Integrated statistical indicators from Scottish linked open government data

被引:4
|
作者
Karamanou, Areti [1 ,2 ]
Kalampokis, Evangelos [1 ,2 ]
Tarabanis, Konstantinos [1 ,2 ]
机构
[1] Ctr Res & Technol HELLAS CERTH, 6th Km Charilaou Thermi Rd, Thessaloniki 57001, Greece
[2] Univ Macedonia, Dept Business Adm, Informat Syst Lab, Egnatia 156, Thessaloniki 54636, Greece
来源
DATA IN BRIEF | 2023年 / 46卷
关键词
Integrated statistical indicators; Linked data; Open government data; Scottish statistics; Machine learning;
D O I
10.1016/j.dib.2022.108779
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Open Government Data (OGD), including statistical data, such as economic, environmental and social indicators, are data published by the public sector for free reuse. These data have a huge potential when exploited using Machine Learning methods. Linked Data technologies facilitate retrieving integrated statistical indicators by defining and executing SPARQL queries. However, statistical indicators are available in different temporal and spatial granularity levels as well using different units of measurement. This data article describes the integrated statistical indicators that were retrieved from the official Scottish data portal in order to facilitate the exploitation of Machine Learning methods in OGD. Multiple SPARQL queries as well as manual search in the data portal were employed towards this end. The resulted dataset comprises the maximum number of compatible datasets, i.e., datasets with matching temporal and spatial characteristics. In particular, the data include 60 statistical indicators from seven categories such as health and social care, housing, and crime and justice. The indicators refer to the 6,976 "2011 data zones" of Scotland, while the year of reference is 2015. Data are ready to be used by the research community, students, policy makers, and journalists and give rise to plenty of social, business, and research scenarios that can be solved using Machine Learning technologies and methods. (c) 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
引用
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页数:14
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