## 2 Methodology  
In the context of this paper, _things_ are defined as physical sensors or mobile phones with sensing equipment. Stationary measurements (e.g., from weather stations) are also included, as well as incidents of events at some particular physical location (e.g., crime or disease occurrence). Physical location is considered any location tagged with geographic coordinates (i.e., latitude and longitude) or an unambiguous place name that has a geographic footprint. It is assumed that geo-referenced objects have spatial properties, modeled as vector or raster data [16].  
We began our investigation with a keyword-based search for related work using _Web of Science_ and _Google Scholar_, as well as the Google search engine, using relevant keywords such as \"Internet of Things\", \"Pervasive Computing\", \"Geospatial Analysis\", and \"Geographical Information Systems (GIS)\", as well as combinations of these keywords, and various keywords of well-known analytical methods used in geoscience.  
We opted for this approach because the research domains of geographic information science and IoT are still relatively separated, with publication patterns focusing either on one direction or the other, but not on their intersection. Papers not written in English were excluded. Improvement of works presented by same authors were counted as one. Through this search, 97 papers were initially identified. Each paper, application, case study or initiative was checked for relevance based on its abstract or summary. By reading the abstract, we tried to identify if a paper complied to the following two criteria:  
1. Used an IoT infrastructure applied in some real-world application.
2. Used some geospatial analytical method or technique based on GIS, in order to address some geospace-related problem.  
For some papers, we needed to read through the whole paper to examine whether they were aligned with our criteria, because reading the abstract/summary alone was not enough.  
Papers that passed these two criteria were then categorized according to the \"Analytical Methods\" categorization as performed by the \"Geographic Information Science and Technology Body of Knowledge\" [17]. This categorization was selected because it represents well the methods employed for analysis in the related work identified. The seven specific categories selected are \"Geometric Measures\", \"Basic Analytical Operations\", \"Basic Analytical Methods\", \"Network Analysis\", \"Data Mining\", \"Analysisof Surfaces\", and \"Geostatistics\". The last two categories have been combined into one in Section 3, because of the similarity between interpolation and kriging as observed in the surveyed papers.  
Afterwards, each category was analyzed individually, examining all 97 papers identified at the previous stage for their relevance, by reading them in detail. By reading the papers thoroughly, the research questions as defined in Section 1 were addressed for each paper separately. From the 97 papers, 55 were considered highly relevant, i.e., belonged to the IoT research area and performed some meaningful geospatial analysis. The identified analytical methods and geospatial analysis techniques, together with the relevant IoT applications discovered for each technique, are presented in Section 3, while the remaining research questions are discussed in Section 4.