Someone searching for a house should be able to go to the Realtors Neighborhood (which, potentially, may include companies scattered across the earth) but then narrow the view presented only to realtors in Portland. Failing to find a suitable realtor in Portland, the user should be able to ask the system to find realtors nearby. In this case, ``nearby'' means in the traditional geographic sense, but there are many ways to slice the same salami of data.
Locality can be defined in many ways: besides geographical (give me all British novelists), it could also be temporal (give me all novelist born in the same decade), or cultural (give me all English-speaking novelists), or physical (give me all female novelists), and so on. So instead of separating the Arts District, say, into novels, drama, criticism, and so on, it could be divided by century, by country of birth of the artists, by subject of the artistic piece. So, depending on whether the user happens to be interested in Spain, Pablo Picasso's Guernica could be right next to George Orwell's Homage to Catalonia.
This issue of variant definitions of locality depending on context is of more general consequence than merely improving websurfing. If we had a truly useful Yellow Pages, for example, it would list all businesses by each street, neighborhood, and mall; by the time needed to get to them from our current location; by whether they're in a safe neighborhood; by their relation to various landmarks; by whether they're currently having a sale; by whether they accept checks, cash, or credit; by their hours of operation; by their nearness to restaurants, gas stations, public restrooms, and malls; by their costliness, reliability, revenue, experience, and returns policy. Any and all of these dimensions of variation could be important to us at one time or another.
Businesses in the Yellow Pages could also be mapped based on what past customers have to say about them. Similarly, web locality may also be defined in terms of who tends to visit which site and in which order. If we already know something about the users of a neighborhood map then their peregrinations within the map can suggest what things should be related more strongly (or less so). For example, in a music or movie or paintings database, finding out that one user likes a certain set of artists could be used to modify the database's linkages between artists. It could also be used to give music recommendations to other users who happen to like some subset of those related artists. Here, the definition of locality is based purely on who likes whom.
There are enormous quantities of work on the web for human and artificial information organizers, so much so that the job of ``librarian'' may become one of the fastest growing job categories in the years ahead.