Assessing housing growth when census boundaries change

Abstract

The US Census provides the primary source of spatially explicit social data, but changing block boundaries complicate analyses of housing growth over time. We compared procedures for reconciling housing density data between 1990 and 2000 census block boundaries in order to assess the sensitivity of analytical methods to estimates of housing growth in Oregon. Estimates of housing growth varied substantially and were sensitive to the method of interpolation. With no processing and areal-weighted interpolation, more than 35% of the landscape changed; 75-80% of this change was due to decline in housing density. This decline was implausible, however, because housing structures generally persist over time. Based on aggregated boundaries, 11% of the landscape changed, but only 4% experienced a decline in housing density. Nevertheless, the housing density change map was almost twice as coarse spatially as the 2000 housing density data. We also applied a dasymetric approach to redistribute 1990 housing data into 2000 census boundaries under the assumption that the distribution of housing in 2000 reflected the same distribution as in 1990. The dasymetric approach resulted in conservative change estimates at a fine resolution. All methods involved some type of trade-off (e.g. analytical difficulty, data resolution, magnitude or bias in direction of change). However, our dasymetric procedure is a novel approach for assessing housing growth over changing census boundaries that may be particularly useful because it accounts for the uniquely persistent nature of housing over time.

Rapid growth of the US wildland-urban interface raises wildfire risk

“…of WUI growth. The decennial US Census provides fine-resolution housing data for 1990, 2000, and 2010, but the boundaries of the smallest units for which housing units are reported (i.e., census blocks) often shift between decades, precluding direct change analyses (27). We have developed algorithms to convert the decennial Census data at census block resolution into a consistent dataset on housing growth across the conterminous United States (Methods), which we combined with 1992, 2001, and 2011 National Land Cover Data (NLCD) on wildland vegetation: forests (classes 41-43), shrublands (classes 51 and 52), grasslands (class 71), and woody wetlands (class 90).…”

Twenty-first century California, USA, wildfires: fuel-dominated vs. wind-dominated fires

“…3 (a) 1964 Hanly Fire perimeter (purple line) with low and high density housing distribution, and (b) 2017 Tubbs Fire perimeter (purple line) with housing distribution. Housing density data were spatially distributed and mapped using methods described in Hammer et al 2004 andSyphard et al 2009. Within the attributes of partial block groups, all areas designated as having housing density between 6.17 to 49 houses per square kilometer were mapped as low density, with 6.17 corresponding to the minimum housing density cutoff for low-density wildland-urban interface (WUI; Radeloff et al 2005).…”

Exploiting temporal information in parcel data to refine small area population estimates

“…The problem of incompatible enumeration units in temporal analysis has recently found increasing interest, with much of this work focusing on methods of areal interpolation (Logan, Xu, & Stults, 2014;Schroeder & Van Riper, 2013). These methods sometimes rely on assumptions concerning the spatial distribution of the population (or other demographic variable of interest) that do not hold true in many situations (Syphard et al, 2009).…”