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Medeiros, E. (2023). Data and modelling for the Territorial Impact Assessment (TIA) of policies. In Eleonora Bertoni, Matteo Fontana, Lorenzo Gabrielli, Serena Signorelli, Michele Vespe (Ed.), Handbook of computational social science for policy. (pp. 177-194). Cham: Springer.

E. J. Medeiros,  "Data and modelling for the Territorial Impact Assessment (TIA) of policies", in Handbook of computational social science for policy, Eleonora Bertoni, Matteo Fontana, Lorenzo Gabrielli, Serena Signorelli, Michele Vespe, Ed., Cham, Springer, 2023, pp. 177-194

@incollection{medeiros2023_1724524788392,
	author = "Medeiros, E.",
	title = "Data and modelling for the Territorial Impact Assessment (TIA) of policies",
	chapter = "",
	booktitle = "Handbook of computational social science for policy",
	year = "2023",
	volume = "",
	series = "",
	edition = "",
	pages = "177-177",
	publisher = "Springer",
	address = "Cham",
	url = "https://link.springer.com/chapter/10.1007/978-3-031-16624-2_9"
}

TY  - CHAP
TI  - Data and modelling for the Territorial Impact Assessment (TIA) of policies
T2  - Handbook of computational social science for policy
AU  - Medeiros, E.
PY  - 2023
SP  - 177-194
DO  - 10.1007/978-3-031-16624-2_9
CY  - Cham
UR  - https://link.springer.com/chapter/10.1007/978-3-031-16624-2_9
AB  - Territorial Impact Assessment (TIA) is still a ‘new kid on the block’ on the panorama of policy evaluation methodologies. In synthesis, TIA methodologies are thematically holistic, multi-dimensional and require the analysis of a wide pool of data, not only of economic character, but also related with social, environmental, governance and planning processes, in all territorial scales. For that, TIA requires a wealth of comparable and updated territorialised data. Here, data availability is often scarce in many of the selected analytic dimensions and respective components, to assess territorial impacts in a given territory, in particular in the domains of governance, planning and environment. In this context, this chapter presents a list of non-traditional potential indicators which can be used in existing TIA methodologies, Moreover, the analysis was able to show how important can be the use of non-traditional data, to complement mainstream statistical indicators associated with socioeconomic development trends. However, for the interested scientist, the dispersal of existing non-traditional data per a multitude of sources can pose a huge challenge. Hence the need of an online platform which centralises and updates non-traditional data for the use of all interested in implementing TIA methodologies.    


ER  -