Level of Mearsurement in Geographical analysis.pptx

Level of Mearsurement in Geographical analysis

1. LEVEL OF MEASUREMENTS

2. Spatial data can further be divided into two types:   Vector data  Raster data Vector data : Vector data represents any geographical feature through point, line or polygon or combination of these. Raster data : Raster data is made up of pixels. It is an array of grid cells with columns and rows. Each and every geographical feature is represented only through pixels in raster data. There is nothing like point, line or polygon. If it is a point, in raster data it will be a single pixel, a line will be represented as linear arrangement of pixels and an area or polygon will be represented by contiguous neighboring pixels with similar values.

3. The Point type for the geography data type represents a single location where  Lat  represents latitude and  Long  represents longitude. The values for latitude and longitude are measured in degrees. A point in GIS is represented by one pair of coordinates (x & y). It is considered as dimension-less object. Most of the times a point represent location of a feature (like cities, wells, villages etc.). NATURE OF DATA: POINT DATA

4. Point symbols are individual designs such as dots, triangles and so on, used to denote a position, locations of a feature, the intensity at a place or representative location for spatial summary. Example: Radio tower, a spot height etc. NATURE OF DATA: POINT DATA

5. NATURE OF DATA: LINE DATA A line or arc contains at least two pairs of coordinates (say- x1, y1 & x2, y2). In other words a line should connect minimum two points. Start and end points of a line are referred as nodes while points on curves are referred as vertices .

6. NATURE OF DATA: LINE DATA Points at intersections are also called as nodes . Roads, railway tracks, streams etc. are generally represented by line. A  Line String  is a one-dimensional object representing a sequence of points and the line segments connecting them.

7. NATURE OF DATA: LINE DATA Line symbols are individual linear signs used to represent a variety of geographical phenomena. Lines depicting rivers, roads, and political boundaries are common example. But use of line symbol does not always mean that the class of features being represented is linear. For instance: contour lines.

8. NATURE OF DATA: AREA DATA In simple terms, polygon is a closed line with area. It takes minimum three pairs of coordinates to represent an area or polygon. Extent of cities, forests, land use etc. is represented by polygon. A  Polygon  is a two-dimensional surface stored as a sequence of points defining an exterior bounding ring and zero or more interior rings.

9. Spatial data can be Discrete and Continuous: Discrete data : Some segment distributions are discrete. They are composed of individual items at particular locations. Intervening areas are empty of that feature or have a value of zero for the attribute value . Such would be case for example with individual houses or industrial plants etc. Continuous data : The data that is continuous (without breaks) in a selected range is known as Continuous Data. In continuous no location is empty. For example: Temperature values on earth surface or categories of land cover exist everywhere etc.

10. LEVELS OF MEASURMENT The "levels of measurement", or scales of measure are expressions that typically refer to the theory of scale types developed by the psychologist Stanley Smith Stevens. Stevens proposed his theory in a 1946 Science article titled "On the theory of scales of measurement".[1] In that article, Stevens claimed that all measurement in science was conducted using four different types of scales that he called "nominal", "ordinal", "interval" and "ratio".

11. Introduction The classification of data is based on the degree of precision with which the data has been collected. There are four categories of data on the basis of measurement: 1. Nominal (less comprehensive) 2. Ordinal (more comprehensive) 3. Interval (still more comprehensive) 4. Ratio (most comprehensive)

12. NOMINAL SCALE We use nominal scales when we distinguish among features only on the basis of qualitative considerations. There is no implication of a quantitative consideration. We are only saying that feature A is of different class than feature B.

13. NOMINAL SCALE For example: We might differentiate a bench mark from a spring or land from water or a maritime air mass from continental air mass. Nominal scale is simply a system of assigning number symbols to events in order to label them.

14. NOMINAL SCALE The usual example of this is the assignment of numbers of basketball players in order to identify them. Such numbers cannot be considered to be associated with an ordered scale, for their order is of no consequences; the numbers are just convenient labels for the particular class of events and as such have no quantitative value.

15. NOMINAL SCALE Nominal scale is the least powerful level of measurement. It indicates no order or distance relationship and has no arithmetic origin.

16. ORDINAL SCALE Ordinal scale involves differentiation by class, but they also differentiate within a class of features on the basis of rank according to some quantitative measure. Only rank is involved; that is, attribute values are ordered from lowest to highest without any definition of numerical values. For example: we can differentiate major ports from minor ports or we can distinguish among small, medium and large cities.

17. ORDINAL SCALE For example: we can differentiate major ports from minor ports or we can distinguish among small, medium and large cities. Ordinal refers to order in measurement. The lowest level of the ordered scale that is commonly used is the ordinal scale.

18. ORDINAL SCALE The ordinal scale places events in order, but there is no attempt to make the interval of the scale equal in terms of some rule. A student’s rank in his graduation class involves the use of an ordinal scale

19. ORDINAL SCALE For instance : If Ram’s position in his class is 10th and Mohan’s position in 40th , It cannot be said that Ram’s position is four times as good as that of Mohan. Ordinal scales only permit the ranking of items from highest to lowest.

20. ORDINAL SCALE Thus the use of an ordinal scale implies a statement of greater than or less than without our being able to state how much greater or less.

21. INTERVAL SCALE In case of interval scale, the intervals are adjusted in terms of some rule that has been established as a basis for making the units equal. The units are equal only in so far as one accepts the assumption on which the rule is based.

22. INTERVAL SCALE Interval scales can have an arbitrary zero, but it is not possible to determine for them what may be called an absolute zero or the unique origin. The primary limitation of the interval scale is the lack of a true zero; it does not have the capacity to measure the complete absence of trait or characteristics.

23. INTERVAL SCALE The Fahrenheit scale is an example of an interval scale and shows similarities in what one can and cannot do with it.

24. INTERVAL SCALE One can say that an increase in temperature from 30 degrees to 40 degrees F involves the same increase in temperature as an increase from 60 to 70 degrees F , but one cannot say that the temperature of 60 degrees F is twice as warm as the temperature of 30 degree F because both numbers are dependent on the fact that the zero on scale is set arbitrarily at the temperature of the freezing point of water.

25. RATIO SCALE Ratio scales have an absolute or true zero of measurement . The term absolute zero is not as precise as it was once believed to be. We can conceive of an absolute zero of length and similarly we can conceive of an absolute zero of time. For example: The zero point on centimeter scale indicates the complete absence of length or height. Depth of snowfall is also measured on ratio scale. Generally all statistical techniques are usable with ratio scale value. But an absolute zero of temperature is theoretically unobtainable and it remains a concept existing only in the scientist’s mind.

26. RATIO SCALE With ratio scale involved one can make statements like “ Jyoti’s ” typing performance was twice as good as that of “ Ritu ”. The ratio involved does have significance and facilitates a kind of comparison which is not possible in case of an interval scale. Multiplication and division can be used with this scale but not with other scales mentioned above.

27. Thank You