8.1.1 SI - Segregation Index

One of most important social transitions is the process of segregation. Segregation shows the spatial separation of different social strata in a given spatial area. Various indices have been developed to measure segregation that differ in complexity and explanatory power. Here a rather simple but widely used one suffices. Duncan and Duncan index of dissimilarity delineates the segregation of a neighbourhood or a district, in per cent. It shows the ratio of the population having to move to have an equal distribution. It calculates the segregation of a select stratum that is to be selected according to the nature of the study, that is, in this case, the upper strata. As educational level is the best proxy to social status, we suggest it as the variable and the university degree as the value to be assessed.

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Segregation index is related to the development of NBS in the following ways:

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* Inhabitants of lower social status can be characterized with poor life qualities, which also mean less or no access to good quality green spaces.

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* This also works the other way around. Areas where good quality green spaces are accessible in 5-10 minutes of walking distance are usually the areas with higher real-estate prices. This also means segregation.

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It is easily seen that the accessibility of green spaces and social segregation within the city are strongly interconnected.

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The social impact of NBS projects can be traced by the spatial or temporal alterations of the segregation index. Correlation can be proved between segregation and the development of green infrastructure, if the implementation of a large scale and complex NBS project has measurable social effect. (Irvine et al. 2013, Haffner 2015, Dooling 2009, Checker 2011)

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===== **Scale as linked to D2.2 framework** =====

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Delete as appropriate:

* National

* City

* Neighborhood

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====== **Data source [ link to data framework]** ======

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* Surveys

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* Data of Central Bureau for Statistics

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* Data of electoral districts (municipalities own them)

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* Growth of green areas [m2 ]

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====== **Data needs** ======

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* data which shows the status of social groups, like ratio of graduated people or income in each spatial unit within the whole city

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* when correlation analysis is carried out there are 2 ways to analyse connection segregation and NBS.

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** 1.) If spatial comparison is done as input data simply the existence of NBS (green areas, larger than 1000 m2 ) is taken into account.

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** 2.) If correlation carried out on a temporal basis the status and quality of NBS is taken into account.

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** Indicators such as accessibility can be used.

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====== **Expected Outputs** ======

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* Unit is %

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* At beginning, and 5 years after end, of an NBS project.

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* Mapping is possible.

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====== **Calculation methods** ======

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* Duncan and Duncan index of dissimilarity, calculation of existing data on neighbourhood (election district) levels. Further a correlation analysis is required to trace the impacts of NBS implementation on a timely or spatial basis.

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* [[image:1641897641309-125.png]]

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* where a is the ratio of a given group in a sub-territory and b is the ratio of the rest, n is the total number of subterritories and i is the actual sub-territory. We suggest it be calculated with a as the ratio of population with university degree. The impact of an NBS project can be proved in two ways. As mentioned, it is possible to track the effect on a timely basis, thus measuring segregation at the beginning of an NBS project and 5 years after its end, the changes in segregation level can be measured. On the other hand, the impact of an NBS implementation can be also proved with spatial comparison of the measured neighbourhood (where NBS has been implemented) and another one that has similar features, but no actions has been done. This allows to prove the effectiveness of NBS projects on segregation if there are no timely data available. It is also important to mention that there might be other dependent variables that cannot be influenced by the implementation of NBS projects. These variables need to be normalized before carrying out the correlation analysis.

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====== **Links and references** ======

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* [[https:~~/~~/www.ecologyandsociety.org/vol21/iss2/art39/>>https://www.ecologyandsociety.org/vol21/iss2/art39/]]

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* ”https:~/~/link.springer.com/chapter/10.1007/978-3-319-56091-5_5

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* https:~/~/www.researchgate.net/profile/Nikolett_Watson Puskas/publication/318324312_A_case_study_on_Budapest_Lessons_on _urban_resilience/links/5963b519aca2728c112739ca/A-case-study-on Budapest-Lessons-on-urban-resilience.pdf

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