Rural settlement spatial patterns and effects:Road traffic accessibility and geographic factors in Guangdong Province, China

doi:10.1007/s11442-019-1593-2

Abstract

Abstract:

Based on land use classification data of remote sensing images, using kernel density, the minimal cumulative resistance model of road traffic accessibility, and a logistic regression model, the characteristics of the spatial pattern and the main factors influencing it were quantitatively examined in Guangdong Province from 1990 to 2013. The framework of the research concerning rural settlement evolution and its effect mechanisms were also discussed and generalized for the future. The results are as follows: (1) The spatial distribution of rural settlements showed spatial directivity of low altitude, low slope, and adjacent to rivers, as well as to villages and towns; thus a special pattern was formed, which was dense on the plains, sparse in mountainous areas, and included two core high density regions of rural settlements in the Chaoshan plain in the east and the Zhanjiang plain tableland region in the west. The spatial distribution of rural settlements was located along the rivers, valleys, and roads with traffic in the mountainous regions surrounding the Pearl River Delta region. (2) In addition to the spatial orientation of the open road, it was important to show that the accessibility of road traffic to the township has had the greatest influence on the spatial distribution of the rural settlements. The connected transport network between towns and villages is significant for rural transformation as a comprehensive increase in township production and service capacity will be the key to optimizing the town-village system in rural areas. (3) Elevation and slope were two basic but influential factors that have affected the distribution, scale, and form of rural settlements. The attributes of the physical geography are the first elements in optimizing village layout and planning spatial reconstruction. (4) In the current Internet and social media era, the reconstruction of market network system orders connects with the global market network system in rural areas. The rural life service circle will be constructed with the township at its core to explore the theory and practice of spatial reconstruction, including its production, life and ecology, and socio-cultural heritage and protection. It will also allow for exploration of the rural settlements’ evolution, rural spatial production, rural social networks, group behavior, social autonomy, and social and cultural fields, which will be the core focus of China’s rural spatial reconstruction research against a background of globalization.

Key words: rural settlement, spatial feature recognition, traffic accessibility, rural geography

Ren YANG, Qian XU, Xuanfang XU, Yanchun CHEN. Rural settlement spatial patterns and effects:Road traffic accessibility and geographic factors in Guangdong Province, China[J].Journal of Geographical Sciences, 2019, 29(2): 213-230.

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Resistance value to prefecture-level cities(×10⁴)	Number	Percentage (%)	Area (ha)	Percentage (%)	Resistance value to counties (×10⁴)	Number	Percentage (%)	Area	Percentage (%)
Resistance value to prefecture-level cities(×10⁴)	Number	Percentage (%)	Area (ha)	Percentage (%)	Resistance value to counties (×10⁴)	Number	Percentage (%)	(ha)	Percentage (%)
<20	123183	24.81	282599.8	39.46	<10	153337	30.87	315588.46	44.06
20-30	124256	25.02	165219.2	23.07	10-15	162224	32.66	215535.62	30.09
30-40	106480	21.44	128698.7	17.97	15-20	110236	22.19	116102.82	16.21
40-50	80260	16.16	72256.98	10.09	20-25	56121	11.3	54703.57	7.64
>50	62419	12.57	67383.22	9.41	>25	14863	2.99	14355.27	2
Resistance value to towns (×10⁴)	Number	Percentage (%)	Area (ha)	Percentage (%)
<4.5	236972	47.58	342533.842	47.62
4.5-9.0	183301	36.81	267395.335	37.18
9.0-13.5	59388	11.93	86747.7314	12.06
13.5-18.0	14326	2.88	17894.74635	2.49
>18.0	4013	0.81	4669.517706	0.65

Elevation (m)	Number	Percentage (%)	Area (ha)	Percentage (%)	Slope (°)	Number	Percentage (%)	Area (ha)	Percentage (%)
<100	279634	55.99	562847.5	77.74	<5	194421	38.93	382235.6	52.82
100-300	167842	33.61	130262.92	17.99	5-10	145839	29.2	214827.7	29.69
300-500	38177	7.64	22833.94	3.15	10-15	75585	15.14	73520.49	10.16
500-700	11064	2.22	6315.56	0.87	15-25	65703	13.16	43263.33	5.98
>700	2707	0.54	1396.14	0.19	>25	17852	3.57	9801.06	1.35

Distance (km)	Number	Percentage (%)	Area (ha)	Percentage (%)	Annual average temperature (°C)	Number	Percentage (%)	Area (ha)	Percentage (%)
<10	262033	52.60	386671.2	53.40489	<16	4637	0.93	3337.59	0.47
10-20	149341	29.98	179346.4	24.77033	16-17	49352	9.92	46745.21	6.51
20-30	72714	14.60	107934.2	14.90727	17-18	152311	30.62	126434.36	17.62
30-40	8093	1.62	20802.5	2.873127	18-19	175543	35.29	286322.48	39.90
>40	5979	1.20	24857.18	3.433137	>19	115614	23.24	254841.83	35.51