Railways and Population Change
in
Industrializing England
An Introduction to Historical GIS
Robert M. Schwartz
Department of History
Mount Holyoke College
June 22, 1999
Draft
Copyright Robert M. Schwartz
Chapter 1
Maps, Concepts, and Numbers
The Map Hunt
It is Tuesday morning. I need to prepare for class. I go to my bookshelf to find information on the economic history of Britain during the industrializing era. I take down the second edition of Peter Mathias’s classic text, The First Industrial Nation. An Economic History of Britain 1700-1914. A glance at the table of contents draws me immediately to Chapter 10 in Part II on “The Railways.” It is short but informative with a useful table showing the miles of railways proposed and opened annually from 1832 to 1850. As I read on, I look for a map of the lines. There is none.
Puzzled, I return to the Table of Contents to find the maps in other chapters. I find the list of tables, the list of figures, but . . .no list of maps. No maps? Hmmm? I look again and run my eyes slowly down the list of figures. Surely, I say to myself, the maps are included here. Not so. Disappointed, I repair to the appendices. They are impressive: richly documented with 40 tables! . . . And yet, there’s not a single map? How can this be?
This surprising discovery prompts some reflections. I do not conclude that the book is second-rate. After all, it is reputed a classic, and the author is an economic historian of the first order. The book attests to his admirable talent of combining quantitative data with economic analysis, historical detail, and lucid exposition. Published in 1983, it also reveals a predilection for statistics at the aggregate level of the nation state. These are statistics that are non-spatial in character, and it seems clearly to have been the author’s intention to focus on them, consciously excluding issues of geographic variation in social and economic development.
Historical geographers, of course, have long attended to questions of this kind. Social historians such as myself are at pains in their research to acquire “a sense of place” through the study of maps and contemporary descriptions. That The First Industrial Nation and a number of other studies of the period proved to be “mapless” came as a surprise nonetheless because my interests and those of other historians and social scientists have changed.
The personal computer has opened new kinds of research opportunities,
ranging from access to on-line library resources to the use of images as
primary historical sources in essays, books, and presentations, be they on
paper or in digital form. More specifically, the appearance on desktop
computers of powerful GIS programs has greatly enhanced our ability to
investigate geographic questions. Previously the mapping of quantities in
space and across time was so laborious and costly that the questions
deemed feasible for study were much more constrained than they are today.
Using the technology available now, historical geographers and historians can
map—and re-map—information with a speed and flexibility unavailable a decade
ago in their search to identify and explain geographic patterns.
Consequently, new questions are arising and old ones are being re-examined as
well. The geographic dimension in contemporary social inquiry and historical
studies is being rediscovered and expanded in new directions. Vive les cartes? Vive les maps!
The purpose of this booklet is to illustrate how this renewed interest and the technology of Geographic Information Systems go hand in hand in the revisiting of old questions and in the charting of new explorations. The topic that we shall explore is a problem in environmental history: the impact on the human and physical environment of new technology. More specifically, we shall use GIS methods to examine the relationship between the development of the railway system and population change in industrializing England and Wales from 1851, the date of an accurate census, to 1914, when the rail system reached its peak in geographic coverage.
This study is possible only because of the pioneering work of Professor Humphrey Southall and Ian Gregory of the Geography Department at Queen Mary and Westfield College in London. Working together, and often with a team of other specialists, they have created and continue to develop the Great Britain Historical Data Base. To my knowledge, this is one of the largest and most advanced collections of historical GIS data in existence. One of its wondrous features is a set of dynamic boundary files for the Census Registration Districts of England and Wales. These files, or “coverages” include virtually all changes in the district boundaries from 1851 to 1911, the result of a vast effort of research and programming. Included also are attribute data from the decennial census enumerations of the same period[1]. With this expression of my appreciation and acknowledgement we can now consider the methods and technology of GIS.
What is
GIS?
Geographic Information Systems is the combination of computer-assisted cartography and data analysis. The abbreviated form, GIS, is typically used in writing and conversation. Each of the terms conveys a meaningful part of the whole:
· Geographic: geographic and spatial entities and realities, such as the location, size, and shape of ponds within a given area, or of rail lines and rail stations within districts of a state or country.
· Information: systematic collections of factual information or data as well as the use and the meaning of the data.
· Systems: the computer technology used to process, analyze, and display the data, including computers as well as the programs to make them function.
A GIS based research project is a process involving a number of phases or aspects. It begins with the collection of information and proceeds as follows.
· Collection: the conversion of geographic information from maps to digital form for use in computer programs; the collection of data regarding the attributes of geographic entities, such as the water quality of ponds in an area.
In addition to the data collected by Southall and Gregory, this part of the work included the digitizing of maps showing the rail lines as they existed in England and Wales in 1845, 1854, 1876, and 1914. Once in digital form, these data were combined with geographic coverages of the Census Registration Districts in England and Wales for each decade from 1851 to 1911. The coverages were joined with attribute data on the districts. Included for each decennial census year (1851, 1861, etc.) and for each district were the size of the population, the area of the district, and the estimated decennial change in the population size that is attributable to migration.
· Data Processing and Management: the storage and organization of the data in computer files and the retrieval of the data for analysis and display.[2]
· Analysis: the investigation of research questions, those posed in designing the project and those arising in the course of the study. Analysis refers to dividing larger questions into smaller components, searching for patterns in the data, interpreting their meaning, and identifying their likely explanations. In GIS research, the basic questions are geographic and spatial. What is where? Why is it there? Our key questions concern the geographic development and influence of railways. In mid 19th century England and Wales, where were the major rail lines located? What network of transport did the lines form? And what combination of economic and geographic factors explains the geographic structure and growth of this network?· Display and Presentation: the creation of maps, tables, and other displays of pertinent information that provide the support for the conclusions reached in the analysis; the presentation and reporting of the study results in a paper, essay, or book. In what follows, I shall concentrate exclusively on the last two components of GIS research, that is, on the analysis, display, and interpretation of data. The investigation centers on the growth and impact of the railway in England and Wales from 1851 to 1914. To proceed, we need to acquaint ourselves with the definition and application of some key geographic and statistical concepts. It is fitting to begin by noting the significance of spatial as opposed to non-spatial analysis. A good way to start, of course, is to look at a map!
Map 1.1 England and Wales: Selected Census
Registration Districts in 1861
Map 1.1displays the name and location of eight Registration Districts, which were administrative units established for census enumerations, beginning in 1841. The map also shows the boundaries of the districts—632 in all. This representation embodies the three components of GIS applications: points, lines, and polygons.·
Points: a point designates the coordinates of a geographic location; it has no spatial extension but marks a spatial position. In the map above the point features are hidden but are used to position the names of the districts.·
Lines: a line is set of connected points that form a segment. When the geographic information on a paper map was converted to digital form, the district boundaries were built first by making points into line segments, and then by combining the segments to form a polygon. Lines possess the spatial properties of length and width as well as elevation. As we shall see, “the iron roads” of railways are represented by lines in maps that follow.
· Polygons: a polygon is a closed, connected set of line segments. Each of the census registration districts is a polygon.
There are other geographic properties associated with this map that we can consider next, noting how they differ from non-spatial attributes of geographic entities. The table below displays both spatial and non-spatial attributes. In a GIS program, a table like this serves to store and retrieve the information needed to create maps and tabular displays. Reading from left to right, the first three columns contain examples of spatial attributes—the Shape, Area, and Perimeter of the registration districts. The non-spatial attributes come next, beginning with the Name of the districts.
Table 1.1 Spatial and Non-Spatial Attributes
SpatialAttributes |
|
Non-SpatialAttributes |
|
Shape |
Area km2 |
Perimeter km |
Name |
County |
Population 1861 |
Level of Pop. Size |
Pop. Density 1861 |
|
Polygon |
215 |
939 |
Alton |
Hampshire |
12063 |
Medium |
56 |
|
Polygon |
100 |
663 |
Barton Upon Irwell |
Lancashire |
39038 |
Medium |
391 |
|
Polygon |
393 |
1158 |
Bootle |
Cumberland |
5880 |
Small |
15 |
|
Polygon |
111 |
568 |
Haslingden |
Lancashire |
69781 |
High |
630 |
|
Polygon |
528 |
1344 |
Machynlleth |
Montgomeryshire |
12395 |
Medium |
23 |
|
Polygon |
5 |
123 |
Plymouth |
Devon |
62599 |
Large |
12388 |
|
Polygon |
1 |
51 |
St. Giles |
London |
54076 |
Medium |
55164 |
|
Polygon |
228 |
1160 |
Wolverhampton |
Staffordshire |
126902 |
Very Large |
557 |
|
Scale of Measure-ment |
Num. |
Numerical |
Nominal |
Nominal |
Numerical |
Ordinal |
Numerical |
As indicated in the last row of the table, the information that defines an attribute is of one kind or another, depending upon the properties of the information. In data analysis these properties are designated as an attribute’s scale of measurement. In this study, we will distinguish three scales of measurement.
· Nominal scale: the definition of a variable or attribute by a qualitative, as opposed to a quantitative, characteristic. The name of a district and the county to which it belongs are examples of nominal attributes.
· Ordinal scale: the identification of an attribute in terms of its position within an ordered set of categories.
In Table 1 the Level of Population Size is measured on an ordinal scale having four categories that range from small to very large. Each of the districts is identified as belonging to one of the four categories. Classified this way, the values of attributes carry quantitative information about the relative size of district populations, but they do not carry information about the magnitude of the population differences between districts. The districts are ranked by size, but they cannot be compared further through the use of addition, subtraction, and so forth, as can be done with what we shall call numerical variables or attributes.
· Numerical scale: the definition of an attribute in terms of a measurable, quantitative characteristic. The area of a district, its population and populations density are good examples of numerical attributes. Unlike their ordinal cousins, they can be compared mathematically, using addition, subtraction, multiplication, and so forth.
With these pre-luminaries in mind, let us consider next something further about the properties of geographic features. Map 1.2 below will serve as basis for our continuing study, so it merits some introductory comments.
Geographic Properties and Attributes
This map displays the names and locations of a sample of registration districts. It is a random sample of districts that comprises 5 percent of the population of England and Wales in 1861. A sample of this kind has the advantage of being sufficiently representative so that the
Map 1.2. Sample of Registration Districts in 1861
the statistical results of our work will be good estimates of the results that we would obtain if we analyzed all of the districts. Our sample consists of 32 districts, drawn at random from a total of 632 such units. The entire collection of districts makes up, in statistical terms, the population of interest for this study. In the next chapter, we shall work with maps based on the entire “population.” But for the moment, we’ll work with the sample. Working with a small collection of cases makes it easier to grasp the analytical methods and to make calculations by hand without a computer.
An understanding of geographic properties will help us read and interpret our maps. To that end, a good way to proceed is to use the geographic concepts to pose and answer questions about our sample districts. The table below will guide us through the process of applying the concepts. Reading from left to right, we first familiarize ourselves with the concept. Then we move on to consider example questions. Finally, we use the map indicated to determine the answers. In some cases the answers are given; for other questions the reader should search out the answers on his or her own.
Table 1.2 Geographic Properties
Geographic Properties |
Example Questions |
Answers(Based on Map 1.2. Sample of Registration Districts in 1861) |
|
||
|
Location · What is where? Where are the x features?
· Which geographic entities are in a given region of the country? |
Which Registration Districts (RDs) border the sea?
Which RDs are in the southeastern part of the country?
|
Cockermouth, Bootle, Pwllheli. Dolgelley, Machynlleth, Plymouth, Christchurch, Ringwood, Rochford |
|
||
|
Proximity How close is x to y? · How many x are within 30 kilometers of y? |
Which two RDs are the closest to Greater London? (Refer to Map 1.3.) According to the scale of the Map 1.3, what is the approximate distance in kilometers between Newton Abbot and Forehoe? How many RDs are within 80 kilometers of Wolverhampton? (Use Map 1.3)
|
Hatfield (about 33 km) Rochford (about 60 km) About 400 km |
|
||
|
Size
· How large is feature x?
· Do small x features form a pattern?
|
Which RD appears to have been the largest in area?
Did the smaller sized RDs tend to cluster in a particular region of the country? |
Cockermouth |
|
||
|
Adjacency · What is next to x?
· Do x and y features share a common boundary in region z? |
Which RDs shared a common boundary?
|
Cockermouth and Bootle Okehampton and Newton Abbot Dolgelley and Machynlleth Ringwood and Christchurch |
|
||
|
Connection
· Are x and y connected by z?
· Do those x and y features that are connected by z form a pattern? |
Which RDs were more or less directly connected by rail line to London? (Use Map 1.5.)
Were the RDs connected to London by rail other significant urban centers? (Use Map 1.4.)
|
Hatfield
|
|
||
|
Containment
· Does z fall within x?
· How large is z within x?
|
Which RDs fall in the lowest level of population density, measured in persons per square kilometer? (Use Map 1.3.) Which RDs fall within the 81 to 128 level of population density? (Use Map 1.3.)
|
Haltwhistle
|
|
||
|
Pattern
· Does z fall within x usually when y is also present in x?
· More often than not, is there a connection between x and y in one region but not in another?
|
What pattern or relationship seems to have existed between major population centers and the extent of the rail lines in 1854? (Use Map 1.4.)
Which region was best served by rail service? London and the Southeast? The Midlands and lower Northwest? The Northeast? The Southwest? (Use Map 1.4 or Map 1.5.)
|
The Midlands and the lower Northwest. |
|||
|
|
|
|
|
|
|
Map 1.3 Population Density in Sample Registration Districts, 1861
Map 1.4 Urban Centers and Rail Lines, 1861
Map 1.5 Sample Districts and Rail
Lines, 1861
Non-Spatial Attributes
Geographic features often contain characteristics that are non-spatial but important to study nonetheless, both on their own and as complements to geographic information. As we saw in Table 1, non-spatial attributes are included in our GIS data base. As we did when familiarizing ourselves with geographic properties, let us review some characteristics of non-spatial attributes, using the table below and the data in Table 1.4.
Table 1.3. Population Size: Properties of a Non-Spatial, Numerical Attribute
|
Size or Magnitude
· What is the maximum value of a set of numbers |
Which of the RDs has the largest population, i.e., the maximum value of this attribute?
|
Wolverhampton in the County of Staffordshire
|
|
· What is the minimum value? |
Which has the smallest population, i.e., the minimum value of this attribute?
|
Ringwood in the County of Hampshire
|
|
Range: the difference between the minimum and maximum values.
|
What is the range of the population size for the sampled districts?
|
|
|
Center: the central tendency of a distribution of numbers, frequently summarized by either the mean or the median.
· The mean is the average of a list of numbers
· The median is the mid-point in a list of numbers arranged in order of magnitude
|
What was the mean population size of the 32 sample districts?
What was the median population size of the districts? (Use Table 1.5) |
|
Table 1.4 Sample Districts Attributes
|
Name |
Area |
County |
Population 1861 |
Population. Density 1861 (persons per km2) |
Rail Density (km per km2) |
||
|
|
|
|
|
|
1845 |
1854 |
1876 |
|
Alton |
215 |
Hampshire |
12063 |
56 |
|
4 |
61 |
|
Barton Upon Irwell |
100 |
Lancashire |
39038 |
391 |
82 |
174 |
328 |
|
Bootle |
393 |
Cumberland |
5880 |
15 |
|
62 |
90 |
|
Cheltenham |
108 |
Gloucestershire |
49792 |
462 |
14 |
13 |
13 |
|
Christchurch |
129 |
Hampshire |
10438 |
81 |
|
48 |
121 |
|
Cockermouth |
690 |
Cumberland |
41292 |
60 |
15 |
29 |
101 |
|
Dolgelley |
639 |
Merionethshire |
12482 |
20 |
|
|
86 |
|
Forehoe |
156 |
Norfolk |
12818 |
82 |
|
166 |
166 |
|
Haltwhistle |
394 |
Northumberland |
6693 |
17 |
45 |
90 |
90 |
|
Haslingden |
111 |
Lancashire |
69781 |
630 |
|
108 |
108 |
|
Hatfield |
121 |
Hertfordshire |
8400 |
69 |
|
80 |
214 |
|
Knaresborough |
165 |
West Riding |
17176 |
104 |
|
364 |
177 |
|
Lexden |
284 |
Essex |
22950 |
81 |
55 |
85 |
103 |
|
Lutterworth |
240 |
Leicestershire |
15515 |
65 |
65 |
74 |
76 |
|
Machynlleth |
528 |
Montgomeryshire |
12395 |
23 |
|
|
114 |
|
Newton Abbot |
486 |
Devon |
59063 |
122 |
|
59 |
102 |
|
Northwich |
260 |
Cheshire |
33338 |
128 |
93 |
94 |
189 |
|
Okehampton |
538 |
Devon |
18580 |
35 |
|
|
53 |
|
Oswestry |
336 |
Shropshire |
23817 |
71 |
|
71 |
149 |
|
Plymouth |
5 |
Devon |
62599 |
12388 |
|
469 |
1214 |
|
Pwllheli |
379 |
Carnarvonshire |
20908 |
55 |
|
|
43 |
|
Richmond (Surrey) |
21 |
Surrey |
18802 |
888 |
235 |
236 |
236 |
|
Ringwood |
149 |
Hampshire |
5357 |
36 |
|
109 |
162 |
|
Rochford |
377 |
Essex |
18282 |
49 |
|
|
39 |
|
Shaftesbury |
149 |
Dorset |
12986 |
87 |
|
|
56 |
|
South Molton |
590 |
Devon |
19209 |
33 |
|
9 |
49 |
|
St. Giles |
1 |
London |
54076 |
55164 |
641 |
738 |
738 |
|
Stamford |
216 |
Lincolnshire |
18213 |
84 |
|
108 |
243 |
|
Thrapston |
225 |
Northamptonshire |
14065 |
62 |
|
56 |
81 |
|
Warminster |
240 |
Wiltshire |
15942 |
67 |
|
9 |
77 |
|
Winchcomb |
231 |
Gloucestershire |
10082 |
44 |
|
|
11 |
|
Wolverhampton |
228 |
Staffordshire |
126902 |
557 |
24 |
143 |
168 |
Table 1.5 Rank Order of Population, 1861
|
Table 1.5 rearranges two columns from the preceding table by reordering them from the smallest to the largest in population size. This makes it much easier to determine the median because we count down to the middle case. Because there are an even number of observations, we have to calculate the median by taking the average of the observation on either side of the theoretical middle, in this example the 16th and 17th numbers on the list. Hence,
the median = (18213 + 18282) / 2 = 18248 |
|
Name |
Population 1861 |
|
|
|
|
|
|
|
Ringwood |
5357 |
|
|
|
Bootle |
5880 |
|
|
|
Haltwhistle |
6693 |
|
|
|
Hatfield |
8400 |
|
|
|
Winchcomb |
10082 |
|
|
|
Christchurch |
10438 |
|
|
|
Alton |
12063 |
|
|
|
Machynlleth |
12395 |
|
|
|
Dolgelley |
12482 |
|
|
|
Forehoe |
12818 |
|
|
|
Shaftesbury |
12986 |
|
|
|
Thrapston |
14065 |
|
|
|
Lutterworth |
15515 |
|
|
|
Warminster |
15942 |
|
|
|
Knaresborough |
17176 |
|
|
|
Stamford |
18213 |
|
|
|
Rochford |
18282 |
|
|
|
Okehampton |
18580 |
|
|
|
Richmond (Surrey) |
18802 |
|
|
|
South Molton |
19209 |
|
|
|
Pwllheli |
20908 |
|
|
|
Lexden |
22950 |
|
|
|
Oswestry |
23817 |
|
|
|
Northwich |
33338 |
|
|
|
Barton Upon Irwell |
39038 |
|
|
|
Cockermouth |
41292 |
|
|
|
Cheltenham |
49792 |
|
|
|
St. Giles |
54076 |
|
|
|
Newton Abbot |
59063 |
|
|
|
Plymouth |
62599 |
|
|
|
Haslingden |
69781 |
|
|
|
Wolverhampton |
126902 |
Table 1.3 (Continued)
|
Frequency Distribution: an arrangement of the numerical values of an attribute showing how frequently they occur.
|
What is the distribution of population size in 1861? |
Study Figure 1.1. |
Figure 1.1is a histogram displaying the distribution of the attribute, population size, in 1861. The values have been reorganized by classifying them into a set of defined categories. The first category is defined as values less than or equal to zero; the last or highest category, as values greater than 130,000. The others consist of intervals of 1000 each, beginning with the interval between 0 and 1000 (greater than zero, up to and including 1000). The scale of measurement here is ordinal. To facilitate the analysis, the original numerical attributes have been converted to ordinal ones. In the process, we lose information (the ability to be more precise, to calculate a mean, and so forth) and sacrifice complexity in order to construct a model, or a simplification, of the underlying distribution. Such simplifications often prove revealing.
What does this histogram reveal? Clearly, the majority of districts have populations of between 10,000 to 20,000 people. A glance at the y axis and the number at the top of the largest bar shows that 16 of the districts—50 percent of the sample—are of this size. Taking this category as “an anchor point,” we see that the distribution falls away to the right, as the number of districts within each category declines from the peak of 16 to 1 in the range between 120000 and 130000. In statistical terms, the distribution is skewed to the right.
Figure 1.1. Histogram of Population Size in 1861
This shape is common to many kinds of economic and demographic data. As in the past, so today, distributions of income and wealth usually resemble this structure. The incomes of most people fall in the lower range, while a small or tiny minority take home the “big bucks.” Many people work at McDonalds but few have the income of a Bill Gates.
In the example before us, the great majority of districts contained populations in the lower range, while only one had more than 100,000 inhabitants. This distribution reflects the evolving demographic landscape of industrializing and urbanizing England and Wales as a whole.
· During the 19th century the differences in size between the majority of communities and the few very large cities were enormous. To cite one example, there were some 3,000 people living in the parishes of Blean near Canterbury (County of Kent), compared to the nearly 3 million inhabitants of London around 1850.
· By 1851, the majority of people in England and Wales lived in cities of 10,000 or more.
· Nonetheless, cities over 100,000 in population continued to make up a minority of communities.
This said, we need to bear in mind some characteristics of census registration districts. They were not specific communities but administrative units that varied in physical size or area. Districts were centered on market towns or on wards within larger cities. Hence, a rural district typically subsumed one or more villages while an urban district such as St. Giles in London contained only part of the entire city’s much larger population.
That districts were units of differing geographic size is another characteristic that deserves a comment. Among the districts, this characteristic accounted for some of the variation in population size. In some situations it is important to remove the effect that one attribute has on another in order to make standardized comparisons. To neutralize the effect of unequal areas in our study, we can make comparisons using population density: the number of people per square kilometer. If we know the area and the population of a set of units, the calculation of density is straight forward. For each unit:
population density = population / area
We have already seen in Map 1.3 a standardized comparison showing different levels of population density among our sample districts. We shall return often to maps of this kind that display the geographic distribution of attributes that have been standardized by area. In an exercise at the end of this chapter, the reader is asked to use the histogram above (Figure 1.1) as a model to create another one for the distribution of population density. For the moment, however, let us return to our analysis of population size.
In addition to a histogram, we can describe a distribution in terms of its center and spread. Earlier we glanced at the mean and the median of a list of numbers, but a second look is needed now to understand the connection of these statistics with a frequency distribution. In this context, the concept of center carries the idea of typicality. The mean and the median provide measures of the central or typical value of a distribution.
· As a central value, the mean is the balance point in a distribution, the point at which the base of a histogram, like a teeter tooter, would remain balanced and level if it rested on the mean.
In Figure 1.1 the mean is the point that serves to balance the large cluster of districts in the lower range against the single district at the extreme right end of the distribution. Being a balance point, the mean is sensitive to extreme values. In this case the single extreme value in the 120000 to 130000 range serves to “pull” the mean away toward it.
· Unlike the mean, the median is not sensitive to extreme values.
This “insensitivity” to extremes is reflected in the difference between median and the mean shown in Figure 1. At 18,248 the median population size of the sample is considerably smaller than the larger mean of 27, 154. Unmoved by the influence of extreme values, the median sits squarely in the middle position of an ordered set of numbers. It divides the set into two equal groups: Fifty percent of the numbers lie below the median; 50 percent lie above.
Both measures of center are informative single-number summaries of a distribution. But the lesson here deserves underscoring: when the median and mean are quite different, this is a clear sign that the underlying distribution of values is skewed either to the right, as in this case, or to the left, as the distribution of ages at death would show. Few die young; most die in advanced age. Studying the mean and the median is a good habit to develop.
After identifying the center of a distribution, we should note how the remaining values are arranged in relation to the center. The term “spread” refers to this characteristic, and there are several measures of spread that are linked either to the mean or to the median. The standard deviation is the measure of spread used with the mean; percentiles and quartiles are typical measures used with the median.
· The standard deviation is the average of the distances between the values in a distribution and the mean. A small SD tells us that the values are more closely clustered about the mean than the values of another distribution with a large SD.
The spread of values around the median is indicated by several other measures that are analogous to percentages.
· Percentiles divide a list of numbers arranged in order of size into 100 parts. Hence, the 10th percentile is the value in a list below which are 10 percent of all the values. On an exam, the 95th percentile in a list of scores is the number that stands above 95 percent of the other scores. It’s an “A.” The 50th percentile is none other than the median.
· Quartiles divide an ordered list of numbers into 4 parts, each part consisting of 25 percent of the list. The first, or lower quartile is the value below which are 25 percent of the numbers. The third, or upper quartile, is the value below which are 75 percent of the numbers.
· Quintiles divide an ordered list into 5 parts, each of which comprises 20 percent of the values in question. We shall return to this concept later when considering the various ways of dividing a distribution when making and interpreting maps.
Now familiar with some geographic and statistical concepts we can apply them to continue our investigation into social and environmental history. It is with the growth of the railways in England and Wales that the next chapter begins.
Exercises
1. On a copy of Map 1.3, draw two intersecting lines that divide England and Wales into four regions. The horizontal line should run from Dolgelley to Forehoe such that Wolverhampton is below the line. The vertical line should proceed from Christchurch due north so that it runs between Knaresborough and Haltwhistle. Using these regions of Northeast, Northwest, Southeast, and Southwest, do you find a pattern of population density that varies by region? Explain.
2. On a second copy of Map 1.3, shift the lines in any direction that seems to make sense. To what extent does this change the results that you found in question 1? Is there a regional pattern? What lesson do you draw from this comparison?
3. The table below shows how the properties of attributes differ according to the scale of measurement. A knowledge of attribute properties is important in choosing appropriate statistical procedures in an analysis. It makes no sense, for example, to try to determine the mean of a nominal attribute, such as Name or Gender. Sometimes a degree of precision in information can usefully be sacrificed to get a clearer picture of an underlying pattern, as we saw in the case of the histogram displaying the distribution of population size. To make the histogram, a numerical attribute (population) was transformed into an ordinal version (Level of Population Size) so the result would highlight a clearer, if less precise, picture of the distribution. To familiarize yourself further with the differing kinds of attributes, first study the table. Then define at least one additional attribute for each of the 6 types represented below in the cells of the table. The new attributes can come from the data presented or suggested here, or from any other context.
Table 1.6. Attributes and Scales of Measurement
|
|
Attributes |
|
|
Scales of Measurement |
Geographic/Spatial |
Statistical/Non-Spatial |
|
Nominal |
Shape: square, rectangle, circle, ellipse Feature: point, line, polygon Adjacent: yes, no
|
Name of District County Gender
|
|
Ordinal |
Proximity: near (1-10 km), in region (10-40 km), distant (> 40 km) Area: small, medium, large
|
Level of Population Size Income Level: low, medium, high
|
|
Numerical |
Position in coordinates of longitude and latitude Area in square kilometers Elevation in meters Proximity in kilometers Position of Rail Lines
|
Population Size Population Density Income in Dollars Number of Migrants Density of Rail Lines Number of Rail Stations Length of Rail Lines |
4. Use Table 1.4 and Figure 1.1 to create a histogram showing the distribution of population density among the 32 sample districts. Describe the pattern you see and explain what you think it reveals about rural and urban life in 19th century England and Wales.
5. Map 1.4 displays major urban centers and the rail system as they existed around 1860. It allows one to take up questions about geographic connections and geographic patterns based on spatial connections. Use the map to “test” and discuss the soundness of the following generalization.
The building of railroads required so much capital that rail companies were at pains to ensure that their large investments would eventually return a profit. Competition among them for the rights to build one route or another was also stiff. Under these circumstances companies looked first to establishing lines providing transport of natural resources to industrial centers and finished products to markets.
6. Study the figures on Rail Density in Table 1.4.
a) Describe the advantage of using this attribute as opposed to one that records the length of rail lines in each district.
b) Create three histograms that show the extent to which railways developed in the sample districts.
c) Briefly state how you would display your results on a map?
Chapter 2
Railways and Population Change, 1851-1914
The social and economic transformation of nineteenth-century England and Wales is the classic example of western industrialization and urbanization. Viewed from the perspective of social and environmental history, this transformation provides an interesting way to examine the impact of new technology on past human and physical environments. One far-reaching example is the steam-powered railroad system which grew to reach nearly all corners of England and Wales from its beginning in the 1830s to its apogee on the eve of World War I. The landscape of the Victorian City was a monument to the railway age, with its huge train stations and rail yards, together with the great earthworks and tunnels that the rail network required. To its stations, moreover, came more and more individuals and families who were moving to town in search of better opportunities, leaving the countryside behind and villages in decline.
Did the railways facilitate migration from countryside to town? What was the timing, extent, and geography of rural depopulation? Did rural men and women migrate in similar patterns? Thanks to GIS methods, all of these questions can be taken up more effectively now than was previously possible. To pursue them, it is advisable to break down the issues into smaller components and examine them first. Looking into the growth of the rail system is a good place to start.
The Growth of the Rail System
The Railway Age began in 1830 with the opening of the line between the port city of Liverpool and Manchester, the center of the revolution in mechanized textile production. Although locomotives pulling coal had earlier steamed along parts of the Stockton and Darlington line which opened in 1825, it was the Liverpool-Manchester railway that was the first to be truly successful. Reflecting the enthusiasm that marked the opening of this new marvel of the age, there were lords, ladies, and gentlemen in attendance to witness the Duke of Wellington commemorate the occasion. The exhilaration of the day, though, was marred by accident. When William Huskinson, a government minister and rail enthusiast, caught sight of the Duke, he went to greet him, but in crossing the track he was struck and killed by a passing locomotive. Thus did the dangers of the rail age make their mark on this historic day. Huskinson’s untimely end, however, was soon overshadowed by the excitement surrounding the transportation revolution, capturing the Victorian public’s imagination in short order.
Writing of his first train journey in 1830, the Reverend Edward Stanley recalled the elation he and his companions felt.
No words can convey an adequate notion of the magnificence ( I cannot use a smaller word ) of our progress. At first it was comparatively slow; but soon we felt that we were GOING, and then it was that every person to whom the conveyance was new, must have been sensible that the adaptation of locomotive power was establishing a fresh era in the state of society.…
The most intense curiosity and excitement prevailed... and ... enormous masses of densely packed people lined the road, shouting and waving hats and handkerchiefs as we flew by them. What with the sight and sound of these cheering multitudes and the tremendous velocity with which we were borne past them, my spirits rose to true champagne height.[3]
As the years wore on and the rail system grew rapidly, some contemporaries saw in the railroad the reflected image of the technical and moral progress they so cherished. Samuel Smiles was one notable example. A self-educated man, his many books celebrated the feats of civil and mechanical engineers and the Victorian virtues their stories embodied. Writing in 1859, he described the railway locomotive, one of their great feats, as nothing less than a moral force for good.
The iron rail proved a magicians' road. The locomotive gave a new celerity to time. It virtually reduced England to a sixth of its size. It brought the country nearer to the town and the town to the country... It energized punctuality, discipline, and attention; and proved a moral teacher by the influence of example.
Not everyone shared such sentiments. Others saw something more menacing in the changes ushere