Continuously Generalizing Buildings to Built-up Areas by Aggregating and Growing

Abstract

To enable smooth zooming, we propose a method to continuously generalize buildings from a given start map to a smaller-scale goal map, where there are only built-up area polygons instead of individual building polygons. We name the buildings on the start map original buildings. For an intermediate scale, we aggregate the original buildings that will become too close by adding bridges. We grow (bridged) original buildings based on buffering, and simplify the grown buildings. We take into account the shapes of the buildings both at the previous map and goal map to make sure that the buildings are always growing. The running time of our method is in O(n 3 ), where n is the number of edges of all the original buildings.The advantages of our method are as follows. First, the buildings grow continuously and, at the same time, are simplified. Second, right angles of buildings are preserved during growing: the merged buildings still look like buildings. Third, the distances between buildings are always larger than a specified threshold. We do a case study to show the performances of our method.

Geographically Masking Addresses to Study COVID-19 Clusters

“…Our proposition is to dilate the building polygons to create building aggregates, using a standard morphological dilation, or buffer operation. The principle is similar to the method to derive a builtup area from building polygons (Boffet, 2000), or to the method for the continuous cartographic generalisation of urban areas (Peng & Touya, 2017). Figure 4 shows how the aggregates are created: when dilated buildings are close to each other, their dilated polygon intersect, so we just merge the dilated polygons that intersect each other to create the cells.…”

Missing the city for buildings? A critical review of pan-scalar map generalization and design in contemporary zoomable maps

“…The algorithms generate continuous transformations over a predetermined scale range. Continuous generalization algorithms are predominantly used in circumstances where morphing is not particularly efficient, such as with road selection [65,66], building aggregation [67], and administrative area aggregation [68,69]…”

More is less - Adding zoom levels in multi-scale maps to reduce the need for zooming interactions

“…But often, abstraction changes in a map are complex to model as a continuous transformation: for instance, the buildings are represented as individual polygons at large scales but are represented as solely built-up areas at smaller scales, and the continuous transformation between a set of polygons into their global footprint is not always understandable [60]. As a consequence, continuous generalization can also cause discrepancies in the abstraction changes and cannot completely solve the desert fog effects when zooming.…”