Introduction to Spatial Exploration of Economic Data
In the introductory chapter, firstly, the summary of how this chapter evolved is provided as well as the organisation of the whole Part III of this book is described. Then, the main body serves as an overview of how the spatial exploration of economic data and respective methods of interdisciplinary analytics can be approached. Based on the authors experiences up to this date, five levels or stages of an analytical approach to spatially analyse economic data are defined. At the lowest level, the author focuses on a “simple” visualisation of data, following by the level of merging (multivariate) statistics of economic data with their spatial component. In the middle, as a third level, spatial statistics – as an implicit use of statistics in the spatial analysis – is mentioned. As the fourth stage, a workflow of the previous ones is depicted, which leads to the final and most advanced level of spatial-economic modelling. This chapter strives to define a universal workflow in economic data analysis. The conceptual framework introduced in this chapter is based on 3 years of interdisciplinary cooperation within the Spationomy project.
KeywordsGeovisual analysis Spatial statistics Exploratory analysis Spatial modelling
This chapter has been formerly work-titled as a “methods of interdisciplinary analytics”, which turned out to be a rather ambitious plan as it might take a whole book to write about methods. In this book, we talk about a fusion of several distinct fields – geoinformatics/geomatics, geography, spatial analysis, geovisual tools and (geo)visualisation on one side, on the other, we refer to economy, business, business informatics, economic data and quantitative methods to work with them, and also about a management. In the Spationomy project, to simplify the mixture of disciplines, we call the former disciplines and people (staff and students) simply as “geo” part; keeping the same logic, the latter (disciplines and people) had a label as “eco”. As the “eco” label may be confused with “ecology”, we lately re-branded the label to “business” part. Anyway, each one above mentioned has a broad theoretical framing, old concepts, methodologies, and contemporary issues to deal with. It will be almost impossible to capture every aspect of these disciplines in on coherent text, and it was not intended at all. However, in previous parts of the book (Parts I and II), we provide a comprehensive overview of the subjects’ bases. The Parts I and II are meant to be an optimal start for those interested in the spatial economic topics.
Part III is dedicated to examples and case studies on how the “geo” and “business” part can be used jointly. Following chapters illustrate a few application of how common knowledge from geomatics, geography, economy and business informatics could be used in practice and research. Firstly, in Chap. 12, an interesting fusion of geospatial tools (mainly for presenting data) with purely business and managerial needs in a water management company is described in detail. This fusion is an example of how both parts (“geo” and “economic”) can be utilised in real-life situations. On the other hand, the case study in Chap. 13, shows an artificial site selection of a fictitious furniture store. This example is a typical process task for a new branch, store, or any facility allocation. In Chap. 14, the issue of demographical development was merged with spatial planning in cities. It represents a standard research paper approach to study given phenomenon, with a unique combination of the main, very actual topic (population ageing), and its spatial pattern in the studied region. Lastly, in Chap. 15, another example of a scientific study is provided. The study aims to capture the spatial implications of a European CO2 emission trading system over the ten years. Cartographic methods and geovisual analysis are used to (spatially) explore basic environmental and economic data referring to pollution allowances market. The set of overviewing introduction and four different case studies demonstrates how methods of interdisciplinary analytics can be deployed in both real-life situation and research. It brings new knowledge and opens possibilities for novel approaches in the new joint field of the “spationomy”.
Now let’s get back to the “methods of interdisciplinary analytics” or “(spatial) exploration of economic data”. During the 3 years of the Spationomy project, we learnt how beneficial it is to combines ideas, methods, approaches, and topics among (and from) already mentioned disciplines. From various experiences – learning and teaching material creation, joint scientific paper preparations, brainstorming and discussions – that we had a chance to have in the Spationomy team, and also from the interaction with other stakeholders, we feel the need to propose an optimal workflow of the spatial and economic data analytics. It is also a practice of the author of this chapter to advice students to follow the next five steps towards a successful bachelor or master thesis. In the following pages, a way to approach the data analysis is presented from the simple to more advanced and complex application of spatial and statistical methods. Introduced five levels reflect the authors’ experiences that were proven in practice during the project. However, it is not a dogma that could not be changed, modified or adjusted to the reader’s needs.
11.1.1 Level 1 – (Geo)Visual Analysis
The geovisual analysis represents the first step in the exploration of the data (spatial, economic, business and any other types). The general objective of data visualisation is to transform textual or numerical information into the form of its graphical representation. Whether it is the picture, scheme, chart, graph, workflow, infographics, map, interactive application, 3D graphics or something else, it focuses on a transfer of information to the reader. The visualisation also serves as a tool for data exploration. We can perform simple (and effective at the same time) exploratory analysis, e.g. to find extreme values, outliers, in a data. By depicting a boxplot, scatterplot, or just linear chart (see Part II, Chap. 8), we can immediately see such outliers, which could be hardly detected when “looking” at the numbers. Indeed, the experienced data analyst can find out anomalies in a raw dataset, or when we have a small data sample, it is easy to capture outliers. However, in the case of big data or other highly heterogeneous data, the hidden pattern could be revealed with considerable difficulties, if at all. With the use of visualisation techniques, we can analyse such messy data, we can describe data patterns inside the dataset, uncover and show extreme values, find relationships in data and compare them, and most importantly to communicate the information much more clearly.
11.1.2 Level 2 – Statistics, Exploratory Data Analysis and Its (Geo)Visualisation
11.1.3 Level 3 – Spatial Statistics, Exploratory Spatial Data Analysis and Its (Geo)Visualisation
11.1.4 Level 4 – A Combination of Analytical Methods (Level 2 and 3)
11.1.5 Level 5 – (Spatial) Modelling
This chapter addressed an ideal workflow of “methods of interdisciplinary analytics”, but we need to note that it is delivered from the author’s practical experiences. Nevertheless, when analysing data, it is advised to proceed from the simplest to more advanced procedures. That is why, the first step to understand a data is to use proposed level one techniques, i.e. simple (geo)visualisation. Then, level two and three can be applied if we aim to explore specific characteristics of data or to conduct comprehensive statistical or spatial analysis. Ideally, after this stage, a combination of both should follow – again, with respect to the research goals. Finally, a modelling phase can be the concluding step in the whole workflow. As mentioned in a previous text, to follow such workflow, it usually requires cooperation among several experts. Therefore, as synopsis to the very first sentence in this introductory chapter – “methods of interdisciplinary analytics” for (spatial) data exploration is indeed a good label. In the next chapter of this part of the book, one synthetic/artificial and four real-life examples are presented.
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