Crowd-sourced crisis mapping is a relatively new phenomenon and platform that enables the collection and visualization of real-time crisis data submitted by users through social media tools and cellular technologies. Crisis maps are generally used by both state and nonstate actors for sense-making and as a reference point for action. The current crisis map visualizations only show the location documents such as reports or short messages have been generated from. Such a limited representation fails to immediately show important content, such as themes from a document and their changes over time. As a result, sense-making becomes time-consuming and cognitively demanding. I present a set of visualization tools: Geo-Temporal Tag Visualization (GTViz), Geo-Temporal Pies and Geo-SparkClouds that treat the tags on the crowd-sourced reports as spatio-temporal textual datasets and provide interaction tools to explore the content of the reports. I also demonstrate the value of such tools with case studies and a controlled user study.

The potential of Digital twin (DT) technology outside of the industrial field has been recognized by researchers who have promoted the vision of applying DTs technology beyond manufacturing, to purposes such as enhancing human well-being and improving quality of life (QoL). The expanded definition of DTs to incorporate living and nonliving physical entities into the definition of DTs was a key motivation behind the model introduced in this thesis for building health digital twins of citizens. In contrast with DTs that have been developed in more industrial fields, this type of digital twins modeling necessitates protecting each citizen's unique identity while also representing features common to all citizens in a unified way. In nature, DNA is an example of a model that is both unified, common to all humans, and unique, distinguishing each human as an individual. DNA’s architecture is what inspired us to propose a digital twin DNA (DT-DNA) model as the basis for building health DTs for citizens. A review of the literature shows that no unified model for citizens’ health has been developed that can act as a base for building digital twins of citizens while also protecting their unique identity thus we aim to fill this gap in this research. Accordingly, in this thesis, we proposed a DT-DNA model, which is specifically designed to protect the unique identity of each citizen’s digital twin, similar to what DNA does for each human. We also proposed a DT-DNA-based framework to build standardized health digital twins of citizens on micro, meso and macro levels using two ISO standards: ISO/IEEE 11073 (X73) and ISO 37120. To achieve our goal, we started by analyzing the biological DNA model and the influencing factors shaping health in smart cities. The purpose of the first is to highlight the DNA model features which provide the building blocks for our DT-DNA model. The purpose of the latter is to determine the main bases of our DT-DNA model of health DTs. Based on the analysis results; we proposed DT-DNA to model health DTs for citizens. In keeping with our DNA analogy, we have identified four bases, A, T, G, and C, for our unified and unique DT-DNA model. The A base in the proposed model represents a citizen’s anthropometric when we build the DT-DNA on an individual level and represents the city’s regulatory authorities when we build the DT-DNA on community and city levels. The T base represents different tasks included in the provided health data that are required to model citizens’ health DT-DNA on different levels. The G base represents the geographic and temporal information of the city, where the citizen exists at the time of data collection. The C base represents the context at the time of data collection. To proof the concept, we present our initial work on building health DTs for citizens in four case studies. The first two case studies are dedicated for health DTs at the micro level, the third case study is dedicated for health DTs at the meso level and the fourth case study is dedicated for health DTs at the macro level. In addition, we developed an algorithm to compare cities in terms of their community fitness and health services status. The four case studies provide promising results in terms of applicability of the proposed DT-DNA model and framework in handling the health data of citizens, communities and cities, collected through various sources, and presenting them in a standardized, unique model.