Exposing the urban continuum: implications and cross-comparison from an interdisciplinary perspective

被引:16
|
作者
Uhl, Johannes H. [1 ]
Zoraghein, Hamidreza [2 ]
Leyk, Stefan [1 ]
Balk, Deborah [3 ,4 ]
Corbane, Christina [5 ]
Syrris, Vasileios [5 ]
Florczyk, Aneta J. [5 ]
机构
[1] Univ Colorado Boulder, Dept Geog, Guggenheim 110,260 UCB, Boulder, CO 80309 USA
[2] Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA
[3] CUNY, Inst Demog Res, New York, NY 10021 USA
[4] CUNY, Baruch Coll, New York, NY 10021 USA
[5] European Commiss, JRC, Ispra, Italy
基金
美国国家卫生研究院; 美国国家科学基金会;
关键词
Built-up surfaces; Global Human Settlement Layer (GHSL); Sentinel-1; Landsat; agreement assessment; urban modeling; cadastral parcel data; U; S; census data; LAND-COVER; PARCEL DATA; AREAL INTERPOLATION; ACCURACY; INFORMATION; SIZE;
D O I
10.1080/17538947.2018.1550120
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
There is an increasing availability of geospatial data describing patterns of human settlement and population such as various global remote-sensing based built-up land layers, fine-grained census-based population estimates, and publicly available cadastral and building footprint data. This development constitutes new integrative modeling opportunities to characterize the continuum of urban, peri-urban, and rural settlements and populations. However, little research has been done regarding the agreement between such data products in measuring human presence which is measured by different proxy variables (i.e. presence of built-up structures derived from different remote sensors, census-derived population counts, or cadastral land parcels). In this work, we quantitatively evaluate and cross-compare the ability of such data to model the urban continuum, using a unique, integrated validation database of cadastral and building footprint data, U.S. census data, and three different versions of the Global Human Settlement Layer (GHSL) derived from remotely sensed data. We identify advantages and shortcomings of these data types across different geographic settings in the U.S., which will inform future data users on implications of data accuracy and suitability for a given application, even in data-poor regions of the world.
引用
收藏
页码:22 / 44
页数:23
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