Census population data are associated with several analytical and cartographic problems. Regression models using remote‐sensing covariates have been examined to estimate urban population density, but the performance may not be satisfactory. This paper describes a kriging‐based areal interpolation method, namely area‐to‐point residual kriging, which can be used to disaggregate the residuals remaining from regression. Compared with conventional cokriging, the area‐to‐point residual kriging is much simpler in that only a semivariogram model for the point residuals is required, as opposed to a set of auto‐ and cross‐semivariogram models involving the dependent variable and all the covariates. In addition, area‐to‐point residual kriging explicitly accounts for any scale differences between source data and target values. The method is illustrated by disaggregating population from census units to the land‐use zones within them. Comparative results for regression with and without area‐to‐point residual kriging show that area‐to‐point residual kriging can substantially improve interpolation accuracy.

The cumulative error at a point in a LiDAR-derived DEM consists of three components: propagated LiDAR-sensor error, propagated ground error, and interpolation error. To combine these error components so as to assess the vertical accuracy of a LiDAR-derived DEM, statistical methods based on the error propagation theory are often used. Due to the existence of systematic error, statistical methods are only effective if a large number of checkpoints are available, which may not be affordable in many practical applications. This paper presents approximation theory as an alternative methodology that departs from error propagation theory in fundamental ways. Using approximation theory, an error bound of the cumulative error at any point in the study site can be obtained, thus informing users conservatively of the spatial variation of DEM accuracy and pointing out the weakly determined areas. The new method is illustrated from DEM users’ perspective by assessing whether a publicly available LiDAR-derived DEM meets FEMA’s accuracy standard for flood risk mapping. The paper calls for a change in the existing methods of assessing and reporting the errors in a LiDAR-derived DEM, in particular those introduced during the ground filtering process.

Areal interpolation is routinely used when spatial data are unavailable at desired geographical units. While many methods are available, few of them were developed specifically for and tested in highly developed urban cores. Even fewer studied subpopulation or population characteristics. This paper explores both issues using parcel map and decennial census data as ancillary information. Using census blocks as intermediate zones, the method first disaggregates source-zone data to intermediate zones, then disaggregates data to parcel level in intermediate zones intersecting target zones, and finally aggregates intermediate-zone and parcel-level estimates to obtain target-zone estimates. Compared to areal weighting and residential proportion, the proposed method is significantly more accurate. All three methods perform the best on population count, and worst on spatially clustered subpopulations such as black/African American population. Quotient variables are more difficult to interpolate than count variables. The research demonstrates the utility of parcel and decennial census data for areal interpolation in highly developed urban cores, and calls for future research on subpopulation and population characteristics.

Urban remote sensing provides an efficient and accessible way to monitor and assess the urban environment. However, the difficulty in classifying bare soil and built-up land is exacerbated in desert landscapes, due to the spectral confusion of bare soil and impervious surfaces. Therefore, urban remote sensing research in desert environments employs complex and time-consuming classification techniques, which cause difficulties in reliability when transferring these methods to other desert cities. This paper describes two new index-based approaches that can successfully detect and classify urban areas without the disruption of bare soil influences in desert environments using Landsat 8–9 satellite imagery. They are called the desert urban landscape index (DULI) and the isoline impervious surface index (IISI). The desert cities of Phoenix, Ciudad Juárez, and Riyadh were used as study areas for the development of these indices. The two proposed indices outperformed the dry built-up index (DBI), with overall accuracy rates of 85% in Phoenix using DULI, 87% in Ciudad Juárez using DULI, and 90% in Riyadh using IISI. DULI also demonstrates the ability to suppress landscape features such as bare soil, mountains, and canyons.