000			02088nab a2200277 4500
999			_c11642 _d11642
003			OSt
005			20210413150608.0
007			cr aa aaaaa
008			210413b ||||| |||| 00| 0 eng d
100			_aWang, Fahui _930414
245			_aMitigating the zonal effect in modeling urban population density functions by Monte Carlo simulation
260			_bSage, _c2019.
300			_aVol 46, Issue 6, 2019,(1061-1078 p.)
520			_aMost empirical studies indicate that the pattern of declining urban population density with distance from the city center is best captured by a negative exponential function. Such studies usually use aggregated data in census area units that are subject to several criticisms such as modifiable areal unit problem, unfair sampling, and uncertainty in distance measure. In order to mitigate these concerns associated, this paper uses Monte Carlo simulation to generate individual residents that are consistent with known patterns of population distribution. By doing so, we are able to aggregate population back to various uniform area units to examine the scale and zonal effects explicitly. The case study in Chicago area indicates that the best fitting density function remains exponential for data in census tracts or block groups, however, the logarithmic function becomes a better fit when uniform area units such as squares, triangles or hexagons are used. The study also suggests that the scale effect remain to some extent in all area units, and the zonal effect be largely mitigated by uniform area units of regular shape.
650			_aMonte Carlo simulation, _945877
650			_a modifiable areal unit problem, _945878
650			_ascale effect, _945879
650			_a zonal effect, _945880
650			_aurban population density function, _945881
650			_a Chicago _945882
700			_aLiu, Cuiling _945883
700			_aXu Yaping _945884
773	0		_011590 _915512 _dSage 2019. _t Environment and Planning B: Urban Analytics and City Science
856			_uhttps://doi.org/10.1177/2399808317749832
942			_2ddc _cART