MeanPointDensity[pdata]
estimates the mean point density
from point data pdata in the observation region reg.
MeanPointDensity[bdata]
estimates the mean point density
from binned data bdata.
MeanPointDensity[pproc]
computes the mean point density
for point process pproc.
MeanPointDensity
MeanPointDensity[pdata]
estimates the mean point density
from point data pdata in the observation region reg.
MeanPointDensity[bdata]
estimates the mean point density
from binned data bdata.
MeanPointDensity[pproc]
computes the mean point density
for point process pproc.
Details and Options
- MeanPointDensity is also known as intensity or mean intensity.
- The mean point density is the average number of points per unit measure of the observation region. It is given by
, where
is the number of points and
is the measure of the observation region reg. - The points pdata can have the following forms:
-
{p1,p2,…} points pi GeoPosition[…],GeoPositionXYZ[…],… geographic points SpatialPointData[…] spatial point collection {pts,reg} point collection pts and observation region reg - If the observation region reg is not given, a region is automatically computed using RipleyRassonRegion.
- The point process pproc can have the following forms:
-
proc a point process proc {proc,reg} a point process proc and observation region reg - The observation region reg should be a parameter-free, full-dimensional and bounded region as tested by SpatialObservationRegionQ.
- The binned data bdata is taken to come from SpatialBinnedPointData.
- With a point process pproc, either exact formulas or approximate computation methods are used.
Examples
open all close allBasic Examples (4)
Find the mean point density of cities in California:
cities = CityData[{All, "California"}];GeoListPlot[cities]MeanPointDensity[{cities, Entity["AdministrativeDivision", {"California", "UnitedStates"}]}]//NCreate a SpatialPointData:
spd = SpatialPointData[RandomReal[1, {300, 2}]]PointValuePlot[spd]MeanPointDensity[spd]Mean point density of a point process:
MeanPointDensity[PoissonPointProcess[30, 2]]MeanPointDensity[ThomasPointProcess[30, 12, 3, 2]]Mean point density of a point process on the surface of the Earth:
proc = HardcorePointProcess[Quantity[0.001, 1/"Kilometers"^2], Quantity[30, "Kilometers"], 2];
reg = GeoBoundsRegion[{{0, 1}, {0, 1}}];MeanPointDensity[{proc, reg}]Scope (2)
Mean point density of points in 2D:
pts = RandomPoint[Disk[], 100];MeanPointDensity[pts]Specify points and the original region:
reg1 = Disk[];Graphics[{Opacity[.2], reg1, Opacity[1], Point[pts]}, Axes -> True]MeanPointDensity[{pts, reg1}]reg2 = Disk[{1 / 2, 0}, 1];Graphics[{Opacity[.2], reg2, Opacity[1], Point[pts]}, Axes -> True]MeanPointDensity[{pts, reg2}]Estimate mean point density for a hardcore point process:
proc = HardcorePointProcess[20, .3, 2];
reg = Disk[];MeanPointDensity[{proc, reg}]N[%]Related Guides
History
Text
Wolfram Research (2020), MeanPointDensity, Wolfram Language function, https://reference.wolfram.com/language/ref/MeanPointDensity.html.
CMS
Wolfram Language. 2020. "MeanPointDensity." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/MeanPointDensity.html.
APA
Wolfram Language. (2020). MeanPointDensity. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/MeanPointDensity.html
BibTeX
@misc{reference.wolfram_2026_meanpointdensity, author="Wolfram Research", title="{MeanPointDensity}", year="2020", howpublished="\url{https://reference.wolfram.com/language/ref/MeanPointDensity.html}", note=[Accessed: 12-June-2026]}
BibLaTeX
@online{reference.wolfram_2026_meanpointdensity, organization={Wolfram Research}, title={MeanPointDensity}, year={2020}, url={https://reference.wolfram.com/language/ref/MeanPointDensity.html}, note=[Accessed: 12-June-2026]}