Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia

Effective government services rely on accurate population numbers to allocate resources. In Colombia and globally, census enumeration is challenging in remote regions and where armed conflict is occurring. During census preparations, the Colombian National Administrative Department of Statistics conducted social cartography workshops, where community representatives estimated numbers of dwellings and people throughout their regions. We repurposed this information, combining it with remotely sensed buildings data and other geospatial data. To estimate building counts and population sizes, we developed hierarchical Bayesian models, trained using nearby full-coverage census enumerations and assessed using 10-fold cross-validation. We compared models to assess the relative contributions of community knowledge, remotely sensed buildings, and their combination to model fit. The Community model was unbiased but imprecise; the Satellite model was more precise but biased; and the Combination model was best for overall accuracy. Results reaffirmed the power of remotely sensed buildings data for population estimation and highlighted the value of incorporating local knowledge

Estimación del orden en un modelo de cadena de markov oculta no homógeneo con presencia de co-variables

El presente documento muestra la estimación del orden o número de estados de la cadena, en un modelo en cadenas de markov ocultas no homogéneas usando la inferencia bayesiana. Para la estimación, se usa el método de Markov Chain Monte Carlo (MCMC), tal que la simulación se realiza de manera conjunta con los demás parámetros del modelo. Adicionalmente cada variable del proceso observado pertenece a la familia exponencial. El uso de esta metodología establece el modelo que mejor ajusta los datos. Estos valores son generados de distribuciones no pseudo a priori, obteniendo convergencia e independencia a un gran número de iteraciones.Abstract. This document shows the estimation of the order or number of states of the Chain, in a non homogeneous hidden markov model using the bayesian inference. For the estimation, we used the Markov Chain Monte Carlo method’s (MCMC) such that the simulation was performed in conjunction with the other parameters of the model, additionally each variable of the observed process belongs to the exponential family. The use of this method select the best model, This values was generated by the non pseudo prior distribution, obtaining convergence and not autocorrelation to a large number of iterations.Maestrí

Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia

Social cartography and remote sensing to support census. (modelled population estimates; population and housing census; GIS; remote sensing; Bayesian statistics; community engagement

Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia

Effective government services rely on accurate population numbers to allocate resources. In Colombia and globally, census enumeration is challenging in remote regions and where armed conflict is occurring. During census preparations, the Colombian National Administrative Department of Statistics conducted social cartography workshops, where community representatives estimated numbers of dwellings and people throughout their regions. We repurposed this information, combining it with remotely sensed buildings data and other geospatial data. To estimate building counts and population sizes, we developed hierarchical Bayesian models, trained using nearby full-coverage census enumerations and assessed using 10-fold cross-validation. We compared models to assess the relative contributions of community knowledge, remotely sensed buildings, and their combination to model fit. The Community model was unbiased but imprecise; the Satellite model was more precise but biased; and the Combination model was best for overall accuracy. Results reaffirmed the power of remotely sensed buildings data for population estimation and highlighted the value of incorporating local knowledge.</p