Recent outbreaks of vaccine-preventable diseases in the United States have drawn attention to the phenomena of vaccine hesitancy and refusal. Hesitancy is seen through the increasing use of exemptions from state vaccine mandates and the recent use of social media for expressing opinions and perspectives related to vaccination. This research places the vaccination narrative into a geographic context and seeks to understand the relationship between vaccine refusal in physical space and the vaccine discussion in cyberspace. Vaccines have long been considered an effective means of eradicating diseases. Recently, however, California has experienced a decline in vaccination rates and an increase in vaccine exemptions. Until the passing of Senate Bill 277 (SB277) in 2015, children were allowed by California law to skip immunizations if a parent submitted a personal beliefs exemption (PBEs). Under SB277, children who are not vaccinated cannot attend school. Some children are still allowed to skip immunizations by submitting a medical exemption (PMEs) at enrollment. Other children are conditionally admitted to school on the 'condition' that they complete any remaining vaccinations when due. This research analyzed the spatial distribution of vaccine exemptions in kindergarten schools in California using the 2015–2016 school immunization data. The two methods used for analysis included Kernel Density Estimation (KDE) and choropleth maps using data aggregated by county. The results from the choropleth maps show that personal belief exemptions for public, private, and charter kindergarten schools are highly concentrated in northern and rural counties. Aggregating vaccine exemptions at the county level and normalizing by school enrollment showed that counties with high ratios of vaccine exemptions vary across public, private, and charter schools. This research also explored the diffusion networks of the vaccine exemption topic in Twitter. Twitter messages related to the California vaccine exemption topic were collected for the whole United States. However, this research only focused on analyzing tweets in California. Two types of information diffusion networks, retweet network and mention network, were examined. This research quantified the influence of users in the networks by applying two network metrics—degree centrality and betweenness centrality. Degree centrality measures the number of connections of a node and is useful to asses which nodes are central for spreading information and influencing others in their immediate neighborhood. Betweenness centrality identifies brokers of information or nodes that connect disparate clusters. Nodes with high betweenness centrality have control over the flow of information in the network. The results suggest that influential users are ranked differently by degree centrality and betweenness centrality for both networks. The results showed that ordinary users may also have strong impacts in the diffusion of information as seen by their high betweenness values despite their low degree centrality. Retweets were found to be more prominent in the diffusion of the vaccine exemption topic compared to mentions. Social network analysis does not capture diffusion processes from a spatial perspective. This research included the spatial context of the mention and retweet networks by using the location information embedded in each node. Nodes were aggregated at the county level and social networks were transformed into visual maps with spatial context. In addition to spatial networks, this research also created chord diagrams to represent the outbound flow and interactions between counties. The findings suggest that county population plays a role in the diffusion of information by social media. Highly populated counties, such as Los Angeles and Sacramento provided a large amount of mention and retweet activity. Additionally, the mention and retweet spatial networks showed counties to have higher in-degree value than out-degree values which indicates more in-flow hubs than out-flow hubs in the network. Unlike the results from the inter-personal social networks, the mention and retweet networks showed that the counties with the highest degree centralities also resulted being the counties with the highest betweenness centrality. Highly populated counties, such as Los Angeles and Sacramento, had very high betweenness centralities in both retweet and mention activity, which means that they served as the bridge and information broker for spreading information related to the vaccine exemption topic. This research is important because most vaccine literature is written from an epidemiological perspective and lacks a geographical component. This research presented an example of applying the spatial social network concept for studying the interaction dynamics between geographic areas. This research expanded studying inter-personal diffusion networks by adding a spatial component. The objective of this research was to study vaccine exemption use and information diffusion across a cyber-physical space in means of better understanding the dynamics of public opinions, views, and responses to the vaccine exemption topic.