AAG 2018 CFP: Urban Cultural Studies

urbanculturalstudies

The Journal of Urban Cultural Studies is organizing an interactive short paper session at the Association of American Geographers Conference to be held April 10-14, 2018 in New Orleans. Each of the 10-14 panelists in the Urban Cultural Studies session will present a 5-minute summary of research or studies in process. A 30- to 45-minute interactive roundtable discussion will follow the presentations.
The CFP is as follows:
In recent years, cities have been increasingly at the forefront of debate in both humanities and social science disciplines, but there has been relatively little real dialogue across these disciplinary boundaries. On the one hand, social science fields that use urban studies methods to look at life in cities rarely explore the cultural aspects of urban life in any depth or delve into close-readings of the representation of cities in individual novels, music albums/songs, graphic novels, films, videogames, online ‘virtual’ spaces, or other…

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Dispersion in Statistical Geography

 We live in a changing world. Changes are taking place in every sphere of life. A man of statistics does not show much interest in those things which are constant. The total area of the earth may not be very important to a research minded person but the area under different crops, area covered by forests, area covered by residential and commercial buildings are figures of great importance because these figures keep on changing form time to time and from place to place. Very large number of experts is engaged in the study of changing phenomenon. Experts working in different countries of the world keep a watch on forces which are responsible for bringing changes in the fields of human interest. The agricultural, industrial and mineral production and their transportation from one part to the other parts of the world are the matters of great interest to the economists, statisticians, and other experts. The changes in human population, the changes in standard living, and changes in literacy rate and the changes in price attract the experts to make detailed studies about them and then correlate these changes with the human life. Thus variability or variation is something connected with human life and study is very important for mankind.

Dispersion:

The word dispersion has a technical meaning in statistics. The average measures the center of the data. It is one aspect observations. Another feature of the observations is as to how the observations are spread about the center. The observation may be close to the center or they may be spread away from the center. If the observation are close to the center (usually the arithmetic mean or median), we say that dispersion, scatter or variation is small. If the observations are spread away from the center, we say dispersion is large.

The study of dispersion is very important in statistical data. If in a certain factory there is consistence in the wages of workers, the workers will be satisfied. But if some workers have high wages and some have low wages, there will be unrest among the low paid workers and they might go on strikes and arrange demonstrations. If in a certain country some people are very poor and some are very high rich, we say there is economic disparity. It means that dispersion is large. The idea of dispersion is important in the study of wages of workers, prices of commodities, standard of living of different people, distribution of wealth, distribution of land among framers and various other fields of life. Some brief definitions of dispersion are:

  1. The degree to which numerical data tend to spread about an average value is called the dispersion or variation of the data.
  2. Dispersion or variation may be defined as a statistics signifying the extent of the scatteredness of items around a measure of central tendency.
  3. Dispersion or variation is the measurement of the scatter of the size of the items of a series about the average.

Properties of a good measure of Dispersion

There are certain pre-requisites for a good measure of dispersion:

  1. It should be simple to understand.
    2. It should be easy to compute.
    3. It should be rigidly defined.
    4. It should be based on each individual item of the distribution.
    5. It should be capable of further algebraic treatment.
    6. It should have sampling stability.
    7. It should not be unduly affected by the extreme items.

Types of Dispersion

The measures of dispersion can be either ‘absolute’ or “relative”. Absolute measures of dispersion are expressed in the same units in which the original data are expressed. For example, if the series is expressed as Marks of the students in a particular subject; the absolute dispersion will provide the value in Marks. The only difficulty is that if two or more series are expressed in different units, the series cannot be compared on the basis of dispersion.
‘Relative’ or ‘Coefficient’ of dispersion is the ratio or the percentage of a measure of absolute dispersion to an appropriate average. The basic advantage of this measure is that two or more series can be compared with each other despite the fact they are expressed in different units. Theoretically, ‘Absolute measure’ of dispersion is better. But from a practical point of view, relative or coefficient of dispersion is considered better as it is used to make comparison between series.

Methods of Dispersion

Methods of studying dispersion are divided into two types :
(i) Mathematical Methods: We can study the ‘degree’ and ‘extent’ of variation by these methods. In this category, commonly used measures of dispersion are:

(a) Range

(b) Quartile Deviation

(c) Average Deviation

(d) Standard deviation and coefficient of variation.

(ii) Graphic Methods: Where we want to study only the extent of variation, whether it is higher or lesser a Lorenz-curve is used.

Why dispersion?

Measures of central tendency, Mean, Median, Mode, etc., indicate the central position of a series. They indicate the general magnitude of the data but fail to reveal all the peculiarities and characteristics of the series. In other words, they fail to reveal the degree of the spread out or the extent of the variability inindividual items of the distribution. This can be explained by certain other measures, known as ‘Measures of Dispersion’ or Variation.
Mathematical Methods

Range
It is the simplest method of studying dispersion. Range is the difference between the smallest value and the largest value of a series. While computing range, we do not take into account frequencies of different groups.

 Quartile Deviations (Q.D.)

The concept of ‘Quartile Deviation does take into account only the values of the ‘Upper quartile (Q3) and the ‘Lower quartile’ (Q1). Quartile Deviation is also called ‘inter-quartile range’. It is a better method when we are interested in knowing the range within which certain proportion of the items fall.

Average Deviation

Average deviation is defined as a value which is obtained by taking the average of the deviations of various items from a measure of central tendency Mean or Median or Mode, ignoring negative signs. Generally, the measure of central tendency from which the deviations arc taken, is specified in the problem. If nothing is mentioned regarding the measure of central tendency specified than deviations are taken from median because the sum of the deviations (after ignoring negative signs) is minimum.

Standard Deviation

The standard deviation, which is shown by greek letter s (read as sigma) is extremely useful in judging the representativeness of the mean. The concept of standard deviation, which was introduced by Karl Pearson has a practical significance because it is free from all defects, which exists in a range, quartile deviation or average deviation.
Standard deviation is calculated as the square root of average of squared deviations taken from actual mean. It is also called root mean square deviation. The square of standard deviation i.e., s2 is called ‘variance’.

Coefficient of Variation

The coefficient of variation is a measure of spread that describes the amount of variability relative to the mean. Because the coefficient of variation is unitless, one can use it instead of the standard deviation to compare the spread of data sets that have different units or different means.

The coefficient of variation (CV) is the ratio of the standard deviation to the mean (average). For example, the expression “The standard deviation is 15% of the mean” is a CV. The CV is particularly useful when you want to compare results from two different surveys or tests that have different measures or values. For example, if you are comparing the results from two tests that have different scoring mechanisms.

Relationship between quartile deviation, average deviation and standard deviation is given as:
Quartile deviation = 2/3 Standard deviation
Average deviation = 4/5 Standard deviation

Link(s) and Source(s):

Maths Zone

School Of Open Learning-Delhi University

 

 

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After Grenfell Tower, more British fire hazards

Dear Kitty. Some blog

This 2016 video is called How Do Fire Sprinklers Know There’s A Fire?

By Felicity Collier in Britain:

Just 2% of socially owned tower blocks have sprinklers

Thursday 14th September 2017

JUST ONE in 50 socially owned tower blocks are fully fitted with sprinklers, an investigation published yesterday has revealed.

And seven in 10 blocks have only one staircase for evacuation, according to the shock results of a BBC Breakfast probe covering half of Britain’s council and housing association-owned towers.

After the disaster at Grenfell Tower, which did not have sprinklers, ministers said they might retrofit sprinklers to blocks depending on the findings of the public inquiry into the fire.

It was made compulsory in England in 2007 for sprinklers to be fitted to all new high rises over 30m tall.

London Fire Brigade commissioner Dany Cotton said: “I support retrofitting. For me, where you can save one life…

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Centrographic Techniques

Centrography is  the establishment of trends and laws of the distribution of phenomena based on the relationships and migrations of their centers of gravity . Measures of center are geographical techniques for locating the position of a point (or area) that represents the average location of the entire population sampled. This population is applicable to a host of variables including species populations, manufacturing, natural resources, epidemics, agriculture, education, etc.

Centrographic measures can be used to describe two features of discrete distributions: (1) central tendency, and (2) dispersion. The former is a point which is found by applying at least one averaging criterion. Since there exist several averaging criteria, there are several such points. The most common are the center of gravity (mean center), median center, and the point of minimum aggregate travel. The latter feature, dispersion, depicts the degree of scatter from a point of central tendency.
In the two dimensional case, there are numerous techniques and figures which demonstrate the scatter of points: the standard radius, the standard deviational ellipse, the standard deviational bicircular quartic and several versions of the sectogram.
Mean center calculations are divided into two methods of use. The first method is called the spatial mean or mean center. According to Wong (2005), the mean center is “a central or average location of a set of points.” The calculation of the mean center involves the summation of the X coordinates, the summation of the Y coordinates, then finally dividing each sum by the number of sample points. This produces an average X,Y coordinate pair (an average point) of all the sample points. For example, the mean center of the U.S. would be found using all 50 state center coordinates in the calculation. The second method is actually the more robust of the two calculations for mean centers, when “the points or locations have different frequencies or occurrences of the phenomenon being studied” (Wong, 2005). When these point locations have different frequencies, a “weight” is then applied by multiplying that weight with each coordinate during the summation of each X and Y dataset. An example of a weight would be the number of residents in a parcel. Parcels with more people would have a higher weight in the mean center calculation of population for parcels in a defined area (like a city). This weighting of variables then affects the resultant mean center point because the points with higher weights (like more people) effect the calculation more “pulling” the center more than lesser weighted points. This effect is known as a “sensitive center”.
There are a number of ways to calculate centers, including the median point calculation, quartilides, decilides and centrilides. The median point method involves finding “the point of intersection of two orthogonal lines each of which divides the population into two equal groups” (Sviatlovsky, 1937). One major downside to the median point method is that its results may vary radically with small changes in populations because it does not have what Sviatlovsky calls a “sensitive center”. This lack of a sensitive center causes the median point to shift erratically with certain changes to the population, whereas, the mean center will not. This occurs because the median center is calculated based on the position of orthogonal lines, not a statistical operation like the mean center calculation. When populations move only slight distances (over an orthogonal), they can cause major shifts in the median point location. The opposite is true when a large population migrates but does not pass over an orthogonal. Quartilides, decilides and centrilides are similar to the median point method in that they involve lines that divide the population into equal portions, however, the number of dividing lines is higher. This produces divisions of populations in quarters, tenths and hundredths. According to Sviatlovsky, this method is advantageous in regional analysis because it allows for “a greater degree of refinement…than ordinarily profitable”.

References
Wong, David, W.S.; Lee, Jay (2005): Statistical Analysis of Geographic Information, John Wiley and Sons.
Sviatlovsky, E. E.; Eells, Walter Crosby (1937): The Centrographical Method and Regional Analysis, Geographical Review, Vol. 27, No. 2.
Poulsen, Thomas M. (1959): Centrography in Russian Geography, Annals of the Association of American Geographers, Vol. 49, No. 3.

 Source(s) and Link(s):

Omaha Demographic Centre Site

Measure of Central Tendencies

Dispersion in Statistical Geography

 

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