Category Archives: GIS

Statistical Atlas – Eurostat regional yearbook 2013

Commodious, utilitarian and valid tool for secondary data based and European cross-national research. Relevant information on economy and finance; population and social conditions; industry, trade and services; agriculture and fisheries; transport; science and technology; among others.

Nowy obraz (18)

Source: http://ec.europa.eu/eurostat/statistical-atlas/gis/viewer/

Interactive maps

The representation of data into maps is for me one of the best ways to illustrate the reality we address. I am glad to share here rMaps, an application to create, customize and share interactive maps from R, with a few lines of code. It supports several javascript based mapping libraries like Leaflet, DataMaps and Crosslet, with many more to be added. You may design such amazing maps as the ones I pin in my pinterest board “Map of the day“.

Further details here:

 

World population in one map

woldwide

Source: Planet Earth @planetepics (2014) “Where humans live” pic.twitter.com/C8HxyAjJdM (9th of January, 2014)

Example of explanatory research by mean secondary data

A referendum to limit migration from European Union countries took place on 10th February of 2014 in Switzerland. In the score of such event, Alexandre Afonso (2014) and Paul Haydon (2014) did a simple analysis of correlation between the share of migrants population per canton and the share of yes to anti-immigration initiative, based on the results of the referendum. The research question that lies beneath these analysis might be “is there a relationship between the share of migrant population in a given community and the way migration is seen by its members. Interestingly both the graphic and map bellow show that wherever there is less number of immigrants, the rejection of immigrants is greater. It is a clear example of explanatory research, where the main objective is identifying the existence between two or more variables. By the way, Swiss voters narrowly back referendum curbing immigration.

The results of this case, also arose multiples new questions on how public opinion is build. Is there a real problem with immigrants or rather certain media shape deliberately population opinion?

map refer

migrants

Reference list

Haydon, Paul (@Paul_Haydon) (2014) “Map of who voted how in Swiss referendum. Areas with fewest immigrants most anti-immigration pic.twitter.com/uZqicWyvC4” 9th of February, 2014, 6:34 PM

Foulkes, Imogen (2014, February 11). Swiss immigration: 50.3% back quotas, final results show. BBC News. Retrieved from http://www.bbc.co.uk/news/world-europe-26108597

 Afonso, Alexandre @alexandreafonso (2014) “Relationship between share of migrants per canton and share of yes to anti-immigration initiative pic.twitter.com/MEqgN6a4Ww” 9th of February, 2014, 6:31 PM

Early maps and diagrams. A little bit of history

The earliest seeds of visualization arose in geometric diagrams, in tables of the positions of stars and other celestial bodies, and in the making of maps to aid in navigation and exploration. The idea of coordinates was used by ancient Egyptian surveyors in laying out towns, earthly and heavenly positions were located by something akin to latitude and longitude at least by 200 BC, and the map projection of a spherical earth into latitude and longitude by Claudius Ptolemy [c.85–c. 165] in Alexandria would serve as reference standards until the 14th century.

Among the earliest graphical depictions of quantitative information is an anonymous 10th century multiple time-series graph of the changing position of the seven most prominent heavenly bodies over space and time (Figure 2), described by Funkhouser (1936) and reproduced in Tufte (1983, p. 28). The vertical axis represents the inclination of the planetary orbits, the horizontal axis shows time, divided into thirty intervals. The sinusoidal variation, with different periods is notable, as is the use of a grid, suggesting both an implicit notion of a coordinate system, and something akin to graph paper, ideas that would not be fully developed until the 1600–1700s. The earliest graphical depictions of quantitative informationThe earliest graphical depictions of quantitative information.

In the 14th century, the idea of a plotting a theoretical function (as a proto bar graph), and the logical relation between tabulating values and plotting them appeared in a work by Nicole Oresme [1323–1382] Bishop of Liseus (Oresme, 1482, 1968), followed somewhat later by the idea of a theoretical graph of distance vs. speed by Nicolas of Cusa.

By the 16th century, techniques and instruments for precise observation and measurement of physical quantities, and geographic and celestial position were well-developed (for example, a “wall quadrant” constructed by Tycho Brahe [1546–1601], covering an entire wall in his observatory)  Particularly important were the development of triangulation and other methods to determine mapping locations accurately (Frisius, 1533, Tartaglia, 1556). As well, we see initial ideas for capturing images directly (the camera obscura, used by Reginer Gemma-Frisius in 1545 to record an eclipse of the sun), the recording of mathematical functions in tables (trigonometric tables by Georg Rheticus, 1550), and the first modern cartographic atlas (Teatrum Orbis Terrarum by Abraham Ortelius, 1570). These early steps comprise the beginnings of data visualization.

Source: Friendly, M. (2008). A brief history of data visualization. In Handbook of data visualization (pp. 15-56). Springer Berlin Heidelberg. http://www.datavis.ca/papers/hbook.pdf

Example of urban mapping research project

Title: Mapping social diversity
Phase one involved demographic mapping which would inform the survey in phase two, and then in-depth interviews as part of Project B. Using census data (UK 2001, Poland 2002), the residential distribution of people in terms of demographic characteristics was mapped in two cities: Leeds (UK) and Warsaw (Poland). Variables were selected to represent the key social dimensions of difference: demographic, socio-economic, ethnic and disability. A standard cluster analysis using a k-means algorithm was implemented for each city separately -for ‘Community Areas’ in Leeds and ‘Urban Regions’ in Warsaw.Graph 1. Cluster classification of Community Areas in LeedsGraph 2. Cluster classification of Urban Regions in Warsaw

cluster maps

Typologies of communities (‘diversity clusters’) were produced using the census data. These clusters varied in terms of wider diversity patterns, but that were internally homogenous. So the aim of the analysis was to reduce the internal variability while increasing the external variability between the types of communities. The mapping exercise has shown that patterns of residential segregation and mix in the two cities are different. Consequently, in different neighbourhoods there exist different opportunities to have contact with people who are different in terms of age, ethnicity, religion/belief, disability and socio-economic status.A comprehensive description of the mapping excercise and more details on the clusters are available in this working paper: click here Survey on attitudes, prejudice and discrimination In phase two we used the diversity clusters to produce a stratified survey sample. A large scale survey was completed by a professional surveying company. The total sample size for the survey was approximately 1500 interviews in each city (3000 in total). The survey examined (a) whether spatial proximity generates ‘meaningful contact’ among diverse social groups, (b) whether it generates respect and understanding regarding people who are different, and (c) which places of encounter constitute sites that can facilitate improved forms of intergroup relations.We intend to explain the variation in attitudes revealed in the survey using both individual attributes and the independent influence of living in particular diversity clusters. The results of the survey will be reported later in 2012/2013.

 In phase two we used the diversity clusters to produce a stratified survey sample. A large scale survey was completed by a professional surveying company. The total sample size for the survey was approximately 1500 interviews in each city (3000 in total). The survey examined (a) whether spatial proximity generates ‘meaningful contact’ among diverse social groups, (b) whether it generates respect and understanding regarding people who are different, and (c) which places of encounter constitute sites that can facilitate improved forms of intergroup relations.

We intend to explain the variation in attitudes revealed in the survey using both individual attributes and the independent influence of living in particular diversity clusters. The results of the survey will be reported later in 2012/2013.

Source: http://livedifference.group.shef.ac.uk/?page_id=105

A Brief History of Data Visualization. Michael Friendly (2006)

It is common to think of statistical graphics and data visualization as relatively modern developments in statistics. In fact, the graphic representation of quantitative information has deep roots. These roots reach into the histories of the earliest map-making and visual depiction, and later into thematic cartography, statistics and statistical graphics, medicine, and other fields. Along the way, developments in technologies (printing, reproduction) mathematical theory and practice, and empirical observation and recording, enabled the wider use of graphics and new advances in form and content.

This chapter provides an overview of the intellectual history of data visualization from medieval to modern times, describing and illustrating some significant advances along the way. It is based on a project, called the Milestones Project, to collect, catalog and document way. It is based on a project, called the Milestones Project, to collect, catalog and document methods to analyze and understand this history, that I discuss under the rubric of “statistical historiography.

Source: Friendly, M. (2008). A brief history of data visualization. In Handbook of data visualization (pp. 15-56). Springer Berlin Heidelberg. http://www.datavis.ca/papers/hbook.pdf

“Cartografía Sociolóxica”

Ao longo desta semana estiven asistindo a un curso de Sistemas de Información Xeográfica (SIX) organizado polo Colexio de Xeógrafos no campus sur de Santiago de Compostela.

Para o que non estea moi posto ao día, en dúas palabras, trátase dun sistema informático que permite plasmar información en mapas dixitais.

A pesares de que o seu uso está máis estendido entre as Ciencias Naturais, pode resultar de gran interese para disciplinas como a Socioloxía, ou as Ciencias Sociais en xeral. En moitas investigacións sociais ou de mercado, a análise comparativa territorial resulta determinante para facermos unha interpretación da realidade obxecto de estudo. A posibilidade de representar información en mapas facilita sen dúbida esta labor. En ocasións, e para que nos entendamos, “un mapa di máis que mil táboas”.

O máis avanzado software para SIX é o Arcgis. O manexo deste tipo de ferramentas está fortemente condicionado pola dispoñibilidade de cartografía. Porén, cada vez son máis as institucións que non só elaboran a súa propia cartografía, senón que esta está dispoñible en internet. Convídovos a visitar a páxina do IDEE, onde atoparedes gran parte da cartografía existente.

Por outra banda, nos últimos anos proliferou algún que outro software libre que non ten nada que envexar aos comerciais, como é o caso do GVgis, deseñado pola Comunidade Autónoma de Valencia (pódese descargar moi facilmente)

O Arcgis foi o software escollido polo mestre para impartir o curso. Ei de recoñecer que tiven algunhas dificultades para comprender algúns termos xeográficos, pero isto non foi un impedimento para adquirir unha serie de conceptos chave para o seu manexo.

Así, para facer un simple mapa de poboación por concellos precisamos, por unha banda, conseguir un mapa de Galicia en formato shape, que podedes descargar gratuitamente na páxina do SITGA. Isto é, basicamente, a plantilla sobre a que volcar posteriormente os datos. Por outra banda, precisades recoller información sobre a poboación de cada concello, por exemplo, no Padrón de habitantes publicado no IGE. Posteriormente, teredes que vincular os códigos municipais cos códigos que o arquivo shape lle ten asignado a cada concello.

Podedes conectarvos a algún servidor externo por vía WMS para cargar mapas temáticos sobre a plantilla shape. Buscade en “San Google” WMS Galicia. É probable que vos leve ao IDEE, onde atoparedes unha dirección web. Pois ben, esa dirección é a que vos pedirá o software para cargar toda a información.
A verdade e que se poden facer virguerías, pero aconséllevos que leades o manual do programa gis que utilicedes, ou mellor aínda, que faledes con alguén que xa coñeza este tipo de ferramentas, pois será de gran utilidade para aprender catro cousas básicas e aforrar tempo.