• No results found

UNIT 1: Use of Data in Geography

N/A
N/A
Protected

Academic year: 2022

Share "UNIT 1: Use of Data in Geography"

Copied!
28
0
0

Loading.... (view fulltext now)

Full text

(1)

B .A. / B.Sc. (Hons.) III Semester (CBCS) GGB3P1: STATISTICAL METHODS IN

GEOGRAPHY

UNIT 1: Use of Data in Geography

(2)

Syllabus

1. Use of Data in Geography: Geographical Data Matrix, Significance of Statistical Methods in Geography;

Sources of Data, Scales of Measurement (Nominal, Ordinal, Interval, Ratio).

2. Tabulation and Descriptive Statistics: Frequencies - Deciles, Quartiles, Percentile, Cross Tabulation.

3. Measurement of Central Tendencies: Mean, Median and Mode, Centro-graphic Techniques.

4. Measures of Dispersion: Standard Deviation, Variance and Coefficient of Variation.

5. Sampling: Purposive, Random, Systematic and Stratified.

(3)

Introduction

Statistics is a branch of science that deals with the

collection,

organisation,

analysis of data and

drawing of inferences from the samples to the whole population.

This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test.

(4)

Geographical Data

• Geographical or spatial data are defined as undigested, unorganized, and unevaluated material that can be associated with a location.

• Geographical data include facts, results of observations, original remote sensing images, basic census figures and statistics all of which are gathered and communicated to the user.

• Data are of little value in or of themselves. To be useful they must be transformed into information.

(5)

•When data are organized, presented, analyzed,

interpreted and considered useful for a particular decision problem, they become information.

•Accordingly, geographical information is defined

as georeferenced data that have been processed into a form that is meaningful and of real or perceived value to decision makers.

(6)

Geographical Data Matrix (GDM)

Geographic data posses two distinct components- Location

and attributes at locations, and the geographical data matrix is perhaps the most logical way to represent the real world. It is a major milestone in the development of data organization methods in scientific geographical studies.

In the GDM, a column represents the variation of attributes of the natural or socio-economic characteristic across some geographical spaces.

(7)

• Thus, basically it shows spatial pattern of the variables that can be identified and mapped.

A row, on the other hand, denotes a specific location in geographic space. Therefore, each cell formed by the rows and columns of GDM contains a specific item of geographic fact that can be found at a particular location. In a

GDM, the column can be compared to study the nature of spatial variation of the

geographic characteristics.

Geographical Data Matrix (GDM)

(8)
(9)

Source of data

Data Collection Techniques

• There are two sources of data collection techniques. Primary and Secondary data

collection techniques, Primary data collection uses surveys, experiments or direct

observations. Secondary data collection may be conducted by collecting information from a

diverse source of documents or electronically stored information, census and market studies are examples of a common sources of secondary data. This is also referred to as "data mining."

(10)
(11)

Primary Data

• Primary data means original data that has been collected specially for the purpose in mind. It means someone collected the data from the original source first hand. Data collected this way is called primary data.

Primary data has not been published yet and is more reliable, authentic and objective.

Primary data has not been changed or altered by human beings; therefore its validity is greater than secondary data.

(12)

Methods of Primary Data Collection

Survey:

Questionnaire:

Interview:

Observations:

(13)

SECONDARY DATA

Secondary data is the data that has been already collected by and readily available from other sources.

When we use Statistical Method with Primary Data from another purpose for our purpose we refer to it as Secondary Data. It means that one purpose's Primary Data is another purpose's Secondary Data. So that secondary data is data that is being reused. Such data are more quickly obtainable than the primary data.

These secondary data may be obtained from many sources, including literature, industry surveys, compilations from computerized databases and information systems, and computerized or mathematical models of environmental processes.

(14)

Methods of Secondary Data Collection

Published Printed Sources:

Books:

Journals/periodicals:

Magazines/Newspapers:

Published Electronic Sources:

E-journals:

General Websites:

Weblogs:

(15)
(16)

Data: qualitative and quantitative

Qualitative data refers to information about qualities, or information that cannot be measured. It’s usually descriptive and textual. Examples include someone’s eye colour or the type of car they drive. In surveys, it’s often used to categorize ‘yes’ or

‘no’ answers.

Quantitative data is numerical. It’s used to define information that can be counted. Some examples of quantitative data include distance, speed, height, length and weight. It’s easy to remember the difference between qualitative and quantitative data, as one refers to qualities, and the other refers to quantities.

A bookshelf, for example, may have 100 books on its shelves and be 100 centimetres tall. These are quantitative data points.

The colour of the bookshelf – red – is a qualitative data point.

(17)

What is quantitative (numerical) data?

Quantitative, or numerical, data can be broken down into two types: discrete and continuous.

Discrete data

Discrete data is a whole number that can’t be divided or broken into individual parts, fractions or decimals. Examples of discrete data include the number of pets someone has – one can have two dogs but not two-and-a-half dogs. The number of wins someone’s favourite team gets is also a form of discrete data because a team can’t have a half win – it’s either a win, a loss, or a draw.

Continuous data

Continuous data describes values that can be broken down into different parts, units, fractions and decimals. Continuous data points, such as height and weight, can be measured. Time can also be broken down – by half a second or half an hour. Temperature is another example of continuous data.

Discrete versus continuous

There’s an easy way to remember the difference between the two types of quantitative data: data is considered discrete if it can be counted and is continuous if it can be measured. Someone can count students, tickets purchased and books, while one measures height, distance and temperature.

(18)
(19)
(20)
(21)

Nominal scale of measurement

The nominal scale of measurement defines the identity property of data. This scale has certain characteristics, but doesn’t have any form of numerical meaning. The data can be placed into categories but can’t be multiplied, divided, added or subtracted from one another. It’s also not possible to measure the difference between data points.

Examples of nominal data include eye colour and country of birth.

(22)

Nominal Data

(23)

Ordinal scale of measurement

• The ordinal scale defines data that is placed in a specific order. While each value is ranked, there’s no information that specifies what differentiates the categories from each other. These values can’t be added to or subtracted from.

• An example of this kind of data would include satisfaction data points in a survey, where ‘one = happy, two = neutral, and three = unhappy.’

Where someone finished in a race also describes ordinal data.

(24)

Ordinal data

(25)

Interval scale of measurement

The interval scale contains properties of nominal and ordered data, but the difference between data points can be quantified. This type of data shows both the order of the variables and the exact differences between the variables.

They can be added to or subtracted from each other, but not multiplied or divided. This scale is also characterised by the fact that the number zero is an existing variable.

In the ordinal scale, zero means that the data does not exist. In the interval scale, zero has meaning – for example, if you measure degrees, zero has a temperature.

(26)

Interval Data

(27)

Ratio scale of measurement

Ratio scales of measurement include properties from all four scales of measurement. The data is nominal and defined by an identity, can be classified in order, contains intervals and can be broken down into exact value. Weight, height and distance are all examples of ratio variables. Data in the ratio scale can be added, subtracted, divided and multiplied.

Ratio scales also differ from interval scales in that the scale has a ‘true zero’. The number zero means that the data has no value point. An example of this is height or weight, as someone cannot be zero centimetres tall or weigh zero kilos – or be negative centimetres or negative kilos. Examples of the use of this scale are calculating shares or sales. Of all types of data on the scales of measurement, data scientists can do the most with ratio data points.

(28)

References

Related documents

The present account is based on data from two sources: (1) the statistics of marine fish landings maintained by the Fishery Survey Division of the Central Marine Fisheries

Table 5.1 demonstrates the descriptive statistics and percentile values (5 th , 50 th and 95 th ) of different measures of adiposity and subcutaneous fat content of

Table 6.5: Summary of descriptive statistics related to uncertainty in

As seen from Table 4.8, all the respondents were aware of and reasonably satisfied with the Bank's leave policy. Tables 4.911-4] (on the following page) pertain to

IV.5 Data collection and tabulation procedure 70 IV.5.1 Data collected through lesson observation 70 IV.5.2 Data collected through opinionnaire 71 IV.5.3 Data collected

1) Classification of a data set according to the nature of data. 3) The methods of construction of discrete and continuous frequency distributions. 4) Fundamentals of

Ratio scale is the highest level of measurement because nominal scale gives only names to the different categories, ordinal scale provides orders between categories other than

Note 2: If some learner is interested to know more about the topics discussed in Secs. 16.4 and 16.5 he/she may refer chapters 6 and 7 of the book written at serial number 5 in