Today, nearly every smartphone can provide a network location alongside the traditional method of using the Global Navigation Satellite System (GNSS). The Android location engine is made up of two primary location providers: the GPS Location Provider (GLP), and the Network Location Provider (NLP). The recommended location service in Android is the Fused Location Provider (FLP), a location application interface (API) from Google which combines information from the GLP and NLP. The FLP is intended to be the ultimate positioning solution in Android, but it has been observed that in instances of GNSS spoofing, the FLP will trust the spoofed signals even in cases where the NLP provides a reliable estimate for the position of the device. In this paper, we describe the methods of the NLP, which include Wi-Fi fingerprinting, or scanning for visible Wi-Fi access points to build a radio map that can be used by devices to provide an estimated location. Furthermore, we test the accuracy of the NLP in the following scenarios: indoor, residential, urban, and canyon. Also, when available, we compare the NLP location to truth in order to assess the potential of the NLP in providing a reliable positioning solution and detecting the presence of GNSS spoofing. In addition, a correlation analysis between the accuracy of the NLP and the number of available Wi-Fi access points is carried out. This study is an assessment of the current accuracy and performance of NLP in Android devices. However, it is important to note that the performance of the NLP will only improve with the advent of 5G technology and Wi-Fi RTT Ranging. The growing network positioning infrastructure combined with the widespread availability of Wi-Fi access points will render the NLP more relevant than ever. The assessed accuracy of the NLP provides a deeper insight into its potential to aid in the detection and mitigation of GNSS spoofing.