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

Table 2

Critical scores of each theme.

No.	ID	Critical score	No.	ID	Critical score
26	temperature	1.0000	18	coastal urban	0.3262
15	urban agglomeration	0.8994	16	green space	0.2226
11	economic development	0.7654	2	spatial pattern	0.2154
13	housing development policy	0.6528	24	landscape pattern	0.2012
17	surface change	0.6197	3	urban infrastructure	0.1993
9	population density	0.6014	6	agricultural land change	0.1835
8	remote	0.5052	20	cellular automata model	0.1624
4	urban planning management	0.5035	28	ecosystem service	0.1449
10	metropolitan region	0.5025	29	water quality	0.1182
27	urban carbon	0.4330	22	physical health	0.0796
7	urban sprawl	0.4210	5	scenario prediction	0.0386
1	urban expansion	0.4025	14	social cost	0.0000
21	climate change	0.3833	23	forest loss	0.0000
19	transportation emission	0.3778	25	flood risk	0.0000
12	distance variable	0.3565