:04:40,160 --> 00:04:43,510 where specific data come from in the composite dataset 120 00:04:43,510 --> 00:04:46,226 while also preserving those values to be used later 121 00:04:46,226 --> 00:04:49,273 to identify specific features of the new dataset. 122 00:04:51,060 --> 00:04:53,090 Once I performed the Union operation, 123 00:04:53,090 --> 00:04:55,450 again using the trails buffer and the town boundaries 124 00:04:55,450 --> 00:04:56,830 as my inputs, 125 00:04:56,830 --> 00:04:59,460 a new output feature class is produced 126 00:04:59,460 --> 00:05:02,240 and we can see that on the left side of the slide. 127 00:05:02,240 --> 00:05:04,350 The Union tool has broken down those polygons 128 00:05:04,350 --> 00:05:06,540 into multiple smaller polygons 129 00:05:06,540 --> 00:05:08,970 based on where they fell in relation to the other polygons 130 00:05:08,970 --> 00:05:10,033 of the input data. 131 00:05:11,810 --> 00:05:13,130 Let's look at the attribute table 132 00:05:13,130 --> 00:05:14,800 of the town boundary dataset 133 00:05:14,800 --> 00:05:16,853 and the output of the Union operation. 134 00:05:17,810 --> 00:05:20,270 Town boundaries table is on top. 135 00:05:20,270 --> 00:05:22,780 Notice that it includes that six digit FIPs code 136 00:05:22,780 --> 00:05:24,880 that is unique to each town. 137 00:05:24,880 --> 00:05:27,690 That's important because it means we can join the output 138 00:05:27,690 --> 00:05:29,413 to the original boundary dataset. 139 00:05:30,410 --> 00:05:33,010 We see seven records in the output feature class 140 00:05:33,010 --> 00:05:34,470 in the bottom table 141 00:05:34,470 --> 00:05:37,070 and note that the FIPs code is present here as well. 142 00:05:38,950 --> 00:05:41,900 If I were to use Select By attributes to select the polygons 143 00:05:41,900 --> 00:05:44,670 with an IsTrailBuffer value of one, 144 00:05:44,670 --> 00:05:47,020 notice that includes features which extend 145 00:05:47,020 --> 00:05:49,820 beyond the boundary of the three Mud River Valley towns. 146 00:05:50,900 --> 00:05:53,730 If I look at the attribute table for that selection, 147 00:05:53,730 --> 00:05:55,670 I see something interesting. 148 00:05:55,670 --> 00:05:57,480 Of course the selected records all have 149 00:05:57,480 --> 00:05:59,740 an IsTrailBuffer value of one, 150 00:05:59,740 --> 00:06:01,460 that was the expression we evaluated 151 00:06:01,460 --> 00:06:03,880 using Select By attributes. 152 00:06:03,880 --> 00:06:06,600 Instead, look at the FIPs six code 153 00:06:06,600 --> 00:06:08,993 or the FID for the Mud River Valley town. 154 00:06:10,179 --> 00:06:11,440 The FIPs code equals zero 155 00:06:11,440 --> 00:06:15,500 for that same record that the FID code equals negative one. 156 00:06:15,500 --> 00:06:16,960 Those are the polygons that fall 157 00:06:16,960 --> 00:06:18,833 outside of the town boundaries. 158 00:06:20,280 --> 00:06:22,740 If I revise my Select By attribute expression 159 00:06:22,740 --> 00:06:25,130 to include a trail buffer value of one 160 00:06:25,130 --> 00:06:27,690 and a FIPs code not equal to zero, 161 00:06:27,690 --> 00:06:29,720 I select fewer records. 162 00:06:29,720 --> 00:06:31,930 Only those polygons within the town boundaries 163 00:06:31,930 --> 00:06:33,553 that are also trail buffers. 164 00:06:35,200 --> 00:06:38,340 From here, you can export just the table. 165 00:06:38,340 --> 00:06:39,870 We've used the Export features tool 166 00:06:39,870 --> 00:06:42,910 to create a new feature class many times before. 167 00:06:42,910 --> 00:06:45,580 This time, we're only going to export the table 168 00:06:45,580 --> 00:06:47,120 because we're going to use it in the next step 169 00:06:47,120 --> 00:06:48,910 to join back to our boundary data, 170 00:06:48,910 --> 00:06:50,493 the Mud River Valley towns. 171 00:06:51,470 --> 00:06:54,510 Once I select Export Table from the list of options, 172 00:06:54,510 --> 00:06:56,963 an interface that should look familiar will open. 173 00:06:58,540 --> 00:06:59,650 It looks pretty much the same 174 00:06:59,650 --> 00:07:02,600 as the Export features dialog box. 175 00:07:02,600 --> 00:07:04,190 If I expand the field section 176 00:07:04,190 --> 00:07:06,090 of the Export Table dialog box, 177 00:07:06,090 --> 00:07:08,330 you see a list of all the fields that will be written 178 00:07:08,330 --> 00:07:09,827 to the output table, 179 00:07:09,827 --> 00:07:11,677 not shown in the image in the center. 180 00:07:13,160 --> 00:07:13,993 Over on the right, 181 00:07:13,993 --> 00:07:16,180 we can see that I've narrowed that down a bit. 182 00:07:17,280 --> 00:07:19,820 I bleated fields from the list by hovering over them 183 00:07:19,820 --> 00:07:22,440 and clicking the red X that appears. 184 00:07:22,440 --> 00:07:24,270 Remember, you're not deleting the attributes 185 00:07:24,270 --> 00:07:25,930 from the input dataset. 186 00:07:25,930 --> 00:07:28,200 Rather, you're excluding them from being written 187 00:07:28,200 --> 00:07:29,703 to the output dataset. 188 00:07:30,590 --> 00:07:31,960 Let's make one more adjustment 189 00:07:31,960 --> 00:07:34,103 before completing our export operation. 190 00:07:35,550 --> 00:07:37,340 I switched over to the Properties tab 191 00:07:37,340 --> 00:07:38,960 in the image on the right. 192 00:07:38,960 --> 00:07:41,620 Here I can change the name of the shape area attribute 193 00:07:41,620 --> 00:07:43,130 to something more descriptive 194 00:07:43,130 --> 00:07:45,460 to make it easier to keep track of. 195 00:07:45,460 --> 00:07:48,650 In this case, I'll rename the attribute buffer area 196 00:07:48,650 --> 00:07:50,763 so I know exactly what it represents. 197 00:07:52,410 --> 00:07:54,910 Then once the table's been exported, 198 00:07:54,910 --> 00:07:57,350 we can join it to the original boundary dataset, 199 00:07:57,350 --> 00:07:59,550 the Mud River Valley towns. 200 00:07:59,550 --> 00:08:02,320 we use the FIP six code to do that. 201 00:08:02,320 --> 00:08:04,600 Afterwards, as you can see on the left, 202 00:08:04,600 --> 00:08:07,120 you can use that information to create a core plus map 203 00:08:07,120 --> 00:08:10,830 to symbolize the total area of trail buffers in each town. 204 00:08:10,830 --> 00:08:13,330 If you want to calculate the percent of the area, 205 00:08:13,330 --> 00:08:16,750 create a new attribute, set it to type double 206 00:08:16,750 --> 00:08:19,320 and divide the buffer area by the shape area value 207 00:08:19,320 --> 00:08:20,223 for each town. 208 00:08:21,100 --> 00:08:23,850 Okay, so that was the union approach. 209 00:08:23,850 --> 00:08:25,350 Let's check out Summarize Within 210 00:08:25,350 --> 00:08:28,320 to evaluate how that works in comparison. 211 00:08:28,320 --> 00:08:30,510 Once again, we've got the same input data, 212 00:08:30,510 --> 00:08:33,323 green trail buffers and gray town boundaries. 213 00:08:34,260 --> 00:08:36,830 Look at the tool dialog box on the right. 214 00:08:36,830 --> 00:08:39,426 I set the input polygons to the town boundaries 215 00:08:39,426 --> 00:08:41,310 and the summary features 216 00:08:41,310 --> 00:08:43,920 which I think should be called features to summarize 217 00:08:43,920 --> 00:08:45,543 or the trail buffer polygons. 218 00:08:47,350 --> 00:08:49,210 Assign an output feature class name 219 00:08:49,210 --> 00:08:50,650 and specify which fields you want 220 00:08:50,650 --> 00:08:53,930 the summary statistics to be calculated for. 221 00:08:53,930 --> 00:08:56,820 Once again, we want to compute the sum of the shape area 222 00:08:56,820 --> 00:08:59,363 of the trail buffer polygons within each town. 223 00:09:00,710 --> 00:09:02,823 Click Run to produce the output dataset. 224 00:09:04,480 --> 00:09:06,320 The output feature class attribute table 225 00:09:06,320 --> 00:09:08,590 is displayed on the top right of the slide. 226 00:09:08,590 --> 00:09:09,870 And I include the output table 227 00:09:09,870 --> 00:09:13,460 from the union operation below for comparison. 228 00:09:13,460 --> 00:09:15,680 In short, we've accomplished in a single step 229 00:09:15,680 --> 00:09:19,550 what required multiple steps or geo-processing operations 230 00:09:19,550 --> 00:09:22,053 when we started our journey with the Union tool. 231 00:09:23,130 --> 00:09:25,610 I'll also note that there's slight variations 232 00:09:25,610 --> 00:09:28,690 in the values that you see between the two tables. 233 00:09:28,690 --> 00:09:31,050 This reflects different data processing subtleties 234 00:09:31,050 --> 00:09:32,950 between the Union and Summarize Within 235 00:09:32,950 --> 00:09:34,653 geo-processing operations. 236 00:09:36,700 --> 00:09:39,020 Let's look at one more example. 237 00:09:39,020 --> 00:09:40,380 In the last example, 238 00:09:40,380 --> 00:09:42,790 the trail buffer data had a single value. 239 00:09:42,790 --> 00:09:45,510 One, meaning that all the polygons in the dataset 240 00:09:45,510 --> 00:09:48,480 were trail buffers and nothing else. 241 00:09:48,480 --> 00:09:51,630 In this case, let's change up the data a bit. 242 00:09:51,630 --> 00:09:54,450 The revised version of the data still features trail buffers 243 00:09:54,450 --> 00:09:57,170 with its trail buffer value of one. 244 00:09:57,170 --> 00:10:00,180 It also includes polygons that are not trail buffers 245 00:10:00,180 --> 00:10:03,880 and assigns a value of zero to that same attribute. 246 00:10:03,880 --> 00:10:05,550 I set the same parameter values 247 00:10:05,550 --> 00:10:08,770 for the Summarize Within tool as the last time around. 248 00:10:08,770 --> 00:10:10,930 But this time I use the modified trail buffer data 249 00:10:10,930 --> 00:10:13,040 as the summary dataset. 250 00:10:13,040 --> 00:10:14,920 Let's take a look at the output. 251 00:10:14,920 --> 00:10:16,320 Think this is going to work? 252 00:10:18,220 --> 00:10:21,050 The top table represents our first effort 253 00:10:21,050 --> 00:10:22,240 for Summarize Within. 254 00:10:22,240 --> 00:10:24,190 Remember that was the version that only included 255 00:10:24,190 --> 00:10:26,960 the trail buffers and nothing else. 256 00:10:26,960 --> 00:10:29,200 Note the difference between the summarized area 257 00:10:29,200 --> 00:10:32,390 and the shape area values in that top table. 258 00:10:32,390 --> 00:10:34,540 The summarized areas are all smaller than the area 259 00:10:34,540 --> 00:10:35,980 of the town. 260 00:10:35,980 --> 00:10:37,400 If you look at the bottom table, 261 00:10:37,400 --> 00:10:39,640 you see something very different. 262 00:10:39,640 --> 00:10:42,650 In this case, the summarize area is essentially the same 263 00:10:42,650 --> 00:10:44,760 as the area of the town. 264 00:10:44,760 --> 00:10:45,863 Why might that be? 265 00:10:46,880 --> 00:10:49,300 Think back to that input dataset. 266 00:10:49,300 --> 00:10:51,380 Remember that each town was entirely covered 267 00:10:51,380 --> 00:10:53,570 by the modified trail buffer, 268 00:10:53,570 --> 00:10:56,000 taking on a value of zero, not a buffer 269 00:10:56,000 --> 00:10:58,710 or one is a buffer. 270 00:10:58,710 --> 00:11:01,740 We've asked ArcGIS to summarize those polygons 271 00:11:01,740 --> 00:11:04,130 and not surprisingly as a result, 272 00:11:04,130 --> 00:11:07,730 the summarized values match the area of the town. 273 00:11:07,730 --> 00:11:09,520 This is obviously not correct 274 00:11:09,520 --> 00:11:11,973 and not the result that we were looking for. 275 00:11:13,630 --> 00:11:15,770 Let's try a different approach. 276 00:11:15,770 --> 00:11:18,940 I've modified the data again here to represent the 500 meter 277 00:11:18,940 --> 00:11:22,520 and 1000 meter buffer around the trails in the valley. 278 00:11:22,520 --> 00:11:24,860 In this case, we'll use a group field 279 00:11:24,860 --> 00:11:27,410 to help make better sense of the outputs. 280 00:11:27,410 --> 00:11:29,810 This is the same basic idea as a case field 281 00:11:29,810 --> 00:11:31,080 that we've seen used before 282 00:11:31,080 --> 00:11:33,030 once summarizing the values in a table. 283 00:11:34,000 --> 00:11:36,510 I set up the Summarize Within in the exact same way 284 00:11:36,510 --> 00:11:38,330 in terms of summarizing the shape areas 285 00:11:38,330 --> 00:11:40,580 of the trail buffer polygons. 286 00:11:40,580 --> 00:11:43,280 I'll use the buffer distance as the group field 287 00:11:43,280 --> 00:11:46,720 so I can distinguish those values in the output table. 288 00:11:46,720 --> 00:11:50,210 Lastly, we also need to specify an output group table name 289 00:11:50,210 --> 00:11:51,943 to write additional outputs too. 290 00:11:53,010 --> 00:11:56,300 We'll see what that table represents here in a minute. 291 00:11:56,300 --> 00:11:58,350 Let's look at two tables here. 292 00:11:58,350 --> 00:12:01,870 On the top is the attribute table for my buffer polygons. 293 00:12:01,870 --> 00:12:05,220 The total area of all buffers is shown on the table. 294 00:12:05,220 --> 00:12:07,660 Next, look at the table below. 295 00:12:07,660 --> 00:12:10,140 This is the Summarize Within result. 296 00:12:10,140 --> 00:12:14,510 Note that the value is smaller than the one above. Why? 297 00:12:14,510 --> 00:12:16,770 Remember, some of those buffer polygons 298 00:12:16,770 --> 00:12:19,080 extend beyond the town boundaries. 299 00:12:19,080 --> 00:12:20,590 The difference in those two values 300 00:12:20,590 --> 00:12:23,010 represents the amount of trail buffer polygons 301 00:12:23,010 --> 00:12:24,610 outside of the Mud River Valley. 302 00:12:25,490 --> 00:12:28,700 Two of the things to note about the Summarize Within result. 303 00:12:28,700 --> 00:12:30,320 The table includes a total area 304 00:12:30,320 --> 00:12:32,180 of features that were summarized. 305 00:12:32,180 --> 00:12:34,620 and there's an attribute named JOIN ID 306 00:12:34,620 --> 00:12:36,670 on the right hand side. 307 00:12:36,670 --> 00:12:39,200 Based on its name, I think you can probably figure out 308 00:12:39,200 --> 00:12:40,850 what that's going to be used for. 309 00:12:42,020 --> 00:12:44,210 This slide features the Summarize Within result 310 00:12:44,210 --> 00:12:45,520 on the top left. 311 00:12:45,520 --> 00:12:47,380 This is the same table from the bottom right 312 00:12:47,380 --> 00:12:49,170 of the last slide. 313 00:12:49,170 --> 00:12:52,210 The output group table, that was the other output you needed 314 00:12:52,210 --> 00:12:55,600 to specify a name for in the Summarize Within dialog box 315 00:12:55,600 --> 00:12:58,460 is shown on the bottom right of this slide. 316 00:12:58,460 --> 00:13:00,337 What's going on here? 317 00:13:00,337 --> 00:13:04,080 Notice the JOIN ID and buffer distance columns are included. 318 00:13:04,080 --> 00:13:05,650 On closer inspection, 319 00:13:05,650 --> 00:13:07,200 and notice there are repeat values 320 00:13:07,200 --> 00:13:08,890 for each of the JOIN IDs; 321 00:13:08,890 --> 00:13:10,830 one for the 500 meter buffer, 322 00:13:10,830 --> 00:13:13,400 the other for the 1000 meter buffer. 323 00:13:13,400 --> 00:13:15,640 This will make it difficult to join the output table 324 00:13:15,640 --> 00:13:16,990 to the boundary dataset 325 00:13:16,990 --> 00:13:20,193 like we tend to do after performing summary operations. 326 00:13:22,070 --> 00:13:25,850 One approach here is to derive what's known as a pivot table 327 00:13:25,850 --> 00:13:29,560 A pivot table reduces the redundancy of the table contents. 328 00:13:29,560 --> 00:13:32,030 Think of it as flattening the one to many relationship 329 00:13:32,030 --> 00:13:34,403 we just saw an example of on the last slide. 330 00:13:35,340 --> 00:13:37,650 To create the pivot table, I need to identify 331 00:13:37,650 --> 00:13:41,710 how I want my input table fields to be reorganized, 332 00:13:41,710 --> 00:13:45,480 set my input field to JOIN ID, my pivot field. 333 00:13:45,480 --> 00:13:47,720 That's the one I'm sort of rotating the table around 334 00:13:47,720 --> 00:13:49,410 in a manner of speaking 335 00:13:49,410 --> 00:13:51,740 and the value field to my area calculations 336 00:13:51,740 --> 00:13:53,593 from the Summarize Within operation. 337 00:13:54,690 --> 00:13:57,690 This means we'll list the area of value 338 00:13:57,690 --> 00:14:00,830 under the correct buffer designation for each town 339 00:14:00,830 --> 00:14:01,943 in a single record. 340 00:14:03,550 --> 00:14:07,140 Result of this operation is shown in the lower left corner. 341 00:14:07,140 --> 00:14:10,160 Remember the original version of the table had six rows. 342 00:14:10,160 --> 00:14:11,930 Now we're down to three. 343 00:14:11,930 --> 00:14:13,920 No repeats for the JOIN ID 344 00:14:13,920 --> 00:14:15,590 and it treats each of those distances 345 00:14:15,590 --> 00:14:17,563 as a separate attribute in the table. 346 00:14:19,110 --> 00:14:21,220 If we look at the Summarize Within attribute table 347 00:14:21,220 --> 00:14:24,380 on the top left and the pivot table on the lower right, 348 00:14:24,380 --> 00:14:27,283 we see that a join is now greatly simplified. 349 00:14:28,720 --> 00:14:31,380 And with that result, we can now use 350 00:14:31,380 --> 00:14:34,130 that summary information to display the total area 351 00:14:34,130 --> 00:14:36,603 of each town that is comprised of trail buffers. 352 00:14:37,800 --> 00:14:39,630 That's it for vector data. 353 00:14:39,630 --> 00:14:41,520 In the last lecture of this learning module, 354 00:14:41,520 --> 00:14:44,403 We'll revisit data approaches to summarizing raster data.