We construct reverse nearest neighbor heat maps [1] for many cities, which can be used for influence exploration.

In many cases, we need to determine the location, for example, for an ATM machine, a meet-up location of a group of friends, or for the next pick-up location of a taxi driver, so that the ATM machine can serve as many customers as possible, the meet-up location is convenient for many members, and the taxi driver can potentially make more money. In such a decision making process, there are many quantitative and qualitative factors to consider. One important quantitative factor is the distance to users and existing ATMs or taxis, which we will refer to as facilities. The distance to users indicates how many users the location will potentially attract, while the distance to existing facilities indicates the degree of competition.

An effective method to model the above quantitative factor is the reverse nearest neighbor of the location. There are many existing algorithms that can return the locations with the largest reverse nearest neighbor set. However, this implicitly assumes that this quantitative factor is the only factor we consider in the above decision making process, and it also assumes that this quantitative factor is only measured by the size of the RNN set, which is generally not true in many real applications. In fact, the quantitative factor could be any real-valued function on the RNN set. For example, the function can take into account the demographic information of the users in the RNN set, or consider social ties between these users, or model similar attributes of the facilities. In a real decision making process, apart from these quantitative factors, there are also many qualitative factors that cannot be effectively quantified or are subject to decision maker's judgments, such as the area safety or convenience of public transportation.

Therefore, we propose to build a heat map, like this, which shows the quantitative measure, which we call the influence, of each location, and a darker color means a higher influence. Such heat map allows the decision maker to explore the whole area while considering various qualitative factors. With a systematically built heat map, we can also support decision makers with many interactive operations like selecting out locations with influence above a given threshold.

[1] Yu Sun, Rui Zhang, Andy Yuan Xue, Jianzhong Qi, Xiaoyong Du. Reverse Nearest Neighbor Heat Maps: A Tool for Influence Exploration, Proceedings of the 31th IEEE International Conference on Data Engineering (ICDE) 2016.

ADELAIDE [138.4648,138.8589] [-34.7303, -35.0705]
ATHENS [23.6285, 23.8081] [37.9076, 38.0457]
BEIJING [116.0692, 116.6707] [39.6734, 40.1642]
CANBERRA [148.9465, 149.2767] [-35.1693, -35.4433]
DARWIN [130.8149, 130.8595] [-12.4299, -12.4730]
DETROIT [-83.1291, -82.9918] [42.2778, 42.3854]
HELSINKI [24.8394, 25.1731] [60.1022, 60.2739]
HOLLYWOOD [-80.2558, -80.1085] [25.9398, 26.0697]
MANDURAH [115.6064, 115.8522] [-32.4652, -32.6868]
MELBOURNE [144.7559, 145.0992] [-37.6634, -37.9339]
ROMA [12.4321, 12.5174] [41.8859, 41.9490]
SYDNEY [150.9364, 151.3078] [-33.7489, -34.0635]
TEHRAN [51.3388, 51.5152] [35.6082, 35.7473]
SHENZHEN [113.7971,114.0738] [22.4942,22.7471]