Spatial analysis data for 'Lines in the sand: quantifying the cumulative development footprint in the world's largest remaining temperate woodland'

These datasets provide the data underlying the publication on "Lines in the sand: quantifying the cumulative development footprint in the world’s largest remaining temperate woodland" https://link.springer.com/article/10.1007/s10980-017-0558-z. . The datasets are: (A) data in csv format: [1] development footprint by sample area: Information on the 24, ~490 km^2 sample areas assessed in the study, including the different infrastructure types (roads, railways, mapped tracks, un-mapped tracks which have been manually digitized in the study using aerial imagery and hub infrastructure such as mine pits and waste rock dumps, also manually digitized in the study). Also contains some key co-variables assessed as potential explanatory variables for development footprint. The region-wide modelling of development footprint found strong positive effects of mining project density and pastoralism, as well as a highly significant negative interaction between the two. At low mining project densities, development footprints are more extensive in pastoral areas, but at high mining project densities, pastoral areas are relatively less developed than non-pastoral areas, on average. [2] Great Western Woodlands (GWW) 20 km grid: The datasets provides data for the 20x20 km grid placed over the whole GWW and used for the regional estimation of development footprint, linear infrastructure density, and linear infrastructure type based on the region-wide analysis. Data is for each cell in the grid and provides the total length of roads in that grid cell, MINEDEX mining projects, pastoral status, etc. This dateset was used to project the data from the 24 study areas across the whole of the Great Western Woodlands and calculate region-wide estimates of development footprint and linear infrastructure lengths. [3] disturbance by patch: This dataset provides the data for each patch for the analysis of patch-level drivers of development footprint, which was performed to gain further insights into the effects of other landscape variables that what could be gleaned from the region-wide analysis. For this analysis, we divided sample areas into polygonal patch types, each with a unique combination of the following categorical co-variables: pastoral tenure, greenstone lithology, conservation tenure, ironstone formation, schedule-1 area clearing restrictions, environmentally sensitive area designation, vegetation formation, and sample area. For each patch type (n=261), we calculated the following attributes: a) number of mining projects, b) number of dead mineral tenements, c) sum of duration of all live and dead tenements, d) type of tenements (exploration/prospecting tenement, mining and related activities tenement, none), e) primary target commodity (gold, nickel, iron-ore, other), f) distance to wheatbelt, and g) distance to the nearest town. [4] mapped versus digitized tracks: This dataset provides mapped and un-mapped track widths, measured using high-resolution aerial imagery at at least 20 randomly-generated locations within each of 24 sample areas. Pastoral tenure and mining intensity for each sample area are included for analysis purposes. [5] edge effect scenarios: Hypothetical edge effect zones were created, based on effect zones gleaned from the literature and arranged under three scenarios, to reflect potential risks of offsite impacts in areas adjacent to development footprints observed (see appendix 3 of article). The calculated proportion of the entire GWW within edge effect zones varied from ~3% under the conservative scenario to ~35% under the maximal scenario. Within the range of development footprints observed in this study, the proportion of a landscape that lies within edge effect zones increases hyperbolically with the number of mining projects, and approaches 100% in the maximal scenario, 60% in the moderate scenario, and ~20% under the conservative scenario. shapefiles: [6] Great Western Woodlands boundary, [7] sample areas (layer file shows sample areas by category).

本数据集支撑了发表于《沙中之线：量化全球现存最大温带林地的累积开发足迹（development footprint）》（https://link.springer.com/article/10.1007/s10980-017-0558-z）的研究成果。本数据集包含以下内容：
(A) 逗号分隔值（CSV）格式数据：
[1] 样区开发足迹数据：涵盖本研究评估的24个面积约490 km²的样区相关信息，包含各类基础设施类型——道路、铁路、研究中通过高分辨率航空影像手动数字化的已测绘与未测绘轨迹，以及研究中手动数字化的矿井坑、废石堆等枢纽基础设施。同时纳入部分关键协变量（covariable），作为开发足迹的潜在解释变量。本研究通过区域尺度开发足迹建模发现，采矿项目密度与畜牧活动均存在显著正向影响，且二者间存在高度显著的负交互效应：在采矿项目密度较低时，畜牧区的开发足迹更为广泛；而当采矿项目密度较高时，畜牧区的开发程度平均而言较非畜牧区更低。
[2] 大西部林地（Great Western Woodlands, GWW）20km网格数据：本数据集为覆盖整个GWW的20×20km网格提供数据，用于基于区域分析开展开发足迹、线性基础设施密度及线性基础设施类型的区域估算。数据包含每个网格单元内的道路总长度、MINEDEX采矿项目、畜牧状态等信息。本数据集用于将24个研究样区的研究结果外推至整个大西部林地，并计算区域尺度的开发足迹与线性基础设施总长度估算值。
[3] 斑块级扰动数据：本数据集为每个斑块提供数据，用于分析开发足迹的斑块级驱动因子，以进一步挖掘区域尺度分析未能涵盖的其他景观变量影响机制。本分析将样区划分为多种多边形斑块类型，每种类型均由以下分类协变量的唯一组合构成：畜牧保有权、绿岩岩性、保护保有权、铁矿层、附表1（Schedule-1）区域清理限制、环境敏感区标识、植被类型及样区。对于每种斑块类型（n=261），我们计算了以下属性：a）采矿项目数量；b）失效矿业租约（tenements）数量；c）有效与失效租约的总存续时长；d）租约类型（勘探/探矿租约、采矿及相关活动租约、无租约）；e）主要目标矿种（金、镍、铁矿石、其他）；f）至小麦带的距离；g）至最近城镇的距离。
[4] 测绘轨迹与数字化轨迹对比数据：本数据集包含测绘与未测绘轨迹的宽度数据，通过在24个样区中的每个样区内至少20个随机生成的位置使用高分辨率航空影像测量得到。为便于分析，本数据集同时包含每个样区的畜牧保有权与采矿强度信息。
[5] 边缘效应情景数据：基于文献中的效应区设置，本研究构建了假想边缘效应区（hypothetical edge effect zones），并分为三种情景，以反映观测到的开发足迹周边区域存在的场外影响潜在风险（详见论文附录3）。整个GWW处于边缘效应区内的比例在保守情景下约为3%，在极端情景下可达约35%。在本研究观测到的开发足迹范围内，景观中处于边缘效应区的比例随采矿项目数量呈双曲线增长；在极端情景下该比例将趋近100%，中等情景下约为60%，保守情景下约为20%。
形状文件（shapefiles）格式数据：
[6] 大西部林地边界矢量数据；
[7] 样区矢量数据（图层按类别展示样区分布）。