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Empirical results show that spatial factors such as distance, population density and communication range affect our social activities, also reflected by the development of ties in social networks. This motivates the need for social network models that take these spatial factors into account. Therefore, in this paper we propose a gravity-low-based geo-social network model, where connections develop according to the popularity of the individuals, but are constrained through their geographic distance and the surrounding population density. Specifically, we consider a power-law distributed popularity, and random node positions governed by a Poisson point process. We evaluate the characteristics of the emerging networks, considering the degree distribution, the average degree of neighbors and the local clustering coefficient. These local metrics reflect the robustness of the network, the information dissemination speed and the communication locality. We show that unless the communication range is strictly limited, the emerging networks are scale-free, with a rank exponent affected by the spatial factors. Even the average neighbor degree and the local clustering coefficient show tendencies known in non-geographic scale-free networks, at least when considering individuals with low popularity. At high-popularity values, however, the spatial constraints lead to popularity-independent average neighbor degrees and clustering coefficients.