Social vulnerability maps are difficult to validate. Social vulnerability analyses are excercises in forecasting which places or groups that will be most in need of assistance in case of a potentially catastrophic event. For validation, we really need a large event to happen, to see if our analyses were right. Because we do not want that to happen, we have to do with imperfect alternatives. In my country, I could probably use records from the insurance companies for the validation of a physical vulnerability analysis. Social vulnerability is more difficult, but see for example Fekete (2009) who uses an independent second data set (a past event) for validation, and Gall (2007) who reviews a number of social vulnerability indices for validation methods and I think she suggest some options.
I do not work with social modelling, but I would use old data and analyse them against the predictor variables (environmental and others). And based on this statistic I would create a predictive models and then used a second old dataset to validate, or rather evaluate, the predictive model. But that requires that the data used for happening is within the range of the data used for creating the model, if not you can not trust the predictive model if you do not fully trust the interpolation that is made.
You may use the historical data available in these mega cities. Also you may done a questionnaire survey for selected sites. Stratified sampling technique is best I think.
You can validate the physical vulnerabilities with help of toposheet or satellite images and kindly note that accuracy of the satellite image resolution matter if the map is very old.
Socio-economic data you can get it from government department and use the correlation & regression method to see the variation from field as well as the government data as part of validation.
This perhaps is a tough question. What type of vulnerability are you talking about? Is it Environmental vulnerability; Social/crime/poverty vulnerability etc.? If it's the former, then remote sensing tools might be useful while baseline socioeconomic surveys including cadastral would be appropriate for the latter.
You can choose two options. One is partitioning the existing data and validate the remaining data (which were not used for development map), or you can collect a fresh data and then you can validate. You may also work out the apparent, true and excess error in the context.