WEBVTT 1 00:00:01.290 --> 00:00:03.000 Hi, and welcome to module one 2 00:00:03.000 --> 00:00:07.860 of Advanced GIS, module one will be delivered in two parts. 3 00:00:07.860 --> 00:00:11.640 In the first part, I'll discuss metadata, what it is, how 4 00:00:11.640 --> 00:00:12.690 to write it, and where 5 00:00:12.690 --> 00:00:14.910 to find specific information describing the various 6 00:00:14.910 --> 00:00:17.070 aspects of a dataset. 7 00:00:17.070 --> 00:00:17.903 The second part, 8 00:00:17.903 --> 00:00:21.390 we'll explore data creation modification procedures. 9 00:00:21.390 --> 00:00:24.930 In the introduction to GIS course, we created, calculated 10 00:00:24.930 --> 00:00:28.350 and updated attribute information modifying the structure 11 00:00:28.350 --> 00:00:31.230 and contents of the attribute table. 12 00:00:31.230 --> 00:00:34.590 In this module, you'll learn to create new vector datasets 13 00:00:34.590 --> 00:00:36.183 and modify their contents. 14 00:00:37.140 --> 00:00:40.260 Let's get started with a quick refresher on metadata. 15 00:00:40.260 --> 00:00:45.090 Metadata is data documentation or data about data. 16 00:00:45.090 --> 00:00:47.490 Ideally, metadata describes the data according 17 00:00:47.490 --> 00:00:49.380 to an established standard. 18 00:00:49.380 --> 00:00:52.020 This makes it easier for analysts outside your agency 19 00:00:52.020 --> 00:00:54.030 or organization to use the metadata both 20 00:00:54.030 --> 00:00:56.280 to locate data resources 21 00:00:56.280 --> 00:00:59.283 and ensure it is used appropriately in an analysis. 22 00:01:00.450 --> 00:01:02.220 Following an established standard 23 00:01:02.220 --> 00:01:05.550 and identifying the standard in the metadata itself 24 00:01:05.550 --> 00:01:08.347 alerts the astute data sleuth to the anticipated content 25 00:01:08.347 --> 00:01:10.803 and approach to documenting the data. 26 00:01:12.330 --> 00:01:14.885 State, local and federal agencies are required 27 00:01:14.885 --> 00:01:19.080 to provide metadata for the geospatial data they produce. 28 00:01:19.080 --> 00:01:21.900 They're not all equally adept at doing so. 29 00:01:21.900 --> 00:01:24.180 The closer to home you get, the more likely you're going 30 00:01:24.180 --> 00:01:26.550 to encounter metadata challenges. 31 00:01:26.550 --> 00:01:29.370 Large agencies and organizations are likely to have a team 32 00:01:29.370 --> 00:01:32.670 of geospatial technicians while a local government 33 00:01:32.670 --> 00:01:34.980 or NGO is more likely to have a small handful 34 00:01:34.980 --> 00:01:37.773 of employees each wearing multiple hats. 35 00:01:39.210 --> 00:01:41.488 Why do we have metadata? 36 00:01:41.488 --> 00:01:43.500 Metadatasets the standard for the way 37 00:01:43.500 --> 00:01:45.480 that data is developed. 38 00:01:45.480 --> 00:01:48.030 For example, when establishing an attribute schema 39 00:01:48.030 --> 00:01:50.700 for a dataset, you might specify a domain range 40 00:01:50.700 --> 00:01:52.380 to limit the values that can be entered 41 00:01:52.380 --> 00:01:54.270 for the attribute. 42 00:01:54.270 --> 00:01:55.350 Suppose you want to include 43 00:01:55.350 --> 00:01:58.560 an attribute representing test scores in a dataset. 44 00:01:58.560 --> 00:02:01.350 There are no extra credit questions on the exam. 45 00:02:01.350 --> 00:02:04.503 This means that scores can only range from 0 to 100. 46 00:02:05.340 --> 00:02:08.460 Specifying this constraint in the domain range eliminates 47 00:02:08.460 --> 00:02:10.980 the possibility of entering a score that is not possible 48 00:02:10.980 --> 00:02:14.673 to achieve either less than 0 or greater than 100. 49 00:02:16.050 --> 00:02:18.300 Metadata can also facilitate data search 50 00:02:18.300 --> 00:02:19.860 and retrieval via keywords 51 00:02:19.860 --> 00:02:22.530 and tags associated with the dataset. 52 00:02:22.530 --> 00:02:24.540 These could represent DatasetThemes 53 00:02:24.540 --> 00:02:28.590 like LAMB cover developing entity like the state of Vermont 54 00:02:28.590 --> 00:02:32.883 or the USGS or even specific attribution within a dataset. 55 00:02:34.020 --> 00:02:37.177 Lastly, metadata guides the appropriate use of data 56 00:02:37.177 --> 00:02:40.080 by providing an abstract describing the dataset 57 00:02:40.080 --> 00:02:42.660 in general terms, the specific purpose 58 00:02:42.660 --> 00:02:44.760 for which the data was developed 59 00:02:44.760 --> 00:02:46.260 and any known use constraints 60 00:02:46.260 --> 00:02:48.090 that may render the data unsuitable 61 00:02:48.090 --> 00:02:49.773 for a particular analysis. 62 00:02:50.640 --> 00:02:52.320 Suppose you want to perform an analysis 63 00:02:52.320 --> 00:02:54.600 of Allstate Parks in Vermont. 64 00:02:54.600 --> 00:02:57.540 The VCGI server is down for maintenance, 65 00:02:57.540 --> 00:03:00.120 but you need to complete your analysis today. 66 00:03:00.120 --> 00:03:02.700 The only dataset you can find related to state parks is 67 00:03:02.700 --> 00:03:05.310 by a guy at UVM named Brian. 68 00:03:05.310 --> 00:03:08.112 His data does not represent all the parks 69 00:03:08.112 --> 00:03:10.170 in the state, only the ones he's visited. 70 00:03:10.170 --> 00:03:12.270 Would this data suffice? 71 00:03:12.270 --> 00:03:14.160 Likely not, and it's better to learn that 72 00:03:14.160 --> 00:03:16.380 before investing the time, download the data 73 00:03:16.380 --> 00:03:17.613 and run your analysis. 74 00:03:18.630 --> 00:03:21.960 This brings me to one of the real takeaways of metadata. 75 00:03:21.960 --> 00:03:25.050 Create metadata so the data developers don't have 76 00:03:25.050 --> 00:03:26.460 to communicate individually 77 00:03:26.460 --> 00:03:30.570 with respective data users regarding dataset details 78 00:03:30.570 --> 00:03:32.400 and so that data users can be certain 79 00:03:32.400 --> 00:03:34.380 that the data they located is suitable 80 00:03:34.380 --> 00:03:35.883 for their intended purpose. 81 00:03:36.750 --> 00:03:38.010 The better your metadata, 82 00:03:38.010 --> 00:03:40.140 the less likely you'll be responding to help requests 83 00:03:40.140 --> 00:03:42.603 to enable others to use the data you produced. 84 00:03:46.200 --> 00:03:47.790 In the US, there's an entity known 85 00:03:47.790 --> 00:03:50.400 as the Federal Geographic Data Committee. 86 00:03:50.400 --> 00:03:51.960 They developed the content standard 87 00:03:51.960 --> 00:03:55.444 for geospatial metadata back in 1998 and have adjusted 88 00:03:55.444 --> 00:03:57.873 and upgraded this standard over time. 89 00:03:58.800 --> 00:04:01.050 The primary purpose of the metadata standard is 90 00:04:01.050 --> 00:04:03.300 to provide a common set of terminology 91 00:04:03.300 --> 00:04:06.870 and definitions for the documentation of geospatial data 92 00:04:06.870 --> 00:04:08.103 or really any data. 93 00:04:09.180 --> 00:04:12.570 All federal agencies are required to use this standard. 94 00:04:12.570 --> 00:04:15.510 Most state agencies also meet the standards as well, 95 00:04:15.510 --> 00:04:18.060 although sometimes when dealing with historic data, 96 00:04:18.060 --> 00:04:20.100 it's a little hit or miss. 97 00:04:20.100 --> 00:04:22.500 I tend to think of metadata in this way. 98 00:04:22.500 --> 00:04:24.900 The closer you are to home, meaning local government 99 00:04:24.900 --> 00:04:27.120 or NGO, the more likely you are 100 00:04:27.120 --> 00:04:29.910 to encounter additional challenges with metadata 101 00:04:29.910 --> 00:04:32.580 or perhaps no metadata at all. 102 00:04:32.580 --> 00:04:34.170 In that case, it's best to reach out 103 00:04:34.170 --> 00:04:36.360 to the data developer to get questions answered 104 00:04:36.360 --> 00:04:38.883 before you use that data for any analysis. 105 00:04:41.010 --> 00:04:43.530 Generally speaking, metadata can be grouped 106 00:04:43.530 --> 00:04:45.990 into the following six categories. 107 00:04:45.990 --> 00:04:48.183 Let's take a look at the details of each. 108 00:04:49.470 --> 00:04:51.000 The data identification section 109 00:04:51.000 --> 00:04:53.430 includes all elements you might expect. 110 00:04:53.430 --> 00:04:56.364 General information like a dataset description, the agency 111 00:04:56.364 --> 00:04:59.866 or organization that created or curated the data, the name 112 00:04:59.866 --> 00:05:01.770 of the person within the organization 113 00:05:01.770 --> 00:05:04.800 that performed the data development or analysis procedure, 114 00:05:04.800 --> 00:05:06.870 and even the name of the contact person within the 115 00:05:06.870 --> 00:05:09.513 organization responsible for data distribution. 116 00:05:10.410 --> 00:05:12.240 There's also information about access 117 00:05:12.240 --> 00:05:15.060 and use constraints for a dataset. 118 00:05:15.060 --> 00:05:16.800 This is particularly relevant if you're dealing 119 00:05:16.800 --> 00:05:19.080 with some information that might be classified 120 00:05:19.080 --> 00:05:21.363 or otherwise feature restricted access. 121 00:05:22.200 --> 00:05:24.210 I don't have a security clearance, although 122 00:05:24.210 --> 00:05:26.820 that's probably what I'd still tell you even if I did 123 00:05:26.820 --> 00:05:29.130 and don't often work with sensitive data, 124 00:05:29.130 --> 00:05:31.680 but early enough in my career that I was a summer intern 125 00:05:31.680 --> 00:05:34.470 for the South Carolina Coastal Management Agency, 126 00:05:34.470 --> 00:05:35.601 I digitized point locations 127 00:05:35.601 --> 00:05:38.790 of civil war artifacts along the South Carolina coast 128 00:05:38.790 --> 00:05:42.810 from existing paper maps, I was only able to access the data 129 00:05:42.810 --> 00:05:45.750 because it was an agency priority for evaluating zoning 130 00:05:45.750 --> 00:05:48.210 and development permit applications. 131 00:05:48.210 --> 00:05:51.000 Use of the data was limited to senior personnel 132 00:05:51.000 --> 00:05:54.093 and even I couldn't access it after the data was developed. 133 00:05:55.140 --> 00:05:58.920 Lastly here, data currency describes the date of production 134 00:05:58.920 --> 00:06:01.620 or last update for the dataset. 135 00:06:01.620 --> 00:06:03.620 Unfortunately, this section does not provide insights 136 00:06:03.620 --> 00:06:05.373 into Bitcoin mining. 137 00:06:07.950 --> 00:06:11.010 The data quality section includes elements describing the 138 00:06:11.010 --> 00:06:14.100 positional and attribute accuracy of the data, 139 00:06:14.100 --> 00:06:16.800 important factors to communicate to the end user of the data 140 00:06:16.800 --> 00:06:19.203 to ensure appropriate fitness for use. 141 00:06:20.130 --> 00:06:23.220 You'll also find information about data completeness. 142 00:06:23.220 --> 00:06:24.690 Has all the work been done 143 00:06:24.690 --> 00:06:26.820 or is this a preliminary dataset that's been released 144 00:06:26.820 --> 00:06:29.133 while the final data set undergoes review? 145 00:06:30.150 --> 00:06:33.240 The lineage provides a history of the dataset identifying 146 00:06:33.240 --> 00:06:35.820 where the original dataset was obtained, 147 00:06:35.820 --> 00:06:37.890 an existing dataset from another organization 148 00:06:37.890 --> 00:06:39.840 that you modified to meet your needs 149 00:06:39.840 --> 00:06:42.483 or a raw dataset collection effort. 150 00:06:43.320 --> 00:06:45.210 The processing steps section details 151 00:06:45.210 --> 00:06:46.440 the data processing steps 152 00:06:46.440 --> 00:06:49.080 that have been performed on the data itself. 153 00:06:49.080 --> 00:06:50.910 Ideally, you could review the process steps 154 00:06:50.910 --> 00:06:52.800 to reproduce the intermediate 155 00:06:52.800 --> 00:06:55.560 and final datasets of an analysis. 156 00:06:55.560 --> 00:06:58.590 In my opinion, the more detail here, the better. 157 00:06:58.590 --> 00:07:00.930 Think of it this way, unless you work on a lot 158 00:07:00.930 --> 00:07:01.860 of projects, it's 159 00:07:01.860 --> 00:07:05.190 likely you'll remember the general details of a dataset. 160 00:07:05.190 --> 00:07:08.010 Details you'd find in the overview section, 161 00:07:08.010 --> 00:07:10.200 but trying to remember the specific roadmap 162 00:07:10.200 --> 00:07:13.590 or geo processing workflow used to go from raw data 163 00:07:13.590 --> 00:07:17.460 to final product may be beyond your mental capacity. 164 00:07:17.460 --> 00:07:20.160 I can barely remember which ski runs I took yesterday. 165 00:07:21.570 --> 00:07:23.940 The end result should also be easier for others 166 00:07:23.940 --> 00:07:25.980 to interpret, which should minimize the need for you 167 00:07:25.980 --> 00:07:27.153 to explain the data. 168 00:07:29.100 --> 00:07:31.050 The spatial reference section provides a lot 169 00:07:31.050 --> 00:07:32.490 of the same content that you would see 170 00:07:32.490 --> 00:07:35.550 in the layer properties interface for a dataset. 171 00:07:35.550 --> 00:07:36.870 This information can be found 172 00:07:36.870 --> 00:07:38.580 in the horizontal coordinate system 173 00:07:38.580 --> 00:07:41.253 and the geodetic model sections of the metadata. 174 00:07:42.600 --> 00:07:44.820 For me, the most frequently accessed section 175 00:07:44.820 --> 00:07:48.270 of metadata is the entity and attribute information. 176 00:07:48.270 --> 00:07:51.390 This section provides information about the entity itself. 177 00:07:51.390 --> 00:07:54.360 Is it a vector or raster dataset? 178 00:07:54.360 --> 00:07:56.798 If it's a vector dataset, does it feature point line 179 00:07:56.798 --> 00:07:59.610 or polygon geometry? 180 00:07:59.610 --> 00:08:00.810 In theory, you should be able 181 00:08:00.810 --> 00:08:04.020 to determine this yourself just by looking at the data, 182 00:08:04.020 --> 00:08:06.493 but why waste the time dealing with a large dataset only 183 00:08:06.493 --> 00:08:09.180 to find it's not what you need when 184 00:08:09.180 --> 00:08:11.673 instead you can consult the documentation first. 185 00:08:12.750 --> 00:08:15.810 Deciphering attributes is a whole different ballgame. 186 00:08:15.810 --> 00:08:18.360 The attribute information includes a definition 187 00:08:18.360 --> 00:08:21.720 of an attribute describing what the attribute represents, 188 00:08:21.720 --> 00:08:25.620 the defining authority and any value restrictions. 189 00:08:25.620 --> 00:08:28.830 Look at the examples on the right side of the slide. 190 00:08:28.830 --> 00:08:30.741 The upper image represents the data definition 191 00:08:30.741 --> 00:08:33.960 for block groups in the 2010 census. 192 00:08:33.960 --> 00:08:36.840 The US Census Bureau is the defining authority 193 00:08:36.840 --> 00:08:38.310 and the value of zero is assigned 194 00:08:38.310 --> 00:08:40.740 when a block group is located on water, 195 00:08:40.740 --> 00:08:43.020 think Great Lake states or houseboats 196 00:08:43.020 --> 00:08:45.750 in the Puget Sound or one through nine, 197 00:08:45.750 --> 00:08:47.550 otherwise, depending on the number 198 00:08:47.550 --> 00:08:49.890 of block groups within a census tract. 199 00:08:49.890 --> 00:08:52.593 The next level of the census geography hierarchy, 200 00:08:53.580 --> 00:08:54.887 the lower image reveals the 201 00:08:54.887 --> 00:08:57.750 for the total population attribute. 202 00:08:57.750 --> 00:08:59.310 Note two things here. 203 00:08:59.310 --> 00:09:02.733 There are no value constraints and no defining authority. 204 00:09:03.960 --> 00:09:06.450 You could specify that the values must be numeric 205 00:09:06.450 --> 00:09:09.900 with a minimum value of zero and no maximum value. 206 00:09:09.900 --> 00:09:12.870 You might also choose to specify the US Census Bureau 207 00:09:12.870 --> 00:09:14.610 as the defining authority, 208 00:09:14.610 --> 00:09:15.840 but that should be apparent based 209 00:09:15.840 --> 00:09:17.913 on the data resource itself in this case. 210 00:09:19.050 --> 00:09:21.660 This information is also what allows the user 211 00:09:21.660 --> 00:09:24.210 to translate from an attribute name represented 212 00:09:24.210 --> 00:09:27.840 as an alphanumeric code to a specific concept, 213 00:09:27.840 --> 00:09:31.443 thereby facilitating the interpretation of the data values. 214 00:09:33.870 --> 00:09:36.390 The data distribution section provides contact 215 00:09:36.390 --> 00:09:39.060 information for the person in charge of distributing data 216 00:09:39.060 --> 00:09:41.580 for the agency that's producing it. 217 00:09:41.580 --> 00:09:43.260 This is a little bit less crucial now 218 00:09:43.260 --> 00:09:45.420 that most data can either be downloaded directly 219 00:09:45.420 --> 00:09:49.170 from the web or linked to via a map server. 220 00:09:49.170 --> 00:09:51.697 The need to contact someone directly is likely higher 221 00:09:51.697 --> 00:09:54.750 for accessing historical archives. 222 00:09:54.750 --> 00:09:57.240 Some of this data may never be digitized 223 00:09:57.240 --> 00:09:58.320 or perhaps the process 224 00:09:58.320 --> 00:10:00.660 of digitizing these historic records is slowed 225 00:10:00.660 --> 00:10:03.630 by budget realities and time constraints. 226 00:10:03.630 --> 00:10:06.450 Alternatively, you may find certain data distributed 227 00:10:06.450 --> 00:10:09.270 in an ancient format like a CD ROM. 228 00:10:09.270 --> 00:10:11.310 The data distribution section should provide you 229 00:10:11.310 --> 00:10:13.590 with all you need to know about ordering data 230 00:10:13.590 --> 00:10:15.843 and any associated fees. 231 00:10:17.460 --> 00:10:20.220 Lastly, there's the meta-metadata 232 00:10:20.220 --> 00:10:22.680 or the data about the metadata. 233 00:10:22.680 --> 00:10:25.140 When was the metadata created or updated? 234 00:10:25.140 --> 00:10:28.680 Who performed the procedure and what standard was used? 235 00:10:28.680 --> 00:10:30.780 I mentioned the FGDC earlier 236 00:10:30.780 --> 00:10:33.720 and their role in setting the standard for the US. 237 00:10:33.720 --> 00:10:35.670 There are also other country standards 238 00:10:35.670 --> 00:10:38.370 and an international standard as well. 239 00:10:38.370 --> 00:10:40.770 Alerting the user to the standard under which the metadata 240 00:10:40.770 --> 00:10:44.670 was developed makes it easier to perform search operations 241 00:10:44.670 --> 00:10:45.780 or use an interpreter 242 00:10:45.780 --> 00:10:47.943 to translate from one standard to another. 243 00:10:49.290 --> 00:10:52.380 Let's look at a couple of examples of metadata. 244 00:10:52.380 --> 00:10:55.350 We'll look at metadata for data that's being distributed 245 00:10:55.350 --> 00:10:58.440 by the Vermont Center for Geographic Information. 246 00:10:58.440 --> 00:11:02.493 Note that VCGI is both a data producer and a data provider. 247 00:11:03.420 --> 00:11:05.670 For those of you that completed the introduction course, 248 00:11:05.670 --> 00:11:08.396 we visited the Vermont Open Geo data portal to access data 249 00:11:08.396 --> 00:11:12.060 and documentation in previous lab assignments. 250 00:11:12.060 --> 00:11:14.670 Hopefully, this interface looks familiar to you. 251 00:11:14.670 --> 00:11:17.520 If you have not yet visited VCGI's website, 252 00:11:17.520 --> 00:11:18.570 you'll get your first chance 253 00:11:18.570 --> 00:11:20.320 in the upcoming lab one assignment. 254 00:11:21.510 --> 00:11:23.280 Let's first look in more detail 255 00:11:23.280 --> 00:11:26.013 at the census dataset that we saw snippets of earlier. 256 00:11:26.880 --> 00:11:28.350 Notice there are two different links 257 00:11:28.350 --> 00:11:30.093 to metadata on this page. 258 00:11:31.770 --> 00:11:34.560 Let's start with the one on the right hand side. 259 00:11:34.560 --> 00:11:37.159 Clicking that link takes you to a new page featuring an 260 00:11:37.159 --> 00:11:42.159 ISO 19139 metadata standard. 261 00:11:42.450 --> 00:11:45.990 This version provides an excellent overview of the dataset 262 00:11:45.990 --> 00:11:48.843 as well as keywords to enable data search and retrieval. 263 00:11:50.280 --> 00:11:52.230 However, if you're interested in greater level 264 00:11:52.230 --> 00:11:55.440 of detail about specific aspects of the data, 265 00:11:55.440 --> 00:11:58.020 like geo processing steps that have been performed 266 00:11:58.020 --> 00:12:01.710 or how to interpret attributes or their unit of measure, 267 00:12:01.710 --> 00:12:03.690 you should click the link in the top left corner 268 00:12:03.690 --> 00:12:05.640 of the overview tab. 269 00:12:05.640 --> 00:12:07.860 This links to a metadata document that complies 270 00:12:07.860 --> 00:12:09.537 with the FGDC standard 271 00:12:09.537 --> 00:12:12.903 and is organized by the six categories I just outlined. 272 00:12:14.040 --> 00:12:16.530 If you look at the data quality section, 273 00:12:16.530 --> 00:12:18.420 you can find information about what, 274 00:12:18.420 --> 00:12:20.721 if any accuracy assessment has been performed 275 00:12:20.721 --> 00:12:23.550 to ensure logical consistency 276 00:12:23.550 --> 00:12:26.340 and geographic accuracy in the dataset. 277 00:12:26.340 --> 00:12:29.130 The top image describes the US Census Bureau's efforts 278 00:12:29.130 --> 00:12:32.223 to ensure an accurate product for the 2010 census. 279 00:12:33.480 --> 00:12:35.070 This is also where you look 280 00:12:35.070 --> 00:12:36.730 for details regarding the lineage 281 00:12:36.730 --> 00:12:40.140 and any data processing that has been undertaken. 282 00:12:40.140 --> 00:12:42.030 Step-by-step process descriptions 283 00:12:42.030 --> 00:12:43.800 for each geo processing operation 284 00:12:43.800 --> 00:12:46.710 that was performed on the dataset gives you a complete 285 00:12:46.710 --> 00:12:50.070 history of the evolution of that dataset. 286 00:12:50.070 --> 00:12:53.700 In this example, we see the details of two process steps. 287 00:12:53.700 --> 00:12:55.500 The first is extracting the Vermont data 288 00:12:55.500 --> 00:12:57.270 from the national dataset, 289 00:12:57.270 --> 00:12:59.370 and the second describes joining the attributes 290 00:12:59.370 --> 00:13:03.720 from the summary file one, the 100% count of the population 291 00:13:03.720 --> 00:13:07.860 to the various geographies produced in the step above. 292 00:13:07.860 --> 00:13:11.010 My opinion on this is to be as detailed as possible 293 00:13:11.010 --> 00:13:14.910 and only list a single processing step per entry. 294 00:13:14.910 --> 00:13:17.460 I'm not sure I could recreate this dataset based 295 00:13:17.460 --> 00:13:18.610 on what I've read here. 296 00:13:20.610 --> 00:13:21.540 If you look at the entity 297 00:13:21.540 --> 00:13:25.050 and attribute information section, you can obtain a lot 298 00:13:25.050 --> 00:13:27.573 of detail about the attributes themselves. 299 00:13:28.440 --> 00:13:30.440 Let's first look at the state attribute. 300 00:13:31.770 --> 00:13:33.180 The definition states 301 00:13:33.180 --> 00:13:34.620 that this attribute represents 302 00:13:34.620 --> 00:13:37.740 the 2010 census state federal information 303 00:13:37.740 --> 00:13:42.100 processing standards or FIPS code number indicating state 304 00:13:43.440 --> 00:13:46.860 that value for Vermont is 50. 305 00:13:46.860 --> 00:13:48.870 This means that the only value you'll find 306 00:13:48.870 --> 00:13:52.530 for the state attribute in this dataset is 50. 307 00:13:52.530 --> 00:13:54.600 If you were working with the national dataset, 308 00:13:54.600 --> 00:13:56.790 you would expect to find the FIPS numbers 309 00:13:56.790 --> 00:13:59.553 for each state in the dataset. 310 00:14:01.170 --> 00:14:04.470 Looking towards the bottom here, we can see an attribute 311 00:14:04.470 --> 00:14:06.753 with labeled P0030002. 312 00:14:09.000 --> 00:14:12.630 This attribute definition states that this represents 313 00:14:12.630 --> 00:14:14.490 that portion of the total population 314 00:14:14.490 --> 00:14:17.490 that identifies as white alone. 315 00:14:17.490 --> 00:14:20.310 If we look at the attribute table for the dataset, 316 00:14:20.310 --> 00:14:25.310 we see code P0030002 and know that in 2010, according 317 00:14:25.789 --> 00:14:30.789 to the US census, there were 596,292 people in the state 318 00:14:31.167 --> 00:14:34.833 of Vermont that identified as white alone. 319 00:14:36.270 --> 00:14:37.983 Okay, one last example. 320 00:14:39.150 --> 00:14:41.820 This time, let's take a look at data representing electric 321 00:14:41.820 --> 00:14:44.850 vehicle charging stations throughout Vermont. 322 00:14:44.850 --> 00:14:47.310 There's the expected metadata link on the right side 323 00:14:47.310 --> 00:14:49.230 of the page, but that won't provide us 324 00:14:49.230 --> 00:14:50.160 with the answers we need 325 00:14:50.160 --> 00:14:52.110 to interpret the attribute information. 326 00:14:53.280 --> 00:14:54.990 Instead, click the external link 327 00:14:54.990 --> 00:14:57.750 to the National Renewable Energy Lab provided 328 00:14:57.750 --> 00:14:59.673 at the end of the dataset description. 329 00:15:01.110 --> 00:15:03.870 That link brings you to a page that looks interesting, 330 00:15:03.870 --> 00:15:06.450 but maybe not as you expected. 331 00:15:06.450 --> 00:15:08.910 You're right, this is not the metadata standard we've seen 332 00:15:08.910 --> 00:15:10.950 in previous examples. 333 00:15:10.950 --> 00:15:13.380 Instead, this is a data dictionary that specifies how 334 00:15:13.380 --> 00:15:16.830 to request and receive data from their server, what's known 335 00:15:16.830 --> 00:15:19.860 as a get request, something we won't worry about for now, 336 00:15:19.860 --> 00:15:22.980 since VCGI takes care of that for us. 337 00:15:22.980 --> 00:15:25.803 It also specifies what data the user receives. 338 00:15:28.020 --> 00:15:30.288 Let's look at the fuel station records fields since 339 00:15:30.288 --> 00:15:32.340 that's the attribution attached to each 340 00:15:32.340 --> 00:15:34.053 of the charging station sites. 341 00:15:35.460 --> 00:15:37.410 This looks a bit more familiar. 342 00:15:37.410 --> 00:15:38.790 There's a data type of string 343 00:15:38.790 --> 00:15:40.740 for the fuel type code attribute, 344 00:15:40.740 --> 00:15:43.432 and the domain of values has been specified with a two 345 00:15:43.432 --> 00:15:47.460 to four digit code and an accompanying definition. 346 00:15:47.460 --> 00:15:52.113 For example, BD represents biodiesel B20 and above, 347 00:15:53.340 --> 00:15:55.650 so that's pretty much it for metadata. 348 00:15:55.650 --> 00:15:59.580 It's not real fun to write and can be cumbersome to read. 349 00:15:59.580 --> 00:16:02.310 That said, I hope you understand the critical role it plays 350 00:16:02.310 --> 00:16:04.800 in both data development and use. 351 00:16:04.800 --> 00:16:06.810 There's lots of interesting data available, 352 00:16:06.810 --> 00:16:09.300 and while you could wait for it to trend on Twitter 353 00:16:09.300 --> 00:16:11.010 or be featured in an interpretive dance 354 00:16:11.010 --> 00:16:14.520 on TikTok, using search tools to discover data 355 00:16:14.520 --> 00:16:16.770 and its associated metadata to understand it 356 00:16:16.770 --> 00:16:20.043 on your own is probably a better path to geoprocessing fame.