lynx   »   [go: up one dir, main page]
Academia.eduAcademia.edu

Figure from:Run to the Hills! A Stratified Sampling Approach to Site Clustering on High Grounds in Ancient Samnium Molise Italy

See full PDFdownloadDownload figure
Figure 3. Research area survey region; circles are original SLP survey regions, squares are TAAP sample areas 1-9, in which the sample units have been colored to represent relative artifact mass densities (natural breaks algorithm). Triangles are sites with a Hellenistic/Samnite chronology.

Figure 3 Research area survey region; circles are original SLP survey regions, squares are TAAP sample areas 1-9, in which the sample units have been colored to represent relative artifact mass densities (natural breaks algorithm). Triangles are sites with a Hellenistic/Samnite chronology.

Related Figures (16)

Figure 1. Above: location of the survey area and the sanctuary San Giovanni in Galdo, Colle Rimontato. Below: distribution of Hellenistic/Samnite sites as encoun- tered and a typical example of a Hellenistic/Samnite site, G6, with the raw collected data and hypothetical distribution based on visibility correction.
Figure 1. Above: location of the survey area and the sanctuary San Giovanni in Galdo, Colle Rimontato. Below: distribution of Hellenistic/Samnite sites as encoun- tered and a typical example of a Hellenistic/Samnite site, G6, with the raw collected data and hypothetical distribution based on visibility correction.
Table 1. Landscape classification with average elevation indication.
Table 1. Landscape classification with average elevation indication.
Figure 2. Spatial positioning of sample areas in TAAP survey region (circles represent previous survey regions of the SLP project, i.e., around the sanctuaries). A) Land use map (green colors indicate different land use categories), B) lithology map (brown colors indicate different lithology categories) overlain to produce C) a spatial layer of (abstract representation of) unique combinations, in which the sample areas are placed as optimally as possible to proportionally intersect the strata resulting in D) the eventual placement of sample areas.
Figure 2. Spatial positioning of sample areas in TAAP survey region (circles represent previous survey regions of the SLP project, i.e., around the sanctuaries). A) Land use map (green colors indicate different land use categories), B) lithology map (brown colors indicate different lithology categories) overlain to produce C) a spatial layer of (abstract representation of) unique combinations, in which the sample areas are placed as optimally as possible to proportionally intersect the strata resulting in D) the eventual placement of sample areas.
Table 2. SA coverage of landscape strata.
Table 2. SA coverage of landscape strata.
Table 4. LU-Litho stratum definition and proportional representation in sample units. Abbreviations: hom. = homogeneous agriculture, het. = heterogeneous agriculture, olig = Oligocene, langh = Langhian, tort = Tortonian, and microc. = microcrystals.
Table 4. LU-Litho stratum definition and proportional representation in sample units. Abbreviations: hom. = homogeneous agriculture, het. = heterogeneous agriculture, olig = Oligocene, langh = Langhian, tort = Tortonian, and microc. = microcrystals.
Table 5. Low and Elev. groups with all sites (first two rows) and without larger settlement centers (last two rows).
Table 5. Low and Elev. groups with all sites (first two rows) and without larger settlement centers (last two rows).
Table 3. SAs with LS strata and minimum, maximum, and mean elevation (m). Bold => 500 m. Off-site density analyses
Table 3. SAs with LS strata and minimum, maximum, and mean elevation (m). Bold => 500 m. Off-site density analyses
Figure 4. Boxplot showing variability of CW mass density between SAs. X-axis represents sample areas, Y-axis represents CW mass densities, and labels represent individual sample unit numbers.
Figure 4. Boxplot showing variability of CW mass density between SAs. X-axis represents sample areas, Y-axis represents CW mass densities, and labels represent individual sample unit numbers.
Figure 5. Boxplot showing variability of CW mass density between Low and Elev. groups. X-axis represents groups, Y-axis represents CW mass densities, and labe represent individual sample unit numbers.
Figure 5. Boxplot showing variability of CW mass density between Low and Elev. groups. X-axis represents groups, Y-axis represents CW mass densities, and labe represent individual sample unit numbers.
Table 6. Hypothesis test summaries. Reject 0-hypothesis means statistically significant variability in distribution; accept 0-hypothesis means no statistically significant variability in distribution. a = 0.05, KW = Kruskal-Wallis, KS = Kolmogorov-Smirnov, dens = density, and adj = adjusted.
Table 6. Hypothesis test summaries. Reject 0-hypothesis means statistically significant variability in distribution; accept 0-hypothesis means no statistically significant variability in distribution. a = 0.05, KW = Kruskal-Wallis, KS = Kolmogorov-Smirnov, dens = density, and adj = adjusted.
Figure 6. Boxplot showing variability of CW number densities between Low and Elev. groups. X-axis represents groups, Y-axis represents CW number densitie and labels represent individual sample unit numbers.
Figure 6. Boxplot showing variability of CW number densities between Low and Elev. groups. X-axis represents groups, Y-axis represents CW number densitie and labels represent individual sample unit numbers.
Table 7. Cross table with distribution of visibility scores across LU-Litho strata, with the visibility scores of stratum 13 in bold.
Table 7. Cross table with distribution of visibility scores across LU-Litho strata, with the visibility scores of stratum 13 in bold.
Figure 7. Boxplot showing variability of PW mass densities between Low and Elev. groups. X-axis represents groups, Y-axis represents PW mass densities, and labels represent individual sample unit numbers.
Figure 7. Boxplot showing variability of PW mass densities between Low and Elev. groups. X-axis represents groups, Y-axis represents PW mass densities, and labels represent individual sample unit numbers.
Figure 8. Boxplot showing variability of CW mass densities between LU-Litho strata. X-axis represents LU-Litho strata, Y-axis represents CW mass densities, an labels represent individual sample unit numbers.
Figure 8. Boxplot showing variability of CW mass densities between LU-Litho strata. X-axis represents LU-Litho strata, Y-axis represents CW mass densities, an labels represent individual sample unit numbers.
Figure 9. Boxplot showing variability of CW mass densities between Low and Elev. groups when multiplied by a factor of 1.36. X-axis represents groups, Y-axis represents CW mass densities, and labels represent individual sample unit numbers. A statistical test can be applied to ascertain whether the observed difference in visibility scores is significantly different and can evaluate how strong the difference is. Specifically, a chi-square test is utilized to compare the distributions of visi- bility scores, thereby determining whether these distributions follow a similar pattern or whether they can be classified as originating from distinct visibility-populations. This test is specifically designed for categorical data; although visibility scores may appear to conform to an interval scale, they are, in fact, ordinal in nature, as the differences between the visi- bility scores (i.e., between 3-4 and between 4-5) do not rep- resent equivalent quantities. A comparative analysis between the cumulative visibility score distribution of the four largest LU-Litho strata, ie., LU-Litho stratum 11, 12, and 14 in one group (together 54.7% of the sample collection, excluding We simulate the potential impact of an overall 27% pro- portional difference by applying a correction for 27% less finds in LU-Litho stratum 13, ie., multiplying CW and PW
Figure 9. Boxplot showing variability of CW mass densities between Low and Elev. groups when multiplied by a factor of 1.36. X-axis represents groups, Y-axis represents CW mass densities, and labels represent individual sample unit numbers. A statistical test can be applied to ascertain whether the observed difference in visibility scores is significantly different and can evaluate how strong the difference is. Specifically, a chi-square test is utilized to compare the distributions of visi- bility scores, thereby determining whether these distributions follow a similar pattern or whether they can be classified as originating from distinct visibility-populations. This test is specifically designed for categorical data; although visibility scores may appear to conform to an interval scale, they are, in fact, ordinal in nature, as the differences between the visi- bility scores (i.e., between 3-4 and between 4-5) do not rep- resent equivalent quantities. A comparative analysis between the cumulative visibility score distribution of the four largest LU-Litho strata, ie., LU-Litho stratum 11, 12, and 14 in one group (together 54.7% of the sample collection, excluding We simulate the potential impact of an overall 27% pro- portional difference by applying a correction for 27% less finds in LU-Litho stratum 13, ie., multiplying CW and PW
Figure 10. Boxplot showing variability of CW mass densities between LU-Litho strata when multiplied by a factor of 1.36. X-axis represents LU-Litho strata, Y-axi represents CW mass densities, and labels represent individual sample unit numbers.
Figure 10. Boxplot showing variability of CW mass densities between LU-Litho strata when multiplied by a factor of 1.36. X-axis represents LU-Litho strata, Y-axi represents CW mass densities, and labels represent individual sample unit numbers.
Лучший частный хостинг