A considerable amount of governmental agencies have sought to improve their inhabitants' quality of life in recent years, encouraging new sustainable technologies. The characterization of how these new technologies are disseminated in urban areas can help planners carry out campaigns or incentive policies that help achieve decarbonization goals. This paper presents a methodology based on fuzzy logic to characterize the spatiotemporal dynamics as to the adopters of photovoltaic systems in residential subareas. This characterization model proposes to demonstrate the influence that the first inhabitants, who adopted the technology of photovoltaic systems, can exert on the inhabitants of adjacent sub-areas. Considering that the degree of adherence to photovoltaic technology is related to subjective characteristics, the proposal uses three linguistic variables: low, medium, and high to model such adherence using fuzzy sets. The proposed methodology results present heat maps that identify the subareas with more inhabitants adopting photovoltaic systems. Hence, it has been applied in a medium-sized city in the state of São Paulo, Brazil. The obtained spatial distribution of photovoltaic adopters is heterogeneous, with subareas presenting medium and high values of photovoltaic adopters for each study year. The percentages of categorized subareas as high in the years 2017, 2018, 2019 are 8,3%, 19,3%, 19,7%, respectively. These results indicate that planners should promote more PV technology, especially in the city's outskirt subareas. A spatiotemporal estimation method and the history of recording installations of photovoltaic systems are used for comparison purposes. The comparison of results shows that the proposed method better characterizes the PV diffusion installed in the first years. This class of results can help planners better assess the diffusion goals of photovoltaic systems in urban areas, which are being used in the integrated planning of sustainable cities and provide information for planners to reach decarbonization goals.