A clear understanding of the demand patterns, is one of the key contributors to laying a firm foundation for tourist planning. In pursuit of that aim, we estimated the number of tourists at specific areas and times in Kyoto City using regression analysis and hierarchical linear models (HLM). We first discuss how to extract the tourists’ data from a “mesh population” obtained from aggregate mobile network operational data. We then propose that a relatively small sample of GPS tracking data for a population that has been monitored over a longer time than the mesh population can be used as a surrogate. To distinguish tourists from other persons, we find that a specified threshold of visiting a certain number of tourist attractions per day is useful. We also examine the effect of months and time of days by HLM on the model fit and number of tourists. Finally, we show that the accessibility of information such as the level of the attractiveness of particular Points of Interests (POIs) measured in terms of “Google ratings”, in conjunction with the GPS records significantly contributes to a better estimation of the number of tourists at specific areas and times in Kyoto City.