(Aggregation is accomplished using the 'Aggregate Add-In', installed with AWhere Pro, but not with AWhere Express. AWhere Express users can download the Aggregate Add-In from the AWhere Inc. website. Click here to read more about the Aggregation Add-In tool.)
The Aggregate Add-In cannot perform an aggregation on a point map layer because points do not have ‘area’. By definition a point is a non-dimensional feature…it has no length or width…thus it has no area. The primary utility of the Aggregate Add-In is to combine multiple, neighboring 2-dimentional map features… that is, map features that have ‘area’ (i.e. polygons)… into one larger polygon (see example below). So, that said, it is clear to see that the Aggregate Add-In is not practical for use with a point map layer (nor line map layers, for that matter…line features are only 1-dimensional).
BUT, if your need is to 'aggregate' the point features of a point map layer in the ‘mathematical’ sense (that is, you don’t necessarily need a physical/geographic aggregation of the point features, but rather you need to aggregate their ‘numeric’ values, mathematically), then there is a ‘trick’ that you can employ to accomplish that. (Click here if you are unsure what I mean by ‘mathematical aggregation’)
How to ‘Aggregate Points’ - The way you aggregate a point map layer is to first buffer it slightly (if you are not sure what Buffering is, click here and read the section about Buffering in the the forum post that appears), and then aggregate the 'buffered' points. When you buffer the point features of a point map layer, you are essentially turning them in to ‘circular polygons’ (i.e. giving them length and width...thus 'area'). Regardless of their shape, circular or otherwise, they are still polygons and so you now have a polygon map layer that can be used with the Aggregate Add-In. You could buffer the points only just slightly, like a few hundred yards, or you can buffer them more, like tens of miles…it is up to you. But, know that there are some issues you will run into if you only buffer them very slightly (see the note* at the bottom of this post for suggestions on how large to buffer the points). Regardless of how large of a buffer you use around the points (1 meter or 100 miles)…the output of a buffer operation is still a new polygon map layer, and that is what is required when using the Aggregate Add-In…a polygon map layer.
Buffer Output – the resulting polygons from a buffering operation are still going to have all of the attributes of the input point layer…so, if your point map layer had an attribute called ‘Sales Q1 2008’…the resulting polygon features created by the buffer operation are also going to have a ‘Sales Q1 2008’ attribute, and all of those values will have been carried over to the polygon map layer.
Aggregate the Buffer Output – Display the polygon map layer that resulted from the buffer operation. Start the ‘Aggregate Add-In’ from the ‘Add-Ins’ menu**, and step through the add-in to set up the aggregation. In Step 3 of the add-in, you will select the attributes for which you want to calculate summary statistics. Refer to the Help documentation from the Aggregate Add-In interface for more on how to do that.
Aggregation Results – Depending on whether or not the circular polygons (the features that were created by the buffering operation) actually touch each other, the features in the output of the aggregation may or may not be physically aggregated into ‘larger’ polygons. That is, if a given set of neighboring, circular polygons physically intersected/touch each other, AND if they shared the same value for the ‘Aggregate Attribute’ (see the Help documentation), then the output of the aggregation will show them as combined into one polygon. For any polygon (from the input layer) that was isolated, and not touching any of its neighbors before the aggregation, then it will also not touch anything after the aggregation process. BUT, regardless of whether the polygons physically touch each other or not, for any polygons that have a common value for the ‘Aggregate Attribute’, they are now all considered one polygon, even though they are physically separated features as you see them on the map. Pay attention to the 'Spatial Aggregation' section below for a visual demonstration of this.
Consider this series of images from just such a sample scenario:
Start with a Point Map Layer - This first image shows a point map layer displayed in AWhere (ignore the pink-colored background with black outline, that is displayed only for spatial reference to show that this is eastern Texas). How do you know it is a point map layer (other than the fact that it looks like a bunch of points on the map)? Look at the ‘treeview’ area to the left of the map window…see the map layer there named ‘Customer Sales by Type’…it has this icon next to it 
…that icon indentifies a ‘Point’ map layer.) This map layer cannot be Aggregated because it is a ‘Point’ map layer. Also (very important) note that this map layer has several attributes, including ‘Customer Number’, ‘Customer Type’, and ‘Sales 2007’.
Buffer Points to Create Polygons - This next image shows the result of the above point map layer having been buffered using a 5-mile (radius) buffer; you now have a ‘Polygon’ map layer. (How do you know it is a polygon map layer? They still look like point features.) Look again at the treeview area to the left of the map window…the map layer named ‘5 Mile Buffer of Customers’ is what you are seeing displayed on the map…note that it has this icon next to it 
…that icon indentifies a ‘Polygon’ map layer.) This map layer can be Aggregated because it is a ‘Polygon’ map layer.
Also, importantly, note that each of the three attributes were carried over to the new polygon map layer. The ‘Customer Type’ attribute is the one being displayed, with each circular polygon colored according to the value it holds for the ‘Customer Type’ attribute…A, B, C, or D. This attribute will be the ‘Aggregate Attribute’…the one upon which the Aggregation is based.
Spatial Aggregation - This next image shows the result of the above polygon map layer having been aggregated based upon the values of the ‘Customer Type’ attribute. Initially, the map may not appear much different from the above map…but look closely. For example, look at the three green 'circles' at the lower-right…note that they are now ‘merged’ together into one, elongated polygon, whereas previously, they were three separate (but over-lapping) circles. You will see several other such examples on the map if you look closely.
Now, you will also see instances where two over-lapping circles were not merged into one polygon (e.g. the green and pink circles near the top of the mapped area). Why did those not merge into one elongated polygon like the yellow one above them? Because they are a differing ‘Customer Type’…one is type ‘B’, and one is type ‘C’. Another example of this is the yellow circle next to the two merged pink circles at the lower-left of the mapped area.
Mathematical Aggregation > this is (probably) really what you are driving toward in a 'Point layer aggregation'. The spatial (geographic) aggregation is probably not the most important result when aggregating point layers; rather, it’s the mathematical aggregation of the values of one or more of the original point layer’s attributes that you are interested in. And, this all occurred along with the physical aggregation of the features (that is, if you designate a numeric calculation of the features' values when setting up the aggregation operation...that is an optional part of the Aggregation routine...and you would want to do that is this situation, as I did in this example).
So, what are the results? In the image above, note that the former ‘Sales 2007’ attribute is now named ‘Sales 2007 Sum’. This is because during the Aggregation operation, I set it up to calculate the ‘sum’ for that attribute. That is, for all of the ‘points’ (i.e. buffered circles) in a given ‘Customer Type’ group, their ‘Sales 2007’ values were summed. So, going into the aggregation operation, all nine (9) of the customers in Group C (the green circles) each had an individual ‘Sales 2007’ value…but the aggregation operation summed all of those nine values together, resulting in one ‘Sales 2007 Sum’ value for all nine of those green circles.
You might ask...'Even if a given green circle was not physically merged with any other green circles, was its numeric value still used in the mathematical aggregation of the 'Sales 2007 Sum' value for Group C?' Yes. Regardless of whether there is a physical/geographic aggregation of all of the features of a given group into one polygon, there is still a mathematical aggregation of the values from ALL of those features (in the group) into one summary value. So, each green polygon you see on the map above has the same ‘Sales 2007 Sum’ value now…they are ‘considered’ one polygon despite their physical separation.
Take the four blue (type ‘D’ customers) polygons on the map below, for example. Before the Aggregation, the ‘Sales 2007’ values for those (individually) were: 49440.50, 32370.51, 48443.91, and 48095.47, respectively. Upon aggregation, those values were summed, and now the one value for all of the four blue polygons is: 178350.39…all four blue circles are now considered one-in-the-same polygon (despite their physical separation), so if I click on any one of those blue circles in order to extract the ‘Sales 2007 Sum’…the value reported to me is 178,350.39 (see image below). I clicked on the bottom-most blue circle and got that result...and I would have gotten the same number reported to me had I clicked on the top-most blue circle, etc.
(*Note – If you buffer the points only slightly and, as a result, only create very small circular polygons, you may find it difficult to see them on the map later. You should buffer the points appropriately large enough for the scale at which you are working. For example, if you are working with a view of the entire Country, you might want to buffer the point layer with a buffer of maybe 10 miles…but if you are working at a smaller scale (e.g. working with a view of only a single State or County, for example) you might get away with buffering the points with only a one mile buffer or less. The idea here is to buffer the points only sufficiently large enough to be able to still see them when zoomed out to the maximum possible extent of your area of interest.)
(** If you have AWhere Professional, the Aggregate Add-In will have been included with your installation. If you have AWhere Express, if will not have been included, but you can download it from our website for free.)