1 00:00:10,490 --> 00:00:13,490 Hello, everyone, and welcome to Jurisprudent. 2 00:00:13,490 --> 00:00:21,680 As we do, you will learn what is the danger is if some the patient data that their data faster data 3 00:00:21,770 --> 00:00:29,570 also you will learn that a presentation of vector and master data and lastly, you will learn conservation 4 00:00:29,570 --> 00:00:30,000 data. 5 00:00:30,860 --> 00:00:38,270 So at first, let's understand what is data in James Bay that is one of the most important component 6 00:00:38,270 --> 00:00:40,460 in things that we can say. 7 00:00:40,460 --> 00:00:45,850 80 percent of the task is going to be completed if we collect the data that properties. 8 00:00:46,220 --> 00:00:54,080 So you need to understand which type of data is used in gyres and which type of data is needed to be 9 00:00:54,080 --> 00:00:54,770 collected. 10 00:00:55,340 --> 00:01:01,930 Data is basically a collection of facts and figures about an object in the phenomena. 11 00:01:02,660 --> 00:01:06,880 Let's say, for example, collection of latitude and longitude of their house. 12 00:01:07,550 --> 00:01:10,940 It is often most expensive component in dates. 13 00:01:11,810 --> 00:01:19,580 So here is the job of types of data that is mainly divided into two parts spatial data and non spatial 14 00:01:19,580 --> 00:01:20,030 data. 15 00:01:20,480 --> 00:01:26,010 Patient data is what they're divided into, two parts that is vector and raster data. 16 00:01:26,990 --> 00:01:30,500 Now let us understand what is patient data? 17 00:01:30,920 --> 00:01:41,150 Vision data is usually stored as coordinates NWT and is the data that can be mapped data that defines 18 00:01:41,150 --> 00:01:43,500 the location of geographic feature. 19 00:01:43,910 --> 00:01:50,100 So, for example, a geographic feature may be any feature such as mountain. 20 00:01:50,870 --> 00:01:58,160 Now, if it defined the latitude and longitude of the mountain, it is known as spatial data. 21 00:01:58,910 --> 00:02:04,010 Spatial data is the physical representation of the picture. 22 00:02:05,070 --> 00:02:15,510 It represents the location, size and shape of the object in the earth that is building montane administration 23 00:02:15,510 --> 00:02:22,720 and boundaries, roads, labor, etc., as you know, that every element is special in nature. 24 00:02:23,190 --> 00:02:27,960 So we didn't describe any element of the word in two ways. 25 00:02:28,560 --> 00:02:33,300 First is locational information in this bill flying the car. 26 00:02:33,690 --> 00:02:38,180 That is the latitude and longitude and address of the tree. 27 00:02:38,730 --> 00:02:42,600 Basically will find that the tree is located. 28 00:02:43,170 --> 00:02:50,880 And this is a good way to describe any element of our word is the attribute, the information that this 29 00:02:50,880 --> 00:02:58,620 is species height, each of the three, the attribute in the information is in the tabular form. 30 00:02:58,620 --> 00:03:06,060 And there are two primary ways that this patient data is more than both disaster. 31 00:03:06,060 --> 00:03:12,390 And the second is director of Vector System usually stores data as coordinates. 32 00:03:12,540 --> 00:03:21,210 For example, each unit for area is surrounded by a set of straight line segment called Vector of that 33 00:03:21,230 --> 00:03:30,750 that this guy is defined exactly that representation of its geographic data, the trymaine geometric 34 00:03:30,750 --> 00:03:39,940 shapes used in that data model that it did in one feature or one line and Bollywood. 35 00:03:40,800 --> 00:03:48,810 So let's understand the first type of feature that is formed by the zero dimensional object that contains 36 00:03:48,840 --> 00:03:51,110 only a single x y coordinate. 37 00:03:51,120 --> 00:03:59,610 There's mindset that they can use to model single in other features, such as small building, then 38 00:03:59,730 --> 00:04:08,330 power pulls Bamford's trees, etc., but have only the properties of locations. 39 00:04:09,090 --> 00:04:11,340 The second feature is lines. 40 00:04:11,580 --> 00:04:18,180 Lines are one dimensional feature comprises of multiple connected points. 41 00:04:18,750 --> 00:04:28,170 Lines are used to represent linear features such as road, train street, central line, etc. Lines 42 00:04:28,170 --> 00:04:30,000 have the property of length. 43 00:04:30,600 --> 00:04:33,870 A line is synonymous with an arc. 44 00:04:34,870 --> 00:04:43,210 The third feature is politicians are doing an optional feature created by multiple lines, which creates 45 00:04:43,270 --> 00:04:52,390 a closed polygon by linking have used to represent features such as Lake George, buildings, vegetation, 46 00:04:52,390 --> 00:04:54,160 communities, etc.. 47 00:04:54,910 --> 00:05:02,280 But we didn't have the properties of Adaiah and perimeter polygons, also known as area. 48 00:05:03,190 --> 00:05:11,740 The second type of data is raster data masterbate that is made up of metrics of big [REMOVED]'s ourselves. 49 00:05:11,740 --> 00:05:22,260 And each pixels have an associated value of value given to itself, which informs the users which entities 50 00:05:22,300 --> 00:05:23,920 is present in it. 51 00:05:24,040 --> 00:05:31,750 So one example value represent elevation above the sea level rise, etc.. 52 00:05:32,500 --> 00:05:40,330 Example of raster dataset is they get an aerial photograph, satellite image, digital pictures. 53 00:05:40,510 --> 00:05:49,750 That is the picture taken on camera or cell phone on nostro images are made up of thousands or millions 54 00:05:49,780 --> 00:05:53,710 of individual picture element are pixels. 55 00:05:54,250 --> 00:06:03,190 Then we zoom into the digital image, Bengazi, each of the individual pixels that make up the image. 56 00:06:04,230 --> 00:06:11,760 In days of aid, in our satellite imagery, each of these big self-contained number that represents 57 00:06:11,760 --> 00:06:21,270 the amount of light at a particular wavelength that was recorded by the camera and is now being displayed 58 00:06:21,270 --> 00:06:26,370 by the computer monitor for this type of feature is blind. 59 00:06:27,030 --> 00:06:33,930 A point in that data is represented by a single dot, that industry data. 60 00:06:33,960 --> 00:06:42,060 It is represented by a single person example of blind R trees, Bamford's, etc.. 61 00:06:43,320 --> 00:06:51,690 The second type of feature is like a line in that data is presented by a set of guidelines that make 62 00:06:51,690 --> 00:06:57,960 up a straight line or a blur depending upon the feature it represents. 63 00:06:58,500 --> 00:07:00,660 That is interesting data. 64 00:07:00,660 --> 00:07:06,270 It is presented by a sequence of cells upgrades. 65 00:07:06,720 --> 00:07:16,590 The example of lines are never straight, central line etc code is split up by an investigator. 66 00:07:16,830 --> 00:07:24,800 It's represented by a setup line which creates a closed finger that is investigator. 67 00:07:25,080 --> 00:07:28,830 It is represented by a zone of sense. 68 00:07:29,900 --> 00:07:40,370 The example of Bonington may be lakes like City Island, etc., not the last type of data that is not 69 00:07:40,380 --> 00:07:47,810 spatial data, not station data is also known as attribute organic statistics data. 70 00:07:49,030 --> 00:07:57,570 Not sufficient data are stored in giant, stable, such cables are known as non-special or attribute 71 00:07:57,570 --> 00:07:58,220 tables. 72 00:07:59,450 --> 00:08:07,790 non-Special neighborhoods are represented by drones and bottoms in which each road shows a special feature 73 00:08:07,790 --> 00:08:11,150 and each column represent characteristics. 74 00:08:12,400 --> 00:08:20,440 The intersection of rows and columns gets the value of specific characteristics for a particular feature. 75 00:08:20,650 --> 00:08:22,350 As you can see in the Baban. 76 00:08:23,640 --> 00:08:29,580 Otherwise known as drugs and alcohol is known as these are items. 77 00:08:30,910 --> 00:08:33,760 So that's all I hope you like the video's debut on.