Spatial Economics - Science topic

La temática sobre Geografía de la población y Censos demográficos son fuentes del tema que has nombrado.

Thank you Hans-Georg Petersen and Alam Galicia Robles

The Krugman article from 1991 seems to be easier to understand.

The book of Capello and Nijkamp seems like a good start. Too bad it is expensive.

If you can help me, I have just one more question. From Krugman's article and Capello's book summary, it seems that NEG moves slightly away from the mainstream economy. Does this make it more challenging to publish on NEG? Or are there more NEG supporters today?

Regards

The differences between clusters and entrepreneurial ecosystems are well described in this article:

Spigel, B., & Harrison, R. (2018). Toward a process theory of entrepreneurial ecosystems. Strategic Entrepreneurship Journal, 12(1), 151-168.

Unfortunately there are a lot of issues with using an index approach (like Gini) to analysing fine-grained spatial data :

" Methodologically, the finer the spatial scale and the fewer the number of people living in an area, the more likely are chance results. Many traditional segregation indices assume that there are no stochastic variations and provide upwardly-biased estimates with apparent segregation when there is none (Carrington and Troske,1997). It is thus necessary to adopt inferential modelling to assess segregation net of stochastic variation as pioneered by Leckie et al. (2012). Segregation is characterised as an estimated variance around a mean. If all places have the same share of a subgroup the variance estimate will be zero; any apparent differences are merely due to chance. However, an estimated variance whose uncertainty intervals do not include zero indicates genuine segregation in the form of unevenness. Importantly they show that several well-known indices [INCLUDING THE GINI] are an increasing monotonic function of the variance. Consequently, the same conclusions will result whichever are used to compare different groups or groups over time. They also provide a method of transforming the variance and accompanying uncertainty into these indices if that helps comparison.

Mobility research finds that movers choose (or are forced to choose) a broad zone of a city and then decide at a micro scale where to locate within that zone (Johnston, Forrest et al. 2016). There can be multiple processes operating at multiple scales and a group can be concentrated in zones and within certain subareas within zones. Methodologically, the scale at which segregation analysis is conducted is critical and can determine the outcomes found. Previous research has resulted in the stylised fact that the most marked segregation is at the finest scale and declines at higher spatial scales. However, there has been much misunderstanding about analysis at multiple scales. Typically (e.g. Lee et al. 2008) the analysis moves through a sequence of ever-growing scales finding the maximum segregation at the finest scale. These analyses are problematic as the finest scale implicitly includes segregation at any relevant higher scale (Duncan et al., 1961) and the apparent decline is likely to be an artefact of the aggregation process whereby data are smoothed as they are combined into larger units.

Fortunately, the modelling approach can be extended to analyse multiple scales simultaneously where unevenness is characterised by the variance at one scale net of the variation at other scales (and net of stochastic variation). Several studies using this approach have found that segregation is greater at the macro scale, as for example for London (Johnston, Jones et al., 2016; Jones et al. 2015).

Processes of structured segregation are likely to produce not just unevenness of distribution but also clustering whereby similar groups are spatially concentrated in certain parts of the city and separated from dissimilar groups. Methodologically, however, most segregation analyses are inherently aspatial merely analysing the frequency distribution of minority rates and ignoring location. What is needed is an assessment of the degree of spatial dependence of the rates – the degree to which similar rates are adjacent to each other. The developing modelling approach in partitioning the variance at different levels implicitly models the dependency at each scale below the topmost one (Jones et al. 2015); larger variances indicate both greater differences between and equivalently greater similarity within areas (Bullen et al., 1997). But this crude approach has no explicit parameter for the degree of spatial dependence; the variance measures only unevenness and there is a need for a modelling approach that additionally assesses spatial clustering as well as aspatial unevenness. Initially, the modelling approach was applied to educational segregation which analyses data for preexisting higher-level level entities (at its simplest, children in schools). For residential segregation, this is not the case and the areas of interest must be defined, thereby confronting the MAUP There are two aspects to this: the scale problem is that results may depend on the number of units studied; while the zonation problem is that different results can be found for a constant number of geographical units but with differing spatial arrangements.

To date the model-based approach has taken a strictly hierarchical approach where the scales and zoning are taken as given and fixed. Thus, a model-based study of segregation in London (Johnston, Jones et al 2016) used census Output Areas at the finest scale which are exactly nested in Middle Layer Super Output Areas which are in turn nested in Boroughs. It may be that this specific mesh is determining the results so we need to research the MAUP to gauge potential sensitivity of results to different spatial architectures. This problem is closely bound up with spatial dependence for if the rates are truly without map pattern then differential aggregations and zonings are unlikely to determine the results (Wong 2009, 114). Views on MAUP range from an ‘essentially unpredictable’ intractable problem (Fotheringham and Wong, 1991,1025) to a ‘very powerful analytical device’ (Openshaw, 1984,7). Another striking feature is that much research simply concludes that results differ and gives little insight as to why. There are several reasons for this. Some studies use real data and then create zones from these – the million correlation coefficients of Openshaw and Taylor (1979) are based on re-grouping areal voting data for Iowa. It is then difficult to know what the ‘true’ relationship is and to isolate what is producing the results. Other studies use simulated data of known properties but are unrealistic in not considering different forms of spatial configuration in determining the results (Amrhein, 1995). A more pervasive problem is that studies have not been set in a general modelling framework (Wrigley, 1995) resulting in no goodness-of-fit measure for determining what is the ‘best’ zonal arrangement. Here we see the MAUP as an opportunity to vary scale and zonation to bring the patterns into focus and thereby gain insight to the underlying processes producing segregation. However, we lack a statistic to provide guidance when this focus has been achieved. This is especially difficult in real data as there may be multiple and unknown configurations where spatial processes are operating; working at a single scale may miss what is going on.

Three other issues are methodologically important for us. Firstly, in assessing change there is a need to incorporate statistical uncertainty. A decreasing population per unit area will result in smaller numbers which will emphasize stochastic variation upwardly biasing segregation estimates. Such analysis of change must be multiscalar as countervailing tendencies (polarization at one scale alongside dispersal at another) may cloud the picture of what is happening (Haggett, 1965). Secondly, ethnicity in the 21st city is not a binary variable, with multiple ethnicities characterising contemporary society. This diversity requires simultaneous modelling of multiple groups which results in smaller numbers as ethnicity is disaggregated, making a modelling approach particularly valuable. The third aspect is that there may be multiple influences on location decision-making so that residential segregation may be differently driven by ethnicity and class. It is vital to have a methodology for assessing which are the key influences on segregation and to do so for multiple groups at multiple scales. We have addressed all three in previous work (Johnston, Jones et al., 2016; Jones et al. 2017) and here we concentrate on spatializing the modelling approach to measure clustering while examining sensitivity to the MAUP for a single time cross-section for just two ethnic groups.

Our agenda should now be clear. A modelling approach is required that can handle and separate stochastic variation from ‘true’ pattern. It must work at multiple scales simultaneously distinguishing unevenness and spatial clustering. It should allow sensitivity analysis to assess which alternative spatial arrangements bring the patterns into focus at one scale net of others. This needs to be set in an inferential framework where the uncertainty of parameters is evaluated and where appropriate measures of goodness of fit allow the preferment of some spatial arrangement over others. The method of comparison must consider model complexity in coming to a parsimonious judgement on models with many potential parameters. The method must also be computationally feasible on realistically-sized data sets. "

PROC MIXED handles spatial correlations built into the error term. For an example, see

Dear Bin, it is highly impressive what can be done with such data. Admittedly my attempt to consider such satellite images has been so far a more practical one, as to test or confirm results from empirical social and economic research on rural and urban evolution, such as e.g. peri-urbanisation. I am also aware of the fractal dimension of space, such as the rank-size distribution of cities or developmental strengths and weaknesses. Implicitly this can be even found in policies, such as the cascade-like logic of the Europe2020 strategy, where the EU as such and every tiny sub-region is defined by the same developmental categories and challenges. The EU is just the aggregate of the variation at national down to local levels. Indeed, to my knowledge there is not much consideration of spatial heterogeneity in regional science. Some few remarks can be found in Brakman et al. 2009 (The new introduction to geographical economics, Cambridge: CUP). I do not have ArcGIS and no clear idea of its certainly powerful capacities. The original inspiration to use ImageJ came from my daughter who just works on her master thesis in biochemistry and the information on http://www.noao.edu/education/gsmt/lpmeasure. In the moment we are preparing a research application with other EU partners on rural & urban change. For this purpose I am preparing a technical paper on the image analysis approach. Your work will be duly considered with references. Once completed I'll let you know. Many thanks and best wishes, Rolf

I am of the opinion that a major proble faced is the issue of community participation I  maintenence of the park. Given the overall cost of maintaining the park there are basic responsibilities of the users, when this is not met the park can become a danger zone. The competition for funds for other sectors in urban development can affect park management. If communities participate in maintenance this problem can be greatly addressed, however the park must be located within their community, that is within walking distance.

Dear Petr,

I'm affraid you are going to have to read papers in French, as it is in this specific linguistic context that the notion has been most studied. In addition to the work Olivier made and to the one by Davezie, I also use in my teaching papers by Eric Ambiaud (et al.):

and G. Dore:

Best regards

d.

Dear Bernard,

Thank you for this interesting answer and for suggesting to broaden the scope of the literature I should examine  (and sorry for this late reply). More particularly, thanks for highligthing the notion of "R&D driven" and the connections with IT literature. It might be useful in my case.

Best regards

Josselin

You study requires model re-specification procedure until the problem on identification of specific variables is resolved.

Dear Sidney,

I would like to recommend my recent paper on tourism and simulacrum, where I discuss the "nudge perspective" to behavioral consistency that is needed for destination resilience (in tourism). The "behavioral turn" is embedded in the gamification perspective to destination development

Best Regards,

Bernard

As Benjamin Casper very relevant indications focus on some detailed description of the form and of the networks, the discussion is going a bit away from your initial question more focussing on the external envelop of the urban form.

I would simply add to the discussion at this stage the idea of fractals that are a way to describe some rather complex forms as large cities are. As I understand your interest is on geometry, this is worth considering, and also fractals come with measurements and comparison between forms, so they can indicate some elements for discussing optimality.

In France we have a specialist of these issues, Pierre Frankhauser, I attach one of his numerous papers.

I recommend Luc Anselin's workbook: http://sal.uiuc.edu/

If your institution subscribes to Springer textbooks, then take a look at Roger Bivand's Applied Spatial Data Analysis with R.