Tamed Wanderers: How AutoNavi Maps Reshapes Urban Spacetime and Individual Existence Through Algorithmic Governance

Abstract

Taking AutoNavi Maps（Amap）as its subject, this paper explores how algorithms reshape urban mobility and individual existence under the guise of prediction and optimization. The central question is: When navigation technology becomes the “default background” of daily actions, at what levels do algorithms participate in the governance of cities and bodies? This study reveals a contemporary shift in power genealogies—from territorial-centered sovereign governance to data-flow-centered generative governance. Through continuous data extraction and integration, AutoNavi transforms urban activities into raw material for algorithmic learning. Its predictive models preemptively intervene in the future, extending governance into unplayed scenarios. Simultaneously, the map's generative practices actively produce spatial order and commercial visibility through ranking and route recommendations. “Algorithmic time” further regulates individual rhythms and emotions, domesticating life within computable temporal frameworks. Research indicates that navigation algorithms not only shape the efficiency of mobility but also, in a subtle yet profound manner, restructure the perceptual framework and existential logic of urban experience.