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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 International Publishing.

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 International Publishing, 2023, pp. 177-194

@incollection{medeiros2023_1714747270506,
	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 International Publishing",
	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  -