Saturday, April 30, 2022

Spatial Mean: Step by Step Calculation

Saturday, April 30, 2022 0 Comments

Introduction:

There is a variation of distributional pattern of different items, such as population settlement, roads etc over space and time. A change in spatial distribution or its average location may be observed and that can be quantitatively expressed by determining the mean centre of the cultural elements for the whole region.

 

Methodology:

The main gravitational centre of population is determined by weighted mean. Weight of population of each administrative area is giving to the X and Y coordinates measuring either inch or centimeter.

Here, the geocentre of each administrative units are selected arbitrarily.

Mean of X () and Y () will give the gravitational centre of population for a particular region for a particular time period.

The formula used to calculate special mean is: 

Calculation of Spatial Mean for Nadia District, West Bengal:






How to Download Census data from Census Digital Library

Saturday, April 30, 2022 0 Comments


Census of India Digital Library (PCA Combined Rural and Urban)



Click on the above link. 

It will take you to the census of India digital library database. 

Click on Table in top row. 

Select Census yearYou will see following list of tables.

 

Click on the primary census abstract.  

Then click on the primary census abstract Total. 

Choose your State. 

Then click on your desired district's Primary Census Abstract Total .xls file

You are all set. Excel data will be downloaded.


N. B. Here you can download many other data using other links. 

Friday, April 29, 2022

What is Nearest Neighbour Analysis? Principle, Assumptions and techniques

Friday, April 29, 2022 0 Comments

Introduction: 

Nearest Neighbour Analysis (NNA) is a technique of determining the spatial distribution of point based objects on map. It is a well known Applied Geographic Technique used for measuring "spatial distribution and interaction of geographic phenomena on the geographic space." 

Most popular use of this technique is identifying the rural settlement types

Proponents:

That technique is known as Nearest Neighbour Index (NNI) which was originally put forwarded by two biologists Clark and Evans and in geography it was introduced by King and Dacy 

Assumptions:

1. Distribution of points on the geographical space is random. 

2. The probability distribution of the distances between the points and their nearest neighbours as normal. 

Equation and Significance test:

Steps for Calculation: 

Step-1: Identification of the settlement patches on the given map area and trace the settlement patches. 

Step-2: Numbering of the settlement patches.

Step-3: Measuring the distances from the settlement to their nearest neighbours. 

Step-4: Calculation of mean observed distance using the formula.  

Step-5: Calculation of the mean expected distance from the given formula. 

Step-6: Calculation of the Nearest Neighbour Index (NNI) using the formula.  

Step-7: Interpretation of the value with the help of the scale given by the proponents. 

Step-8: Test of significance of the index value.

Disadvantages: 

The method is simple to understand but it has some major drawbacks. 

1. The boundary demarcation is subjective. Any changes in the boundary may influence the index value. 

2. It does not distinguish between single and multi clustered pattern. 

3. It actually averages out the sub patterns observed within the area. 

Points to be noted: 

No point outside the boundary of the given area is generally included in the study. 

But if the nearest neighbour of any point lies just outside the boundary the distance should be counted.

See Video 



Rank Size Rule by G. K. Zipf

Friday, April 29, 2022 0 Comments

 
The concept “Rank-Size Rule” was first propounded during the first quarter of the present century. Though as Rosing (1966) has said, Zipf was not the first person to point towards the regularity of city sizes. The empirical existence of a regular relationship between the size of urban centres and their ranks was first presented by Auerback (1913) in a study of German cities. He opined that the population of the n'th city was 1/n'th the size of the largest city.
The rank-size rule was first scientifically put forward by G. K. Zipf (1941) as a theoretical model to express the relationship between observed and empirical regularity in the size of settlement hierarchy either urban or rural. This observed phenomenon is often referred to as Zipf’s Law.
Zipf (1949) observed that the logarithm of population size when plotted against the logarithm of the rank of the city produced points close to a straight line, with negative slope (rank inversely proportional to size). 
The idea that settlement size and rank have a systematic relationship was popularized by Zipf (1949),
expressed it by simple formula as:

Pr =  P1/rk , r = 1,2,...

This suggest that if the population of the largest city (P1) is divided by any city in the same region, the result will approximately be the population of the city (Pr) whose rank number is used as a divisor.
If the population of the largest city is known, the population of all other cities can be derived from the rank of their size. 
Thus, if the largest city has 100,000,00 population the tenth city will have one-tenth or 100,000 and the hundredth city will have one-hundredth as many or 10,000. 
 

Thursday, April 28, 2022

Human Development Report Data Centre

Thursday, April 28, 2022 0 Comments

Here is the link for downloading data on Human Development Reports of Different Countries. Human Development Data Center | Human Development Reports

Human Development Data Center

Select data by dimension, indicator, year and/or country to see a dynamic interactive visualization of the data

(represented as line for trends, or bar for single years)

The human development data are sourced from international data agencies with the mandate, resources, and expertise to collect national data on specific indicators unless otherwise noted.
The data are presented in two sections of the website:

‘Data’ page – provides downloads of over 150 global indicators and composite indices for over 190 countries in CSV and PDF. This page also provides documentation and methodologies, FAQ’s, API access, and other key information.

‘Country profiles’ page – presents HDI values and ranks, data visualizations/trends for the HDI and other composite indices, country explanatory notes, and over 150 other indicators.
Datasets include definitions of indicators and sources for original data components at the end of each table, with full source details in the Statistical references.

Download data by indicator: