Socio-spatial secondary data source for all over the world

In this post I want to echo this fantastic source of socio-spatial data, perfect for your GIS analysis in most of the countries all over the world

The files have been created from OpenStreetMap data
and are licensed under the Open Database 1.0 License. See
www.openstreetmap.org for details about the project.

This file contains data as of 2020-10-12T20:42:02Z and every day
a new version of this file is being made available at:

http://download.geofabrik.de/

A documentation of the layers is available here:

http://download.geofabrik.de/osm-data-in-gis-formats-free.pdf

Geofabrik also makes extended shapefiles to order; please see
http://www.geofabrik.de/data/shapefiles.html for details and example
downloads.

Socio-economic analysis with nighttime light series. Step 1: downloading shapefile of the study area

Context: we analyze economic changes in Colombian municipalities between 1993 and 2020.

We need to download ArcGis, a geographic information system (GIS) for working with maps and geographic information maintained by the Environmental Systems Research Institute (Esri). It is used for creating and using maps, compiling geographic data, analyzing mapped information, sharing and discovering geographic information, using maps and geographic information in a range of applications, and managing geographic information in a database. Particularly important for the purpose of this post is ArcMap, one of the applications, which is primarily used to view, edit, create, and analyze geospatial data. In the absence of license for this software, you can use free software such as GVSig.

Once installed either of the GIS software you use, it is time to upload the cartography of your study area. Normally, that cartography is in a geospatial vector data format called shapefile. VDS Technologies provides shapefile of administrative division across the globe. We download the administrative division of Colombia. The file includes 23 documents, including the dataset of data and the proper geospatial vector data (.shp). COL_adm0 corresponds to the boundaries of the whole country, COL_adm1 the department and COL_adm2 the municipalities, our basic analysis unit.

In ArcMap, we “add data”, look in our folder and select COL_adm2

The boundaries of the municipalities will show up in the visor Clicking in Windows, Table of Content, another window shows up in the left side with a list of the layers you can visualize.

Clicking right bottom above COL_adm2 layer, you can see the table properly, with all the values associated to each of the municipalities showed in the visor. This table of content often contains demographic and other data and we can do some basic analysis. For instance, visualize population density using a scale of colors where the intensity works as an indicator of the number of inhabitants in that particular region.

Now that we have the cartographic basis for our analysis, next post is about “downloading nighttime images”, that is the “raw data” we are considering to analyze economic performance in each of the Colombian municipalities between 1993 and 2020, something that, using more official statistics would be more difficult.

Socio-economic analysis with nighttime light series: downloading nighttime images

Context: we analyze economic changes in Colombian municipalities between 1993 and 2020.

In this post, we focus on how to download our “raw data”, i.e. the nighttime light data for Colombia.

Step 1: Download nighttime light series from NOAA National Centers for Environmental Information (NCEI). Particularly Version 4 DMSP-OLS Nighttime Lights Time Series (DMSP) – The DMSP annual composite data contain average radiance values of cloud-free coverages, reflecting the persistent lights from cities, villages, and roads, with a spatial resolution of about 900m, and a temporal coverage of 1992 to 2013 – and VIIRS data which is available from 2013 on and with is a finer spatial resolution of 450m approximately. We will later do a post on the particularities of VIIRS, here we focus on DMSP.

Once we are in NOAA website, we will see two different tables. We use “Average Visible, Stable Lights, & Cloud Free Coverages” table. Why? because clouds is a major constraint in night light analysis and this table contains cloud free images.

We click on every year and wait until the file is downloaded. We save all the files in a folder “Nightlight Analysis” and within that folder, we create one new sub-folder for each of the years.

Each of the year contains eight files compressed in a format .tar

Once we place each of the .tar files in their respective year folder, we extract the file and obtain the following files. The most important for our analysis is F162008.v4b_web.stable_lights.avg_vis.tif At the end of this exercise you should have as many avg_vis.tif as years you want to analyse. Then you will be ready to perform the next step: uploading nighttime light file to a GIS software, and convert them from raster to points before we build our dataset.

Socio-economic analysis with nighttime light series: uploading nighttime light file to a GIS software

Context: we analyze economic changes in Colombian municipalities between 1993 and 2020.

After having downloaded nighttime images from NOAA National Centers for Environmental Information (NCEI), and install ArcGIS or any other GIS software in our computer, we proceed now to upload nighttime files for our analysis.

NOAA provide images covering the whole world which can be difficult to manage in terms of size. For that reason, we will only upload the images corresponding to our study are, i.e. Colombia. To do that, make sure you have uploaded first the map of the study are, here Colombia (see previous post)

In ArcMap, we go to Windows—Search and one window with search engine will show up in the right side.

In order to extract only the images corresponding to Colombia, we search the tool “extract by mask”. There you will find further details on this tool. Extracting all the nighttime light files we should have stored previously in our folder is the first step to build our dataset of light intensity, i.e. our economic indicator.

Once we click on “extract by Mask” this window will appear asking for input raster, which is the night light file stored somewhere in your computer, in my case, in the folder I stored all the years “nightime light time series DMSP-OLS”. Then, input raster or feature mask data. This is the “mask” properly, and the way to say the software you only want one part of the whole image, in this case, the boundaries of Colombia, so we choose “COL_adm2”, the shapefile we previous open in ArcMap. Finally, in output raster, this is the file that will store all the data from the nighttime light series, the file that will contain our dataset for our analysis.

After clicking OK, we will be able to visualize Colombia image at night

We will extract as many files as years we want to analyse. In our case, we uploaded from 1993 to 2013, which as visible in the “table of content window”.

But before making that dataset look like a classic dataset we use in Spss or similar statistical softwares, we need to convert all the nighttime light files into a different format. These files are in format raster. In computer graphics and digital photography, a raster graphics or bitmap image is a dot matrix data structure that represents a generally rectangular grid of pixels, viewable via a bitmapped display, paper, or other display medium.

Importantly, NOAA assign a value from 0 to 63 to each of the pixels, where 63 means the maximum light intensity. However, in practice, we cannot see the value of each of the pixels we need for our analysis, so that we need to convert this raster into “Points”, meaning that every single pixel you see in the above image will be converted in a point with a given value, this time associated to a table of content.

We go again to window—-search and look for “raster to point”. We have two options, selecting “raster to point” and convert all the raster one by one into points or simply “extract value to points”, which will allow us to extract all the values from all the raster and then convert them into points.

Option 1

Option 2

Following the option 2, we select the file where we previously created “dataset” (in the image basedatosrecurse) and add all the rasters by clicking +. We click ok and wait until the program is able to perform the transformation.

The software will now show a new layer in the Table of content window “basedatosrecurse” (or dataset as labelled above) Clicking right bottom with the cursor on this layer, we “open attribute table” and see all the data with a classic dataset used in Spss. Every record correspond with one tiny point in the map (former pixel in raster format) and have assign a value from 0-63 for each of the years, where 63 indicates greater economic growth. You can see the dataset I created for Colombia here in my ResearchGate profile https://www.researchgate.net/publication/344282484_Shedding_light_on_the_local_resource_curse_in_Colombian_coal

We are almost ready to perform our advance economic analysis. Before, we need to link each of the points with the municipalities. See our next posts.

Socio-spatial differentiation in southern Leipzig post-mining area

Bellow it is showed the three types of communities (Kabisch, 2004) adjacent to the coal mine, now pit lakes, in the southern leizig.

Rural villages: Dreiskau-Muckern (469 inh. in 2014), Oelzchau (610 inh. in 2014), Pötzschau (374 inh. in 2014), Mölbis (515 inh. in 2014), Störmthal (512), Auenhain , Wachau, Güldengossa (394 inh. in 2014): High satisfaction with the housing conditions regarding the apartment, very high satisfaction with the local living conditions (share of respondents who would recommend a good friend to move to their community), quiet location, attractive surroundings, pleasant social atmosphere, close to future recreation areas. (TOTAL POPULATION. APPROX: 4.000 inhabitants)

These communities are characterised by a relatively large proportion of farmers and farming employees, the other inhabitants working in the mining industry. The level of qualification is relatively low, while the average age is relatively high. Others: out-migration of younger, well-educated inhabitants during the last few decades. The main burden affecting these communities was their classification as “mining protection areas”. Although this classification was abolished after 1990, the local population suddently had to face new worries such as unemployment and early retirement.

Suburbs: Markkleeberg-Ost, Grossstädteln: hight satisfaction with the local housing conditions regarding the apartment, very high satisfaction with the local living conditions (share of respondents who would recommend a good friend to move to their community), quiet location, close to the city of Leipzig, varied infraestructures, good transport links, close to future recreation area.

Relatively high level of qualifications and higher household income among the inhabitants. Urban morphology: detached family housing. Most of the employees work in the city of Leipzig. Consequently, the collapse of the brown-coal industry did not affect these inhabitants to the same extent as the residents of the other community types. No out-migration tendency.

Small towns affected by industry: Gaschwitz (671 inh. in 2014), Grossdeuben, Rötha (3,704 inh. in 2014), Espenhain (2,267 inh. in 2014): Low satisfaction with housing conditions, very low satisfaction with the local living conditions (share of respondents who would recommend a good friend to move to their community), close to the city of Leipzig, good transport links, poor infraestructures, vehicle pollution, devastated landscape, buildings in bad state of repair. TOTAL POPULATION: APPROX: 7.000 inhabitants.

Most of the inhabitants worked in the former brown-coal industry. The majority of the residents rent flats in three-or four-storey blocks owned by the industrial enterprises. In this small towns, the collapse of the brown-coal industry led to social disaster, with unemployment suddenly mushrooming. High unemployment has persisted, despite the migration of sections of the population.

Reference

Kabisch, S. (2004). Revitalisation chances for communities in post-mining landscapes. Peckiana, 3, 87-99.

Polish-German Border Towns: Models of Transnationalism?

Border towns 1.JPG

Funded by the British Academy this project investigates the transformations of public space in interface areas of the German-Polish ‘twin towns’ of Frankfurt-Slubice, Guben-Gubin and Görlitz-Zgorzelec along the Oder-Neisse border. Despite modest populations, the border towns have major symbolic value for two nations attempting to write a new chapter in a modern history marked by war, trauma and deep resentments. The eastward expansion of the EU has propelled the towns from the margins to the heart of Europe. Cultural and socio-economic divisions nevertheless run deep. With the opening of the borders in 2007 changing physical realities are dramatically impacting possibilities of transnational interactions. This project offers the first comparative study of the border towns and specifically the role of spatial settings in cross-border exchange. This allows for a more contextual account of the relationship between social practice and place in German-Polish twin towns and sheds light on how communities use urban environments to cope with legacies of conflict and ongoing ethno-national difference.

Principal Investigator: Dr Maximilian Sternberg

Associated researcher: Lefkos Kyriacou

Photographer: Matthias Schumann (www.monofoto.de)

The academic positions I’ll apply for…some day

PROFESSOR (W2) OF SOCIOLOGY /URBAN AND REGIONAL STUDIES

The Ruhr-Universität Bochum – faculty of Social Sciences invites applications for the position of a Professor (W2) of Sociology /  (successor Prof. Strohmeier) starting the earliest possible date.

 The Chair represents urban and regional studies from a broad spectrum. Applicants are required to have their main emphasis in the following topics:
  • spatial distribution patterns of life situations and life styles within the context of urban and regional social change,
  • labour and housing markets in an urban, a regional and global context,
  • social urban integration problems (immigration, small scale segregation and local participation) in cities,
  • social and demographic changes in comparison.

The professorship is coupled with responsibilities as head of the Centre of Interdisciplinary Regional Research (Zentrum für interdisziplinäre Regional­forschung, ZEFIR). The future holder of the post has to fulfil teaching perfor­mance for the Bachelor programme in basic and advanced modules and in the Master programme, specifically in the “Urban and Regional Development” study programme. The applicant is expected to contribute in the faculty’s research clusters “Labour and Social Structure” and “Public Sector and State Action” as well as interdisciplinary cooperation with other teaching and research units of the faculty and the university.

Positive evaluation as a junior professor or equivalent academic achievement (e.g. habilitation) and evidence of special aptitude are just as much required as the willingness to participate in the self-governing bodies of the RUB and to generally get involved in university processes according to RUB’s mission statement. We expect further more:
  • high commitment in teaching,
  • readiness to participate in interdisciplinary academic work,
  • willingness and ability to attract external funding,
  • fostering of international connections of the subject in research and teaching.

The Ruhr-Universität Bochum is an equal opportunities employer.

Complete applications with the usual documents should be sent to the Dean of the Faculty of Social Sciences of the Ruhr-Universität Bochum, 44780 Bochum, Germany (e-mail: dekanat-sowi@rub.de) no later than 07.11.2014.

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

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