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.
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.
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.
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.
Building a personal research and teaching library – a few thoughts inspired by Dave Beer, The case of bookcases. Thank you to Dave both for this post and encouraging me to say something about my books.
At home, I’m fortunate to have a large room as a study. This is the main writing collection, with all the books by Foucault, Heidegger, Lefebvre and other thinkers whose work I want to have easily accessible. I have most in original language and translation. I also have a lot of secondary literature on each of them, and, especially with Foucault, a lot of related texts – documents, bibliographies, pamphlets, etc. I also have nearly all my history of political thought and philosophy books, pre-20th century, at home – loads of books by Plato, Aristotle, Descartes, Leibniz, Kant, Hegel, Marx, Nietzsche and many others. I also have books by Kostas Axelos, the complete…
The introduction of coal mining in the 1940’s transformed the landscape and economy of As Pontes, Spain. Industrialisation created successive waves of economic and population booms, but when the mining slowed in the 1990s, the region experienced economic depression. Real and perceived social divisions and environmental abuses on the part of the mining company remained entrenched in people’s memories. This paper provides an overview of the factors that likely affected community acceptance of the new pit lake in As Pontes, Spain. Pit lakes are often attractive closure options for companies, and community opinion of pit lakes can influence pit end use. Community perceptions of the pit lake before, during, and after filling were assessed using case studies, interviews, and focus groups, and by tracking news events and analysing internet forums. The results broadly indicated high community acceptance of the pit lake by people residing in the town. However, interviews revealed that acceptance of the pit lake was influenced by previous experiences with the mining company; company employees and local politicians were more likely to be positive about the benefits of the lake, whereas those not directly affiliated with the lake (long-term residents, remote villagers, school teachers) were more likely to have a negative view of it. Thus, technical success is not the only factor that influences community acceptance of pit lakes and company closure plans. Unresolved social issues can also influence the way certain people perceive the new landscape, regardless of ecological and aesthetic impacts.
The unforeseen coincidence between a general confinement and the period of Lent is still quite welcome for those who have been asked, out of solidarity, to do nothing and to remain at a distance from the battle front. This obligatory fast, this secular and republican Ramadan can be a good opportunity for them to reflect on what is important and what is derisory. . . . It is as though the intervention of the virus could serve as a dress rehearsal for the next crisis, the one in which the reorientation of living conditions is going to be posed as a challenge to all of us, as will all the details of daily existence that we will have to learn to sort out carefully. I am advancing the hypothesis, as have many others, that the health crisis prepares, induces, incites us to prepare for climate change. This hypothesis…
Landscape value corresponds to an attachment or emotional bond that people develop with places. There are strong cultural ties to landscapes and feelings for the visual beauty of mountains, lakes, coasts, forests, etc., which are a common bond among people or social groups of a given region. Arguments related to landscape values are commonly heard in Europe from opponents to the construction of wind farms for example. Landscape values may also be important for the tourism industry and landscapes can therefore be managed as a key component of tourism infrastructure.
Landscape value often has an association with environmental and natural resource values. The values that people appreciate in a landscape may often also be important ecologically. Landscape values can be divided into use and non-value, the former of which provides tangible benefits (such as economic value through, for instance, tourism, or recreation value) and the latter of which provides spiritual, identity or ecological value.
For further reading
Penning-Rowsell, E. C. (1981) Fluctuating fortunes in gauging landscape value. Progress in human geography, 5(1), 25-41.
Zografos, C., & Mart, J. (2009). The politics of landscape value: a case study of wind farm conflict in rural Catalonia. Environment and Planning A, 41(7), 1726-1744.
This glossary entry is based on contributions by Julien Francois Gerber
EJOLT glossary editors:Hali Healy, Sylvia Lorek and Beatriz Rodríguez-Labajos
O problema de partida: apatia política nas democracias contemporâneas Nos últimos anos formou-se um consenso surpreendente entre muitos autores sobre a crise do sistema democrático. A surpresa deriva do fato de que, depois da Queda do Muro de Berlim, a democracia ocidental parecia triunfar definitiva e incontrastavelmente. De fato, havia tempo que alguns teóricos já tinham alertado para problemas irresolvidos e dilemas que caracterizam nossas sociedades democráticas. Já na década de 1970, Jürgen Habermas e Claus Offe tinham chamado atenção para os desafios que o Estado democrático de bem-estar social tinha que enfrentar na Europa (Habermas,  1980; Offe,  1984). Com o desenvolvimento da economia capitalista e o multiplicar-se das crises econômicas e financeiras, provocadas – na leitura marxista desses autores – pela própria lógica do sistema capitalista, o Estado se viu na obrigação de encontrar remédios para os efeitos negativos de tais crises e para obviar às correspondentes crises de legitimação que ameaçavam o sistema econômico e político. Um dos instrumentos utilizados para esse fim foi a adoção de políticas de segurança social, que foram aprofundando-se e transformando-se em políticas de bem-estar social. Ora, apesar de considerar esse processo em geral de maneira positiva, Habermas em várias obras alerta para um efeito negativo: o cidadão tende a transformar-se em cliente, renunciando à participação ativa e assumindo a atitude passiva de quem se limita a aguardar serviços do Estado (Habermas, 1973, pp. 9 e ss., 2012, pp. 626 e ss.).
Mais ou menos na mesma época, Niklas Luhmann, ao discutir a noção de “democratização da política”, afirmava que as sociedades contemporâneas são tão complexas que as “teorias clássicas da democracia” parecem ultrapassadas e incapazes de entender adequadamente a realidade política (Luhmann,  1983, p. 153). A ideia de uma vontade popular é inspirada por uma analogia com os indivíduos, mas não se deixa aplicar a sistemas altamente complexos. Essa complexidade faz com que “o nível de informação do público” seja “extremamente baixo”. Até em casos que dizem respeito ao interesse pessoal dos cidadãos, como no “do direito tributário, ou daqueles relativos aos seguros e às pensões”, é improvável que o indivíduo conheça as leis em questão. Longe de considerar isso lamentável, Luhmann pensa que “ignorância e apatia são as condições mais importantes para uma mudança das leis, que segue passando despercebida, e para a variabilidade do direito e, portanto, são funcionais para o sistema” (Luhmann,  1983, p. 191).