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Concentration Analysis Using Microgreographic Data

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Defining the Spatial Scale in Modern Regional Analysis

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

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Abstract

Analysis of spatial distribution of economic activity has plenty of implications in several areas like urban planning, infrastructures, firm supporting policies and land use, among others, and is receiving an increasing attention by researchers. Most of analyses of spatial distribution of economic activity have been carried out using extant administrative units (e.g., counties, regions, etc.), but unfortunately, these analyses suffer from the shortcoming that administrative units vary greatly in size and shape, do not always coincide with real economic areas and are sometimes arbitrary.

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Notes

  1. 1.

    There are also other approaches such as those that use the stochastic methodology of Point Pattern or those that use Neuronal Networks for pattern recognition. However, these approaches are not able to do the multisectorial analyses that are the goal of this work. For a discussion about whether administrative units match functional or economic units see Parr (2008).

  2. 2.

    See, among others, the applications of Boix and Galletto (2008) for Spain and De Propris (2005) for the UK.

  3. 3.

    Applications for services are scarce. See, for instance, Sforzi (1999).

  4. 4.

    See Guimarães et al. (2011) for an application about how to solve biases caused by previous measures that do not take into account spatial position of units.

  5. 5.

    Concretely, Boix and Galletto (2008) identify four axes where specialized industrial district are of great importance: the main axis runs across the Mediterranean coast from the north of Catalonia to the south of Murcia; the second one links the south of Catalonia to the Basque Country and North-East of Castile and León; the third one goes South from Madrid to the provinces of Toledo, Ciudad Real, Jaen and Córdoba; and the last one is scattered across the provinces of Pontevedra and A Coruña (North-West of Spain).

  6. 6.

    See Openshaw and Taylor (1979) for a detailed analysis and Wrigley (1995) for a further review.

  7. 7.

    See, nevertheless, papers by Arauzo-Carod and Manjón-Antolín (2004) and Arauzo-Carod (2008) about the implications for industrial location analysis. See also Olsen (2002) for a broad discussion of the units to be used in geographical economics.

  8. 8.

    See Páez and Scott (2004) for a detailed report of techniques whose results are affected by MAUP problem.

  9. 9.

    We have omitted non mainland parts of Spain due to lack of continuity of Balearic Islands and Canary Islands with the rest of the country, which could cause important biases in our results.

  10. 10.

    It is important to notice that SABI data set is about firms, not establishments, so each firm could have more than one establishment, although most of firms have only one establishment.

  11. 11.

    Other alternative statistical sources such as Censo de Locales (INE) are not currently updated. Although having firms as observation units instead of establishments, the Censo de Locales also provides useful information for locational analysis.

  12. 12.

    There are alternative datasets such as DIRCE (INE) but their data is presented only at two-digit level and geographical location of the firms is also highly spatially aggregated.

  13. 13.

    See, among others, Brenner (2006, 2004) and Ellison and Glaeser (1997) for empirical applications with such administrative units.

  14. 14.

    In view that some of MAUP problems come from size and shape of administrative units we should tackle both issues. While size implications are analysed in detail at beginning of Sect. 15.4, shape problems can be overcomed by using neutral cells, like squares, so we avoid problems linked to ad-hoc designs of geographical units (gerrymandering).

  15. 15.

    Similarly the index could be calculated for bigger cell sizes.

  16. 16.

    In any case, upper levels could be needed in order to analyse multisectorial clusters à la Porter (1998).

  17. 17.

    As an example, indices of high-tech industries such as office machinery, computers and medical equipment, precision and optical instruments (0.644) and electrical machinery and apparatus (0.664) are clearly lower than those of some low-tech industries such as food, beverages and tobacco (1.452) and agriculture and fishing (1.424).

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Author information

Authors and Affiliations

  1. Universidad de Alcalá, C/Alvaro Cumqueio, 7, Alcala de Henares, Madrid, 28805, Spain

    Federico Pablo-Martí

  2. Facultat d’Economia i Empresa, Universitat Rovira i Virgili (CREIP-QURE), Avda. de la Universitat, 1, Reus, 43204, Spain

    Josep-Maria Arauzo-Carod

Authors
  1. Federico Pablo-Martí
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  2. Josep-Maria Arauzo-Carod
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Corresponding author

Correspondence to Federico Pablo-Martí .

Editor information

Editors and Affiliations

  1. , Applied Economics (RegioLab), University of Oviedo, Campus del Cristo s/n, Oviedo, 33006, Spain

    Esteban Fernández Vázquez

  2. , Applied Economics (RegioLab), University of Oviedo, Campus del Cristo s/n, Oviedo, 33006, Spain

    Fernando Rubiera Morollón

Appendix 1 – List of Industries

Appendix 1 – List of Industries

Code

Industry

1

Agriculture and fishing

2

Extractive activities

3

Food, beverages and tobacco

4

Textiles, leather clothes and shoes

5

Wood, furniture and other manufactures

6

Paper and publishing

7

Chemical products

8

Rubber and plastic products

9

Non-metallic mineral products

10

Basic metals

11

Fabricated metal products

12

Machinery and equipment

13

Office machinery, computers and medical equipment, precision and optical instruments

14

Electrical machinery and apparatus

15

Transport materials

16

Recycling

17

Construction

18

Electricity and water distribution

19

Trade and repair

20

Hotels and restaurants

21

Transport and communications

22

Financial intermediation

23

Real estate activities

24

Business services

25

Public administration

26

Education

27

Health and veterinary activities, social services

28

Other services

  1. Source: SABI

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Pablo-Martí, F., Arauzo-Carod, JM. (2012). Concentration Analysis Using Microgreographic Data. In: Fernández Vázquez, E., Rubiera Morollón, F. (eds) Defining the Spatial Scale in Modern Regional Analysis. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31994-5_15

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  • DOI: https://doi.org/10.1007/978-3-642-31994-5_15

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