Returns an Grid2DSquareCellDouble[] containing geometric density surfaces
at a range of scales: result[ 0 ] - is the result at the first scale (
double the cellsize of grid ) result[ 1 ] - if it exists is the result at
the second scale ( double the cellsize of result[ 0 ] ) result[ n ] - if
it exists is the result at the ( n + 1 )th scale ( double the cellsize of
result[ n - 1 ] ) The algorithm used for generating a geometric density
surface is described in: Turner A (2000) Density Data Generation for
Spatial Data Mining Applications.
For returning the arithmetic mean of all non noDataValues as a BigDecimal
Throws an ArithmeticException if nonNoDataValueCountBigInteger is equal
to zero.
For returning the arithmetic mean of all non noDataValues as a BigDecimal
Throws an ArithmeticException if nonNoDataValueCountBigInteger is equal
to zero.
For returning the Geometric Mean of all non noDataValues as a BigDecimal
Warning! This is imprecise and it can happen that Math.pow does not
return what might be expected! (For example, negative powers in the range
(0,1) for negative numbers.)
TODO:
Develop a pow function such as com.ibm.icu.math.BigDecimal.pow
This resource is not used here due to licensing, but it could be...
For returning the Geometric Mean of all non _NoDataValues as a double
Warning! This is imprecise and it can happen that Math.pow does not
return what might be expected! (For example, negative powers in the range
(0,1) for negative numbers.)
TODO:
Develop a pow function such as com.ibm.icu.math.BigDecimal.pow
This resource is not used here due to licensing, but it could be...
Returns a HashSet containing _CellIDs which identifies cells for which
neighbouring cells in the immediate 8 cell neighbourhood that are either
the same value, lower or noDataValues
Returns double[] result of kernel parameters where:
result[0] = The total sum of all the weights for a given kernel;
result[1] = The total number of cells thats centroids are within distance
of an arbitrary cell centroid of grid2DSquareCell.
Returns a double value for the height of a kernel at thisDistance from
the centre of a kernel with; Bandwidth distance, weight at the centre of
weightIntersect and distance decay of weightFactor.
Returns an Grid2DSquareCellDouble result containing values which
indicate the direction of the maximum down slope for the immediate 8 cell
neighbourhood.
Returns an Grid2DSquareCellDouble[] metrics1 where:
metrics1[0] = no data count;
metrics1[1] = flatness;
metrics1[2] = roughness;
metrics1[3] = slopyness;
metrics1[4] = levelness;
metrics1[5] = totalDownness;
metrics1[6] = averageDownness;
metrics1[7] = totalUpness;
metrics1[8] = averageUpness;
metrics1[9] = maxd_hhhh [ sum of distance weighted maximum height differences ];
metrics1[10] = mind_hhhh [ sum of distance weighted minimum height differences ];
metrics1[11] = sumd_hhhh [ sum of distance weighted height differences ];
metrics1[12] = aved_hhhh [ sum of distance weighted average height difference ];
metrics1[13] = count_hhhh [ count ];
metrics1[14] = w_hhhh [ sum of distance weights ];
metrics1[15] = mind_hxhx_ai_hhhl [ sum of distance weighted ( minimum difference of cells adjacent to lower cell ) ];
metrics1[16] = maxd_hxhx_ai_hhhl [ sum of distance weighted ( maximum difference of cells adjacent to lower cell ) ];
metrics1[17] = sumd_hxhx_ai_hhhl [ sum of distance weighted ( sum of differences of cells adjacent to lower cell ) ];
metrics1[18] = d_xhxx_ai_hhhl [ sum of distance weighted ( difference of cell opposite lower cell ) ];
metrics1[19] = d_xxxl_ai_hhhl [ sum of distance weighted ( difference of lower cell ) ];
metrics1[20] = sumd_xhxl_ai_hhhl [ sum of distance weighted ( sum of differences of lower cell and cell opposite ) ];
metrics1[21] = mind_abs_xhxl_ai_hhhl [ sum of distance weighted ( minimum difference magnitude of lower cell and cell opposite ) ];
metrics1[22] = maxd_abs_xhxl_ai_hhhl [ sum of distance weighted ( maximum difference magnitude of lower cell and cell opposite ) ];
metrics1[23] = sumd_abs_xhxl_ai_hhhl [ sum of distance weighted ( sum of difference magnitudes of lower cell and cell opposite ) ];
metrics1[24] = count_hhhl [ count ];
metrics1[25] = w_hhhl [ sum of distance weights ];
metrics1[26] = mind_hxhx_ai_hlhl [ sum of distance weighted ( minimum difference of higher cells ) ];
metrics1[27] = maxd_hxhx_ai_hlhl [ sum of distance weighted ( maximum difference of higher cells ) ];
metrics1[28] = sumd_hxhx_ai_hlhl [ sum of distance weighted ( sum differences of higher cells ) ];
metrics1[29] = mind_xlxl_ai_hlhl [ sum of distance weighted ( minimum difference of lower cells ) ];
metrics1[30] = maxd_xlxl_ai_hlhl [ sum of distance weighted ( maximum difference of lower cells ) ];
metrics1[31] = sumd_xlxl_ai_hlhl [ sum of distance weighted ( sum of differences of lower cells ) ];
metrics1[32] = mind_abs_hlhl [ sum of distance weighted ( minimum difference magnitude of cells ) ];
metrics1[33] = maxd_abs_hlhl [ sum of distance weighted ( maximum difference magnitude of cells ) ];
metrics1[34] = sumd_abs_hlhl [ sum of distance weighted ( sum of difference magnitudes of cells ) ];
metrics1[35] = count_hlhl [ count ];
metrics1[36] = w_hlhl [ sum of distance weights ];
metrics1[37] = mind_hhxx_ai_hhll [ sum of distance weighted ( minimum difference of higher cells ) ];
metrics1[38] = maxd_hhxx_ai_hhll [ sum of distance weighted ( maximum difference of higher cells ) ];
metrics1[39] = sumd_hhxx_ai_hhll [ sum of distance weighted ( sum of differences of higher cells ) ];
metrics1[40] = mind_xxll_ai_hhll [ sum of distance weighted ( minimum difference of lower cells ) ];
metrics1[41] = maxd_xxll_ai_hhll [ sum of distance weighted ( maximum difference of lower cells ) ];
metrics1[42] = sumd_xxll_ai_hhll [ sum of distance weighted ( sum of differences of lower cells ) ];
metrics1[43] = mind_abs_hhll [ sum of distance weighted ( minimum difference magnitude of cells ) ];
metrics1[44] = maxd_abs_hhll [ sum of distance weighted ( maximum difference magnitude of cells ) ];
metrics1[45] = sumd_abs_hhll [ sum of distance weighted ( sum of difference magnitudes of cells ) ];
metrics1[46] = count_hhll [ count ];
metrics1[47] = w_hhll [ sum of distance weights ];
metrics1[48] = mind_lxlx_ai_lllh [ sum of distance weighted ( minimum difference of cells adjacent to higher cell ) ];
metrics1[49] = maxd_lxlx_ai_lllh [ sum of distance weighted ( maximum difference of cells adjacent to higher cell ) ];
metrics1[50] = sumd_lxlx_ai_lllh [ sum of distance weighted ( sum of differences of cells adjacent to higher cell ) ];
metrics1[51] = d_xlxx_ai_lllh [ sum of distance weighted ( difference of cell opposite higher cell ) ];
metrics1[52] = d_xxxh_ai_lllh [ sum of distance weighted ( difference of higher cell ) ];
metrics1[53] = sumd_xlxh_ai_lllh [ sum of distance weighted ( sum of differences of higher cell and cell opposite ) ];
metrics1[54] = mind_abs_xlxh_ai_lllh [ sum of distance weighted ( minimum difference magnitude of higher cell and cell opposite ) ];
metrics1[55] = maxd_abs_xlxh_ai_lllh [ sum of distance weighted ( maximum difference magnitude of higher cell and cell opposite ) ];
metrics1[56] = sumd_abs_xlxh_ai_lllh [ sum of distance weighted ( sum of difference magnitudes of higher cell and cell opposite ) ];
metrics1[57] = count_lllh [ count ];
metrics1[58] = w_lllh [ sum of distance weights ];
metrics1[59] = maxd_llll [ sum of distance weighted maximum height differences ];
metrics1[60] = mind_llll [ sum of distance weighted minimum height differences ];
metrics1[61] = sumd_llll [ sum of distance weighted height differences ];
metrics1[62] = aved_llll [ sum of distance weighted average height difference ];
metrics1[63] = count_llll [ count ];
metrics1[64] = w_llll [ sum of distance weights ];
Returns an Grid2DSquareCellDouble[] metrics1 where: \n
metrics1[0] = no data count; \n
metrics1[1] = flatness; \n
metrics1[2] = roughness; \n
metrics1[3] = slopyness; \n
metrics1[4] = levelness; \n
metrics1[5] = totalDownness; \n
metrics1[6] = averageDownness; \n
metrics1[7] = totalUpness; \n
metrics1[8] = averageUpness; \n
metrics1[9] = maxd_hhhh [ sum of distance weighted maximum height differences ]; \n
metrics1[10] = mind_hhhh [ sum of distance weighted minimum height differences ]; \n
metrics1[11] = sumd_hhhh [ sum of distance weighted height differences ]; \n
metrics1[12] = aved_hhhh [ sum of distance weighted average height difference ]; \n
metrics1[13] = count_hhhh [ count ]; \n
metrics1[14] = w_hhhh [ sum of distance weights ]; \n
metrics1[15] = mind_hxhx_ai_hhhl [ sum of distance weighted ( minimum difference of cells adjacent to lower cell ) ]; \n
metrics1[16] = maxd_hxhx_ai_hhhl [ sum of distance weighted ( maximum difference of cells adjacent to lower cell ) ]; \n
metrics1[17] = sumd_hxhx_ai_hhhl [ sum of distance weighted ( sum of differences of cells adjacent to lower cell ) ]; \n
metrics1[18] = d_xhxx_ai_hhhl [ sum of distance weighted ( difference of cell opposite lower cell ) ]; \n
metrics1[19] = d_xxxl_ai_hhhl [ sum of distance weighted ( difference of lower cell ) ]; \n
metrics1[20] = sumd_xhxl_ai_hhhl [ sum of distance weighted ( sum of differences of lower cell and cell opposite ) ]; \n
metrics1[21] = mind_abs_xhxl_ai_hhhl [ sum of distance weighted ( minimum difference magnitude of lower cell and cell opposite ) ]; \n
metrics1[22] = maxd_abs_xhxl_ai_hhhl [ sum of distance weighted ( maximum difference magnitude of lower cell and cell opposite ) ]; \n
metrics1[23] = sumd_abs_xhxl_ai_hhhl [ sum of distance weighted ( sum of difference magnitudes of lower cell and cell opposite ) ]; \n
metrics1[24] = count_hhhl [ count ]; \n
metrics1[25] = w_hhhl [ sum of distance weights ]; \n
metrics1[26] = mind_hxhx_ai_hlhl [ sum of distance weighted ( minimum difference of higher cells ) ]; \n
metrics1[27] = maxd_hxhx_ai_hlhl [ sum of distance weighted ( maximum difference of higher cells ) ]; \n
metrics1[28] = sumd_hxhx_ai_hlhl [ sum of distance weighted ( sum differences of higher cells ) ]; \n
metrics1[29] = mind_xlxl_ai_hlhl [ sum of distance weighted ( minimum difference of lower cells ) ]; \n
metrics1[30] = maxd_xlxl_ai_hlhl [ sum of distance weighted ( maximum difference of lower cells ) ]; \n
metrics1[31] = sumd_xlxl_ai_hlhl [ sum of distance weighted ( sum of differences of lower cells ) ]; \n
metrics1[32] = mind_abs_hlhl [ sum of distance weighted ( minimum difference magnitude of cells ) ]; \n
metrics1[33] = maxd_abs_hlhl [ sum of distance weighted ( maximum difference magnitude of cells ) ]; \n
metrics1[34] = sumd_abs_hlhl [ sum of distance weighted ( sum of difference magnitudes of cells ) ]; \n
metrics1[35] = count_hlhl [ count ]; \n
metrics1[36] = w_hlhl [ sum of distance weights ]; \n
metrics1[37] = mind_hhxx_ai_hhll [ sum of distance weighted ( minimum difference of higher cells ) ]; \n
metrics1[38] = maxd_hhxx_ai_hhll [ sum of distance weighted ( maximum difference of higher cells ) ]; \n
metrics1[39] = sumd_hhxx_ai_hhll [ sum of distance weighted ( sum of differences of higher cells ) ]; \n
metrics1[40] = mind_xxll_ai_hhll [ sum of distance weighted ( minimum difference of lower cells ) ]; \n
metrics1[41] = maxd_xxll_ai_hhll [ sum of distance weighted ( maximum difference of lower cells ) ]; \n
metrics1[42] = sumd_xxll_ai_hhll [ sum of distance weighted ( sum of differences of lower cells ) ]; \n
metrics1[43] = mind_abs_hhll [ sum of distance weighted ( minimum difference magnitude of cells ) ]; \n
metrics1[44] = maxd_abs_hhll [ sum of distance weighted ( maximum difference magnitude of cells ) ]; \n
metrics1[45] = sumd_abs_hhll [ sum of distance weighted ( sum of difference magnitudes of cells ) ]; \n
metrics1[46] = count_hhll [ count ]; \n
metrics1[47] = w_hhll [ sum of distance weights ]; \n
metrics1[48] = mind_lxlx_ai_lllh [ sum of distance weighted ( minimum difference of cells adjacent to higher cell ) ]; \n
metrics1[49] = maxd_lxlx_ai_lllh [ sum of distance weighted ( maximum difference of cells adjacent to higher cell ) ]; \n
metrics1[50] = sumd_lxlx_ai_lllh [ sum of distance weighted ( sum of differences of cells adjacent to higher cell ) ]; \n
metrics1[51] = d_xlxx_ai_lllh [ sum of distance weighted ( difference of cell opposite higher cell ) ]; \n
metrics1[52] = d_xxxh_ai_lllh [ sum of distance weighted ( difference of higher cell ) ]; \n
metrics1[53] = sumd_xlxh_ai_lllh [ sum of distance weighted ( sum of differences of higher cell and cell opposite ) ]; \n
metrics1[54] = mind_abs_xlxh_ai_lllh [ sum of distance weighted ( minimum difference magnitude of higher cell and cell opposite ) ]; \n
metrics1[55] = maxd_abs_xlxh_ai_lllh [ sum of distance weighted ( maximum difference magnitude of higher cell and cell opposite ) ]; \n
metrics1[56] = sumd_abs_xlxh_ai_lllh [ sum of distance weighted ( sum of difference magnitudes of higher cell and cell opposite ) ]; \n
metrics1[57] = count_lllh [ count ]; \n
metrics1[58] = w_lllh [ sum of distance weights ]; \n
metrics1[59] = maxd_llll [ sum of distance weighted maximum height differences ]; \n
metrics1[60] = mind_llll [ sum of distance weighted minimum height differences ]; \n
metrics1[61] = sumd_llll [ sum of distance weighted height differences ]; \n
metrics1[62] = aved_llll [ sum of distance weighted average height difference ]; \n
metrics1[63] = count_llll [ count ]; \n
metrics1[64] = w_llll [ sum of distance weights ]; \n
For returning the mode of all non noDataValues either as a
TDoubleHashSet or as a TIntHashSet respectively depending on
if ( this._Grid2DSquareCell.getClass() == Grid2DSquareCellInt.class ) or
if ( this._Grid2DSquareCell.getClass() == Grid2DSquareCellDouble.class ).
Returns a double[] _SlopeAndAspect where:
_SlopeAndAspect[0] is the aggregate slope over the region weighted by
distance, weightIntersect and weightFactor;
_SlopeAndAspect[1] is the aggregate aspect over the region weighted by
distance, weightIntersect and weightFactor.
Returns a double[] _SlopeAndAspect where:
_SlopeAndAspect[0] is the aggregate slope over the region weighted by
distance, weightIntersect and weightFactor;
_SlopeAndAspect[1] is the aggregate aspect over the region weighted by
distance, weightIntersect and weightFactor.
Returns a double[] _SlopeAndAspect where:
_SlopeAndAspect[0] is the aggregate slope over the region weighted by
distance, weightIntersect and weightFactor;
_SlopeAndAspect[1] is the aggregate aspect over the region weighted by
distance, weightIntersect and weightFactor.
Returns an Grid2DSquareCellDouble[] each element of which
corresponds to a metrics of up slope cells of grid - a DEM
The steeper the slope the higher the runoff?