A New Method for Identifying Key and Common Themes Based on Text Mining: An Example in the Field of Urban Expansion

Table 1

Keywords and theme naming.

No.	KW1	KW2	KW3	KW4	KW5	ID
1	expansion	urban	china	urbanization	city	urban expansion
2	growth	urban	spatial	pattern	model	spatial pattern
3	city	growth	urban	infrastructure	state	urban infrastructure
4	urban	plan	planning	growth	management	urban planning management
5	urban	scenario	future	growth	prediction	scenario prediction
6	land	change	agricultural	development	urban	agricultural land change
7	sprawl	urban	measure	study	index	urban sprawl
8	urban	study	remote	city	datum	remote
9	population	city	density	large	high	population density
10	metropolitan	region	urban	period	paper	metropolitan region
11	development	spatial	urban	pattern	economic	economic development
12	factor	urban	distance	variable	datum	distance variable
13	policy	development	housing	effect	local	housing development policy
14	public	social	cost	population	economic	social cost
15	urban	level	regional	agglomeration	beijing	urban agglomeration
16	space	development	green	indicator	paper	green space
17	datum	surface	image	change	map	surface change
18	urban	dynamic	analysis	coastal	result	coastal urban
19	transportation	effect	transport	emission	center	transportation emission
20	model	simulation	simulate	cellular	automaton	cellular automata model
21	change	increase	climate	effect	result	climate change
22	associate	health	form	location	physical	physical health
23	forest	increase	zone	urbanization	loss	forest loss
24	landscape	pattern	metric	soil	patch	landscape pattern
25	risk	flood	level	present	environment	flood risk
26	increase	effect	temperature	decrease	degree	temperature
27	urban	global	carbon	time	project	urban carbon
28	ecosystem	natural	service	loss	river	ecosystem service
29	water	impact	quality	environmental	concentration	water quality