Here's the problem, as I see it, with point-wise mapping of information: when there are enough points, it becomes a forest and it's no longer useful. For example, here's the Ellis data all mapped together on SF using EditGrid, an online spreadsheet that can take any map data and map it with Google maps:
I would like to do is to have some interesting ways of clustering or displaying this information in a way that's not overwhelming visually and not a bear to produce in a GIS system. [Note to GIS system designers: your general purpose tools, which can be used for everything from prospecting for oil to identifying crime patterns, are incredibly difficult to use because they're so general purpose. Just because there's a latitude and longitude attached doesn't mean that the same tool is useful for everything. A nail and a can of peaches are both made of metal, but that doesn't mean you can or should use can openers and hammers interchangeably. Geographic information is like metal: it's a basic material, and the tools you use with it should be tuned to the task. But I digress.] Unfortunately, neither Liz or I was unable to find any such tools (EditGrid is relatively easy, but what I'd like is the equivalent of the Excel chart wizard for maps, and they're still in the early days of integrating their product with Google). We did what we could with what was available (in this case, Mapbuilder.net). Here's a still of all of the Ellis evictions from January 2000 to March 2005. You can start to see trends in both location clusters and in time and this tells a story about real estate in San Francisco. I'm not totally sure what story it tells, but it's a start, and an interesting mapping experiment to have spent an evening on.
Click on the map to get a live, zoomable, scrollable Google Map, but be warned: Google Maps isn't very good at displaying 500 points on a map and it'll bog down your browser for a while.
If you'd like to use the data yourself, here's my dated and geocoded spreadsheet, which we used the excellent and free Batch Geocode utility, which we also used to convert the dataset into a KML file for Google Earth.