Both of these solutions require extensions to the standard ArcGIS desktop, such as Spatial Analyst.
If you don't want to interpolate between points, the solution is easy. Simply use the Conversion Toolbox > To Raster > Points to Raster tool to create a raster of your points. You can use the Cell Size field to determine the size of the grid, and you can combine points in simple ways (MOST_FREQUENT | SUM | MEAN | STANDARD_DEVIATION | MAXIMUM | MINIMUM | RANGE | COUNT).
If you want to interpolate between points, you have multiple options, depending on the results you want. Kriging and Inverse Distance Weighted are two common types of interpolation, but Spatial Analyst comes with others as well.
Depending on what your points actually represent, you may also consider using Point Density or Kernel Density. These would be more appropriate when you were attempting to measure the density of discrete observations, rather than interpolate between scattered measurements.
Look into these, and return if you have more specific questions. There are users here with extensive backgrounds in geostatistics who should be able to handle any problems.
Because these are regular points, you effectively already have a DEM; it's just in a different format than ArcGIS likes. This makes two different strategies available to you:
Convert the data into a format ArcGIS can handle. One way is to set up a raster extent and cellsize that (i) cover your DEM and (ii) situate each point near the middle of its cell. Import your DEM as a point layer and convert that directly, with no interpolation, into a grid format. (Each cell acquires the value of the unique point lying within it.) From now on, interpolation will be automatically performed whenever you resample the DEM, when aggregating, resizing, or reprojecting it. This gives you access to nearest-neighbor (not recommended), bilinear, and cubic convolution methods. You can create three separate grids this way: one for elevation, one for slope, one for aspect. For this to work, though, you must be using exactly the same coordinate system (including the projection) used to create the DEM in the first place, so that the points are truly evenly spaced and parallel to the coordinate axes.
Pretend the points are "irregular" and interpolate only the elevations using any method you can (splines, IDW, kriging, etc). Derive new slope and aspect DEMs from the interpolated elevations. Unless you are using a Topo2DEM approach and have additional information you can supply--such as stream beds, elevation contours, peaks, or such material--then it is likely this method will merely produce meaningless artifacts that reflect the interpolation method as much as they do the original data. It will also require much more computation than the first method (kriging may even be impossible because of this).
From what I know, there is no direct tool. So you need to :
1) read the maximum value in the properties of your raster (right click on the raster > raster properties ) (if not available, launch "build statistics"(management tools > raster > raster properties). Make sure that your skip factor is set to "1" in order to use all pixels during the calculation (skip factor correspond to the pyramid level)
2) use the "Equal to" tool (spatial analyst > math > logical) to identify the cells with the maximum value
remark : alternatively you can do this with "reclassify" or map algebra.
Best Answer
Both of these solutions require extensions to the standard ArcGIS desktop, such as Spatial Analyst.
If you don't want to interpolate between points, the solution is easy. Simply use the Conversion Toolbox > To Raster > Points to Raster tool to create a raster of your points. You can use the Cell Size field to determine the size of the grid, and you can combine points in simple ways (
MOST_FREQUENT | SUM | MEAN | STANDARD_DEVIATION | MAXIMUM | MINIMUM | RANGE | COUNT
).If you want to interpolate between points, you have multiple options, depending on the results you want. Kriging and Inverse Distance Weighted are two common types of interpolation, but Spatial Analyst comes with others as well.
Depending on what your points actually represent, you may also consider using Point Density or Kernel Density. These would be more appropriate when you were attempting to measure the density of discrete observations, rather than interpolate between scattered measurements.
Look into these, and return if you have more specific questions. There are users here with extensive backgrounds in geostatistics who should be able to handle any problems.