On the Possibility of Using Geoinformation Technologies to Study Economic Inequality (Based on the Materials of the Siberian Region of 1926-1927)

Vladimirov Vladimir Nikolayevich Doctor of History Professor, Department of Russian History, Altai State University 656049, Russia, Altai Krai, Barnaul, Lenin Avenue, 61, room 312 vvladimirov@icloud.com Other publications by this author

Krupochkin Evgenii Petrovich PhD in Geography Associate Professor, Head of the Department of Economic Geography and Cartography, Altai State University  656049, Russia, Altaiskii krai, g. Barnaul, ul. Prospekt Lenina, 61, aud. 502M krupochkin@mail.ru Other publications by this author

IntroductionThe study of various aspects of economic inequality in the past is one of the leading trends in economic history.

The interest in this is due not only to the possible projections of the developed provisions and formulated conclusions to the present, but also to the quite achievable prospects for obtaining reliable and verifiable historical knowledge based on a wide range of statistical sources and a solid methodological base involving mathematical methods and information technologies.

Meanwhile, geoinformation systems and technologies, the popularity and effectiveness of which in historical research has reached a very high level, have not yet become a familiar tool for studying historical aspects of inequality. At the same time, the study of such one of the types of economic inequality in our country – wage inequality – inevitably faces the problem of regional peculiarities in the vast territory of Russia, where the specifics of natural-geographical and socio-economic conditions have determined the specifics of socio-economic processes.

As a result, there should be access to the spatial features of the regions and their influence on the specifics of the processes taking place in a particular territory. Thus, the possibility of using geoinformation technologies in this case can be said a priori. This paper discusses some aspects of the application of geospatial analysis in the study of wage inequality among employees of district cities of the Siberian Territory in 1926-1927 (the period of the late NEP).

Sources and methodologyThe Siberian Region was formed on May 25, 1925 by the decree of the Presidium of the Central Executive Committee ^[1].

As a result of the enlargement of the administrative-territorial division, a giant region appeared, which included 6 provinces and an autonomous region. The capital was the city of Novonikolaevsk (since 1926 – Novosibirsk). At the same time, the formation of the system of state statistics took place ^{[2; 3; 4]}. The regional statistical bureau was launched, and later the regional statistical department. Powerful statistical forces consisting of high-level practical statisticians were concentrated in the regional center. They published, in particular, various statistical collections, including a series of publications "Bulletin of Labor Statistics of the Siberian Region" (1926-1928) and its continuation "Bulletin of Labor and Industry Statistics" (1928-1929).

It is clear that not all statistical data are suitable for working with them in geoinformation systems. A necessary condition here is their binding to certain spatial units, which may be administrative-territorial territories. From this point of view, we have selected data that characterize the salaries of employees of the district cities of Siberia for 8 quarters, covering almost 2 years – 1926 and 1927.

Based on the available data, a table was created in Microsoft Excel for further processing. Some adjustments have been made to the table. Thus, the cities of Tulun and Kirensk, for which data were provided only for part of the period, were excluded from the table. In the list of cities, along with Barabinsk, Kainsk is indicated, which was not the center of the district, but in most cases wages are indicated in it, although the center of the district was Barabinsk. The missing Biysk salary value for February 1927 was restored as the average value between the wages of two adjacent periods (Table 1).

To create thematic maps and further analyze spatial patterns, we calculated the average wage values for the period under review, as well as the growth rate, both of these indicators were added to the table. The growth rate is based on the formula: where ?P is the growth rate, Pt is the current period (the most recent data in chronological order), Pb is the value of the indicator for the previous or base period of statistical observation.

 Table 1

Sources: ^{[5, p. 41, table. 12; 6, p. 22, tab. 12; 7, p. 31, table 18; 8, p. 39, table. 17; 9, p. 26, Table 15]}.

To develop historical GIS and further data visualization using its tools, as well as geospatial analysis of various aspects of inequality of the population of the Siberian Region, operations were performed to create a set of spatial historical data, as well as geocoding of statistical information, GIS functionality was applied for geostatistical processing and analysis of the results obtained.

Fig. 1. Publication of the map of the Siberian Region used for digitization in GIS.

Source: ^[10].

During the development of GIS, a 1:3000000 scale map of the Siberian Region was digitized (Fig. 1), on which there is a coordinate grid, the situation (hydrographic network and relief displayed by washing) and the administrative division according to the zoning data of 1929 were plotted (Fig. 2).

Fig. 2. Map of the Siberian Region: compiled by V.G. Boldyrev.

Source: ^[10]

Special attention was paid to the mathematical basis. A modern reference point is taken as the origin of the reference system/coordinate system – the intersection of the Greenwich meridian line with the equator line. It is indicated on the map that its main scale is preserved at the 50th and 60th parallels of the S.S., this indicates the use of a conical projection, but we did not have the exact parameters of this projection.

The tasks preceding the entire cycle of work on the creation of historical GIS were the transformation and geo-linking of the specified map. Transformation is a procedure of geographical reference and geometric correction of an image (raster), in which not only the creation of a mathematical basis occurs, but also the elimination of distortions on the map (raster) by performing mathematical transformations. The full cycle of geo-linking operations in the case of minor distortions on maps is described by us in the previously published article ^[11].

However, for our card, in fact, this procedure was performed twice:

1. When converting a raster using the spline method in ArcGIS;

2. When importing the transformed raster and georeferencing in MapInfo PRO.

To perform the transformation, a sufficient number of links is required, provided that the raster (map) corresponds to the coordinates of the target data. To achieve the maximum coincidence of coordinates, a coordinate grid layer created with a step of 2 degrees is used. To transform raster data, we used the spline method with the following parameters:

X’ = Ax + By + C,

Y’ = Dx + Ex + F,

where X is the number of columns in the raster, Y is the number of rows, X’ is the horizontal value in coordinate space, Y’ is the vertical value in coordinate space, A is the width of the cell in map units, B is rotation, C is the x’ coordinate of the center of the upper left cell, D is rotation, E – negative height of the cell in map units, F – y' coordinate of the upper left cell (Fig. 3).

Fig. 3. The scheme of raster transformation using mathematical transformations

Source: ^[12].

Thus, six key parameters determine the transformation of the raster into a given coordinate system and projection – A, B, C, D, E, F. The final stage was the import and re-correction of the raster at the nodes of the coordinate grid (Fig. 4).

Fig. 4. The scheme of binding and correction of the imported raster in the MapInfo PRO program

The completed cycle of operations minimizes distortion across the map field and allows you to work with it in a GIS environment. Let's note the main advantages of the processed and geo-linked raster:

1. Digitization in a given coordinate system;

2. The possibility of using any cartometric operations and functions provided by the GIS functionality for working with spatial data;

3. The possibility of correct layout;

4. The ability to perform overlay operations;

5. Analytical operations.

When digitizing the boundaries of the districts, we used a geo-linked historical map of the Siberian Territory published in 1929, which no longer had the Tarsky District, the entire territory was part of the Omsk District. Therefore, first, natural and administrative landmarks (internal division, river network) were determined, and then the border between Omsk and Tarsky districts was already drawn along them.

The next step was geocoding the database from the Microsoft Excel environment into GIS. Geocoding refers to the process of displaying external or internal (data in the system) spatially coordinated data on the map, or the process of converting a location description (for example, coordinates, address or place name) to a location on the Earth's surface ^{[13; 14]}.

The digitization of the districts was carried out taking into account the topology of the map and layers, in which the topological (or geometric) relations between the common parts of objects for all objects within the layer have an equivalent value. This allows you to edit objects with a common geometry at the same time. When creating topologically correct objects, individual sections were traced when they coincided with the layer of modern administrative division. This approach, firstly, saves time on digitization, and secondly, increases the accuracy of the location of objects plotted on the map in the plan.

The next stage (geospatial historical analysis) consisted in constructing a series of maps reflecting the average salary level of employees in district cities, as well as the dynamics of wages based on quarterly statistics (Table 1). As an indicator of dynamics, we used the growth rate. At the same time, for the convenience and clarity of visualization, we considered it possible to distribute data on the cities – the centers of the districts – to the entire territory of the districts. Thus, when analyzing cartograms, it should be borne in mind that we are not talking about districts as a whole, but only about their centers.

Main resultsTo visualize the distribution of statistical indicators, both absolute (initial) and relative, we used discrete methods of constructing thematic maps in a GIS environment.

They are represented by classical cartograms built on a grid of districts and displaying the corresponding statistical indicators for two years – 1926 and 1927.

Visualization of average salary values by city (Fig. 5) shows that the highest salary of employees is observed in Novosibirsk, which is not surprising, since it is the capital of the Siberian Region.

Fig. 5. Distribution of average wages in the district cities of the Siberian Territory in 1926-27 (in gold rubles)

By expert means, we have identified the intervals of wages shown on the map in the district cities corresponding to low (up to 56.0 rubles), medium (from 56.1 to 60.0 rubles) and high (from 60.1 to 70.0 rubles). her level. Novosibirsk, where the salary is the highest (77.62 rubles), we considered to allocate it to a separate group as the center of the Siberian Region.

Fig. 6. Cartogram of the average salary of employees of district cities of Siberia by districts of the Siberian Territory in 1926-1927. On the cartogram (Fig. 6), it is noticeable that cities with low wages are concentrated in the south of Western Siberia (Barnaul, Biysk, Kamen), this also includes Tara and a number of cities in the central part of the Siberian Territory (Krasnoyarsk, Kansk, Minusinsk).

Cities with an average salary level are located in Eastern Siberia (Irkutsk) and Western Siberia (from Tomsk in the north to Ust-Abakansk in the south), they also include the cities of modern Altai Krai Slavgorod and Rubtsovsk. The high level of wages characterizes two cities in Western Siberia (Kainsk, Omsk) and the center of the Oirot Autonomous Region of Ulalu, which at that time was just becoming a city. Given the geographical location of Novosibirsk, we can talk about a high differentiation of intercity salaries of employees in the district cities of Western Siberia.

Visualization of salary growth rates by city (Fig. 7) shows a greater variation than the average salary. There are more or less three groups of cities with low (up to 13.0%), medium (from 13.1 to 40.0% and high (over 40%) growth.

Fig. 7. Distribution of the average wage growth rate of employees in the district cities of the Siberian Territory in 1926-27 (in %)

Fig. 8. Cartogram of the growth rate of average wages of employees of district cities of Siberia in the districts of the Siberian Territory in 1926-1927.

On the cartogram (Fig. 8) it is clearly noticeable that the greatest increase in wages is demonstrated by those cities in which its level was low (Kamen) or medium (Slavgorod, which is the leader of the increase, Shcheglovsk and Achinsk), and sometimes even high (Kainsk). All these cities, with the exception of Achinsk, are located in Western Siberia. The smallest increase is observed, on the one hand, in cities with high salaries (Novosibirsk, Omsk). But this also includes a number of cities with low wages, the lack of growth of which leads to the allocation of peculiar "outsider cities" in this regard (Barnaul, Biysk, Kansk). Thus, in the district cities of Western Siberia, there is a further inter-city differentiation of employees' salaries.

ConclusionThe given examples of the use of geoinformation technologies show that there are certain prospects in their use for the study of economic inequality.

For further development of this direction, it is necessary to expand the source base and improve the methodology and technique of research.