Maps are a necessity in people's lives. Map cognition research, a science of investigating perception, learning, memory, reasoning and decision making of maps, has long been an important issue in the foundation of cartography. As a core part of map cognition research, studies on visual cognition of maps have made a series of achievements promoted by advancements of related fields (e.g., psychology, cognitive science and computer vision) and new techniques (e.g., eye tracking, EEG and functional magnetic resonance imaging). Particularly, eye tracking approach has gained much attention, which has been used as a crucial method to investigate map cognition. This paper reviews the new progress of eye tracking based visual cognition of maps which have been made since 2008. We summarized these progresses in six aspects: (1) stimulus: from static maps to dynamic and interactive maps; (2) research extent: from map cognition to map-based spatial cognition; (3) experiment environment: from lab environment to real environment; (4) map dimension: from 2D to 3D; (5) individual differences: from single dimension to multiple dimensions; (6) research purpose: from theory exploration to application. We also summarized future research challenges, difficulties and possible solutions. We hope this review can serve as an initial effort to open more endeavor for future eye tracking investigations into map cognition.

This paper reveals the principle of geographic big data mining and its significance to geographic research. In this paper, big geodata are first categorized into two domains: earth observation big data and human behavior big data. Then, another five attributes except for "5V", including granularity, scope, density, skewness and precision, are summarized regarding big geodata. Based on this, the essence and effect of big geodata mining are uncovered by the following four aspects. First, as the burst of human behavior big data, flow space, where the OD flow is the basic unit instead of the point in traditional space, will become a new presentation form for big geodata. Second, the target of big geodata mining is defined as revealing the spatial pattern and the spatial relationship. Third, spatio-temporal distributions of big geodata can be seen as the overlay of multiple geographic patterns and the patterns may be changed with scale. Fourth, big geodata mining can be viewed as a tool for discovering geographic patterns while the revealed patterns are finally attributed to the outcome of human-land relationship. Big geodata mining methods are categorized into two types in light of mining target, i.e. classification mining and relationship mining. The future research will be facing the following challenges, namely, the aggregation and connection of big geodata, the effective evaluation of mining result and mining "true and useful" knowledge.