Visibility and Connections among Cities in Digital Space

Abstract

Alongside traditional approaches to defining the positions and prestige of cities, their functioning and visibility in digital space have recently gained attention. The objective of this paper is to describe the visibility of cities and their mutual connections in digital space. This study used the Google search engine to obtain information on Google-indexed web sites concerning Polish cities. Socioeconomic data were analyzed to identify the relationships and similarities between online and offline worlds. The results support conclusions about two important problems associated with such analyses: temporal changes and the relationships between online and traditional measures describing individuals’ socioeconomic activity.

Keywords:

• digital space
• Poland
• visibility of cities
• connections between cities
• online–offline relations

Acknowledgments

I thank the editor and three anonymous referees for their constructive comments and suggestions on the first version of this manuscript. No potential conflict of interest was reported by the author.

Note on Contributor

Krzysztof Janc is an assistant professor in the Department of Spatial Management, Institute of Geography and Regional Development, University of Wrocław, Poland.

Notes

1. For a more detailed discussion on the hierarchy and the rank of cities in terms similar to those outlined in paper see Boulton et al., 2011.

2. Localities selected for the analysis are, in order from the largest to the smallest and using Polish diacritics (as typed into the search engine), Warszawa, Kraków, Łódź, Wrocław, Poznań, Gdańsk, Szczecin, Bydgoszcz, Lublin, Katowice, Białystok, Gdynia, Częstochowa, Radom, Sosnowiec, Toruń, Kielce, Gliwice, Zabrze, Bytom, Olsztyn, Bielsko-Biała, Rzeszów, Ruda Śląska, Rybnik, Tychy, Dąbrowa Górnicza, Płock, Elbląg, Opole, Gorzów Wielkopolski, Wałbrzych, Włocławek, Zielona Góra, Tarnów, Chorzów, Koszalin, Kalisz, Legnica, and Grudziądz.

3. Alexa Traffic Rank measures web sites’ popularity based on activity criteria, a method known as traffic-based ranking.

4. In 2014, the Google Discussion search filter was removed. For the purposes of this study, this was not a problem because the regularities and relative relations examined here can be determined using other Google search filters.

5. For example, if we search for “Warszawa,” we can assume that some results will refer to the former cinema in Wrocław called Warszawa or the car Warszawa, which is no longer manufactured. This understanding indicates the city's importance as an immaterial being and whether the chances of finding a large number of articles in this area in major electronic media are within the margin of error.

6. The indices used are entrepreneurship (number of businesses per 10,000 inhabitants), average income, number of nongovernmental organizations per 10,000 inhabitants, per-capita personal income tax, and balance of arrivals and departures for work. A PCA conducted using the IBM SPSS Statistics 20 software returned only one component. Details of the PCA results are presented in the Appendix.

7. The figure shows the results for four, not five, study periods due to the similarity of the results and the desire to preserve the greater transparency of the figure.

8. The Upper Silesian conurbation has the largest concentration of mining and heavy industries and the highest density of large cities in Poland.

9. This example refers to the case of Katarzyna W., the murder of a baby girl by a woman from Sosnowiec.

10. “Edges” refers to connections between nodes (cities).

11. This measure can be indirectly identified with flows of people, because in a free market context, transport connections are central to moving income; i.e., they are used by customers.