| Chapter 1. Overview | ||
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| Chapter 1. Overview | ||
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As an introduction, this chapter proposes an overview of a typical Standard HyperAtlas v2 session, describing possible paths of investigation.
Users of the Standard HyperAtlas v1 may remember the typical path of investigation, they were supposed to follow the seven following steps:
Choice of area, zoning and indicator of interest (that's to say a ratio)
Visualization of the ratio and (eventually) visualization of numerator and denominator without transformation
Analysis of inequalities at large level
Analysis of inequalities at medium level
Analysis of inequalities at local level
Synthesis of inequalities at large, medium and local level
Export of results towards a report
Of course, users are free to develop their own paths of investigation, and we can imagine different types of scenarios where users do not follow steps 1 to 7, but they adopt different strategies.
Let's now consider the following examples to demonstrate the benefits of a Multiscalar Territorial Analysis approach thanks to Standard HyperAtlas:
Example 1
A stakeholder interested in the reform of structural funds after 2013 will probably use a path of investigation following the type (1)=>(3)=>(7) that will be repeated many times in order to test various scenario of allocation of funds. For example, what happens if:
NUTS2 is replaced by NUTS3?
GDP pps is replaced by GDP in Euro?
the threshold of 75% of EU mean is replaced by 80%?
Turkey joins EU?
etc.
Example 2
A local decision maker mainly interested in its region may use a path of investigation following the type (1)=>(6)=>(Save map), if the objective is to quickly extract three figures describing the situation of the regions at European, National and Local levels for a given criteria. He/she can then decide to click on other regions in order to benchmark its situation with neighbouring areas, or to identify other regions with the same strength and weaknesses. He/she can also decide to modify the indicator and to explore the strength of weaknesses of his/her region for various criteria, GDP/inh, unemployment, accessibility, ageing, etc.
Example 3
A spatial economist interested in economic convergence may decide to examine the situation of regions according to vertical contexts (e.g. belonging of region to a state, an INTERREG area) and horizontal contexts (e.g. difference between a region and its neighbours for different thresholds of contiguity or distance). He/she will therefore follow the expected steps (1) to (7), but he/she will probably introduce loops in the steps (4) and (5) in order to explore different variants of vertical and horizontal context. The loop (1)=>(5) will for example provide answer on question like the GDP/inh. Of course, the region of Budapest is greater than the neighbours for a distance of one hour by road, but what happens for a distance of two hours on a truck? Four hours? etc.
Having established that different users will not pay equal attention to the different functions offered by HyperAtlas, we can also suspect that expert users will expect more sophisticated functions than non-expert users, who will be on the contrary reluctant to enter into complex indicators or results.
Considering these different types of users, Standard HyperAtlas v2 provides an expert mode (see Standard HyperAtlas expert mode chapter), opened on request by the user (expert users or curious). In summary, the expert mode provides the following tools that complete the typical path of investigation:
Equi-repartition maps, one per context, for Large, Medium and Small (local) levels
Lorenz curve and statistical indexes (Gini index, Hoover index, coefficient of variation, ...)
Boxplots
Spatial autocorrelation chart
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| Introduction |  | Part I. Standard HyperCarte Web Application |