# Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
## 2 Fundamental Requirements of Smart Human Mobility
### Human Mobility Modeling and Prediction
#### 2.3.2 Models and Methods
For example, a GM describing the spatial interaction is used to predict migration between cities [78]. It was also used to predict the observed movement patterns to control malaria transmission in Africa. A study concluded that model movement rates between pairs of locations increase with population size and decrease with distance [79]. Other works also seek to understand what are the best parameters to use to obtain the best predictions of human mobility with this model. For example, [80] concluded that appropriate choices of input data and fitted parameters improves the predictive power of spatial interaction models like GM. According to [PERSON] [81], in the financial point of view, SEM adds the consumer's and the producer's surplus to provide a market equilibrium might be obtained by maximizing the net social payoff function (NSP). This model assumes that consumption and production in two different areas connected by a transportation network satisfy based on equilibrium two conditions:  
* With \(C\), the \(P\) is equal to the \(TC\) plus the \(SP\);
* Without \(C\), the \(P\) is lower than the \(TC\) plus \(SP\). where \(C\) is commercialization between pair of markets, \(P\) is price, \(TC\) is transportation cost and \(SP\) is supply price. Inspired model by SEM, [PERSON] analyzes how economic agents locate through space [82]. However, [83] improved this standard SEM. Besides the mobility is determined by preference shocks for locations, such as [PERSON] [84], he also moved costs that are origin-destination dependent. All these transportation science and spatial economics approaches are fundamental to predict human mobility fluxes between locations. As we mentioned, the DL algorithms of our work use large volumes of data to learn complex patterns about the movements of people. Their predictive models reveal if the entity is operating within acceptable parameters or is exhibiting an anomaly. Moreover, it requires diverse training data--collected across multiple time frames-to make algorithmic assessments more real-world and accurate. However, to broaden and enrich the correlations made by algorithms, it needs data from diverse open-source datasets. For the GM and SEM to be estimated, it is also necessary to merge databases from different sources. The large size of the typical database of a gravity model is due to the basic principle that even if the researcher is interested only in the factors influencing a particular trading relationship the effect should be measured based on a gravity equation estimated for all countries, not just the countries involved. In addition, these equations can be estimated for either cross-sections or panels of locations. Both machine learning and mathematical models researcher should choose the best attributes to apply in models, denying the involvement of the remaining variables of the dataset. Another difficulty related to building a database for these models that data from a variety of different data sources have to be merged in a single database. Since data may be available in different formats or classifications, the researcher needs to organise this information.