Dataset Information

Kernel density estimation and transition maps of Moldavian Neolithic and Eneolithic settlement.

ABSTRACT: The data presented in this article are related to the research article entitled "Neo-Eneolithic settlement pattern and salt exploitation in Romanian Moldavia" (Brigand and Weller, 2018) [1]. Kernel density estimation (KDE) is used in order to move beyond the discrete distribution of sites and to enable us to work on a continuous surface that reflects the intensity of the occupation in the space. Maps of density per period - Neolithic I (Cris), Neolithic II (LBK), Eneolithic I (Precucuteni), Eneolithic II (Cucuteni A), Eneolithic III-IV (Cucuteni A-B and B) - are used to create maps of density difference (Figs. 1-4) in order to analyse the dynamic (either non-existent, negative or positive) between two chronological sequences.

SUBMITTER: Brigand R

PROVIDER: S-EPMC5988340 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Kernel density estimation and transition maps of Moldavian Neolithic and Eneolithic settlement.

Brigand Robin R Weller Olivier O

Data in brief 20180203

The data presented in this article are related to the research article entitled "Neo-Eneolithic settlement pattern and salt exploitation in Romanian Moldavia" (Brigand and Weller, 2018) [1]. Kernel density estimation (KDE) is used in order to move beyond the discrete distribution of sites and to enable us to work on a continuous surface that reflects the intensity of the occupation in the space. Maps of density per period - Neolithic I (Cris), Neolithic II (LBK), Eneolithic I (Precucuteni), Eneo ...[more]

PMID: 29876417

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Project description:BackgroundLocal destinations have previously been shown to be associated with higher levels of both physical activity and walking, but little is known about how the distribution of destinations is related to activity. Kernel density estimation is a spatial analysis technique that accounts for the location of features relative to each other. Using kernel density estimation, this study sought to investigate whether individuals who live near destinations (shops and service facilities) that are more intensely distributed rather than dispersed: 1) have higher odds of being sufficiently active; 2) engage in more frequent walking for transport and recreation.MethodsThe sample consisted of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia. Destinations within these areas were geocoded and kernel density estimates of destination intensity were created using kernels of 400m (meters), 800m and 1200m. Using multilevel logistic regression, the association between destination intensity (classified in quintiles Q1(least)-Q5(most)) and likelihood of: 1) being sufficiently active (compared to insufficiently active); 2) walking≥4/week (at least 4 times per week, compared to walking less), was estimated in models that were adjusted for potential confounders.ResultsFor all kernel distances, there was a significantly greater likelihood of walking≥4/week, among respondents living in areas of greatest destinations intensity compared to areas with least destination intensity: 400m (Q4 OR 1.41 95%CI 1.02-1.96; Q5 OR 1.49 95%CI 1.06-2.09), 800m (Q4 OR 1.55, 95%CI 1.09-2.21; Q5, OR 1.71, 95%CI 1.18-2.48) and 1200m (Q4, OR 1.7, 95%CI 1.18-2.45; Q5, OR 1.86 95%CI 1.28-2.71). There was also evidence of associations between destination intensity and sufficient physical activity, however these associations were markedly attenuated when walking was included in the models.ConclusionsThis study, conducted within urban Melbourne, found that those who lived in areas of greater destination intensity walked more frequently, and showed higher odds of being sufficiently physically active-an effect that was largely explained by levels of walking. The results suggest that increasing the intensity of destinations in areas where they are more dispersed; and or planning neighborhoods with greater destination intensity, may increase residents' likelihood of being sufficiently active for health.

Dataset Information

Kernel density estimation and transition maps of Moldavian Neolithic and Eneolithic settlement.

Publications

Kernel density estimation and transition maps of Moldavian Neolithic and Eneolithic settlement.

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