Offshore Island Connection Line: A new perspective of coastal urban development boundary simulation and multi-scenario prediction

Figures & data

Figure 1. Distribution types of the Offshore Island Connection Line (OICL).

Figure 2. Research boundary of the study area (the Jinpu New Area).

Figure 3. Research framework.

Table 1. Data source and description

Data type	Date of acquisition	Data	Resolution/scale	Sources
Raster data	17 September 2000	LANDSAT-5(TM)	30 m	Geospatial data cloud of computer network information center of Chinese Academy of Sciences (http://www.gscloud.cn)
	27 September 2010	LANDSAT-5(TM)
	20 June 2020	LANDSAT-8(OLI)
	2009	GMRT DEM	30 m/100 m	Global Multi-Resolution Topography Synthesis(https://www.gmrt.org/GMRTMapTool)
Vector data	2009, 2019	Land use survey data	1:10,000	Dalian Natural Resources Bureau
	2014	Three types of space (urban space, agricultural space and ecological space)	1:20,000	Dalian main functional area planning
		Ecological red line delimitation scheme
		Agricultural land
		Urban built-up areas
		Planning coastal reclamation areas
	2020	Coastline	1:50,000	Dalian administrative zoning map and Google Earth update
		Road transport network
		Administrative division

Figure 4. Land use classification in 2000, 2010, and 2020 under traditional and OICL perspectives (2010 coastline as reference boundary).

Table 2. Land use classification

Category	Describe
Urban land	Contains urban residential, industrial parks, urban facility land, forests (less than 1 km^2), excluding coastal salt fields, beaches
Ecological forest land	Forests (above or equal 1 km^2), orchard with ecological forest value
Cultivated land	Cultivated land, including facility agriculture, rural settlements, some orchards (new planting), etc.
Sea and land water body	Contains oceans, beaches, enclosures, land reservoirs, rivers

Figure 5. Spatial distribution of eight influencing factors under traditional and OICL perspectives (see Table 3 for the meanings of the labels and units).

Figure 6. Boxplots of eight influencing factors under traditional and OICL perspectives (see Table 3 for the meaning of the labels).

Table 3. Description of influencing factors

Type	Labels	Influence factor	Treatment method and description	Probability change curve
Naturalenvironment	Altitude	Altitude	ArcGIS raster to point tool and excel logistic regression method, raster data is processed into point set. The greater the altitude (H), the less likely it is to be selected as construction land . Collect and sort 21 samples corresponding to the construction land conversion possibility and altitude (see supplementary materials), and fit the following exponential equation:${\mathrm{P}}_{\mathrm{u}\mathrm{r}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{\_}\mathrm{H}}=1/\left(1+{\mathrm{e}}^{-\left(3.4255-0.0177\mathrm{H}\right)}\right)$Using this equation, the conversion probability of construction land at any altitude is calculated.
	Slope	Slope	ArcGIS raster to point tool and excel logistic regression method, raster data is processed into point set. The greater the slope (S), the less likely it is to be selected as construction land (${\mathrm{P}}_{\mathrm{u}\mathrm{r}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{\_}\mathrm{S}}$). Collect and sort 21 samples corresponding to the construction land conversion possibility and slope (see supplementary materials), and fit the following exponential equation:${\mathrm{P}}_{\mathrm{u}\mathrm{r}\mathrm{b}\mathrm{a}\mathrm{n}\mathrm{\_}\mathrm{S}}=1/\left(1+{\mathrm{e}}^{-\left(3.1804-0.1035\mathrm{S}\right)}\right)$Using this equation, the conversion probability of construction land at any slope is calculated.
	Dis2CL	Distance to coastline	ArcGIS multi-loop buffer analysis, within 1 km on both sides of the coastline, the conversion probability is set to 0.6, each outward expansion of 2 km, the conversion probability decreases 0.1, from the coastline 9–17 km probability is 0.1. Planning reclamation areas completely open conversion, set to 1, beach water part can be converted, set to 0.65, land water is not allowed to occupy, set to 0.001.
Infrastructure	Dis2UBA	Distance to urban built-up areas	ArcGIS multi-loop buffer analysis, within 1 km outside the urban built-up areas, the conversion probability is set to 0.8, each outward expansion of 2 km, the conversion probability decreased 0.2, from the edge of the urban built-up areas 7–11 km probability is 0.1. The probability of existing urban built-up areas is 1, and the infrastructure coverage area is 0.9.
	Dis2ETR	Distance to expressways and trunk roads	ArcGIS multi-loop buffer analysis, the probability within the range of 0.2 km from high speed and main road is set to be 1, 0.8 within 0.2–0.5 km, 0.6 within 0.5–1 km, 0.4 within 1–2 km, 0.2 within 2–3 km, 0.1 within 3–4 km, 0.01 within 4–5 km, 0.001 within 5–6 km, and 0.
Planning restrictions	Dis2ERL	Distance to ecological red line	ArcGIS multi-loop buffer analysis, the probability of 100 meters outside the ecological red line is 0.01, 0.1 within 100–200 meters, each outward expansion of 100 meters, the conversion probability increases by 0.2, the probability of 500 meters away from the ecological red line is 1, and the ecological red line area is set to 0.
	LU	Land use	ArcGIS grid calculator, other land types completely open conversion, general garden, woodland, cultivated land permit conversion, basic farmland in principle does not permit conversion, set other land 1, garden 0.7, woodland 0.4, cultivated land 0.3, basic farmland 0.001.	– –
	TTS	Preliminary division scheme of three types of space	ArcGIS grid calculator, unchanged urban space, low conversion of agricultural space, strict control of ecological space, land urban space 1, sea urban space 0.8, land and sea agricultural space 0.3, land and sea ecological space 0.2.	– –

Figure 7. Real and simulated land use under traditional and OICL perspectives in 2020.

Table 4. Classification accuracy evaluation

Perspective	Overall accuracy	Kappa	FoM
Traditional perspective (sea–land boundary)	75.42%	0.601	0.128
OICL perspective	83.57%	0.768	0.172

Figure 8. Setting of the land use conversion matrix for HID, ESP, and EEB scenarios.

Figure 9. The 2035 land use patterns and the composition of new urban land under HID, ESP, and EEB scenarios.

Table 5. Comparison of landscape pattern index of each scenario

Scenario	Type	AREA_AM	FRAC_AM	PARA_CV	ENN_AM	TECI
HID	Urban land	14,508.49	1.14	78.65	354.56	65.54
	Ecological forest land	1901.54	1.12	67.57	358.65	83.63
	Cultivated land	71,524.06	1.23	47.65	213.24	73.44
	Sea-land water body	37,044.12	1.15	50.36	213.15	68.67
ESP	Urban land	12,919.88	1.16	80.11	315.48	68.96
	Ecological forest land	1934.21	1.12	57.51	320.28	82.42
	Cultivated land	64,318.13	1.22	45.92	212.38	74.51
	Sea-land water body	38,197.32	1.16	47.05	211.01	70.54
EEB	Urban land	11,916.54	1.15	79.14	303.21	68.14
	Ecological forest land	2576.13	1.13	58.53	310.38	82.13
	Cultivated land	65,679.15	1.23	47.00	223.71	74.38
	Sea-land water body	36,946.11	1.15	49.06	211.15	68.74

Figure 10. Area and proportion of land use types in 2020 and three scenarios of 2035.