Sultana and colleagues make data reliability a spatial and situated question. Their study tests open-source models of sanitation flows against direct observation, key-informant interviews and focus-group discussion, finding substantial correspondence while also identifying phenomena that remote data cannot adequately capture. The iconic operation is triangulation across data regimes: modelled flow, observed drainage conditions, maintenance practices and local institutional knowledge are not treated as competing truths but as partial evidential layers. This produces a rigorous account of what open data can and cannot claim. Methodologically, validation becomes a process of returning abstract representation to the field and then revising the model through locally unavailable or unrecorded information. The wider bridge is to open science, GIS, critical data studies and urban infrastructure research. The paper demonstrates that public data becomes operationally trustworthy not through openness alone but through provenance, field correction and collaboration with institutions capable of supplying missing contextual records.
