Journal of Geographical Sciences >
Regeneration patterns and drivers of different underutilized lands in the rust belt city of developing country: An empirical case study for Northeast China
Li Wenbo (1990-), Associate Professor, with a specialty in management and planning of land resources. E-mail: liwenbo@jlu.edu.c |
Received date: 2022-05-18
Accepted date: 2023-03-24
Online published: 2023-07-24
Supported by
National Natural Science Foundation of China(42001223)
Rust belt cities are largely threatened by a waste of urban space at their core; however, in developing countries where land resources are widely used as instruments for macroeconomic stabilization, urban periphery is also at risk of being underutilized due to land hoarding. Such geographic differences entail new knowledge about how, where, and why underutilized lands are regenerated in the city. Furthermore, rapid urban growth imposes development disparity and mixed underutilization issues on cities in developing countries; therefore, how the geo-information obtained by the regeneration of different underutilized lands differs will be valuable for urban planners and policymakers to make prudent trade-offs. To fill these gaps, we conducted a sequential investigation into the regeneration of underutilized lands in a representative rust belt city - Changchun City in Northeast China, in an attempt to measure the regeneration pattern and analyze the underlying determinants using the Classification and Regression Trees analysis. The results indicated that, of all underutilized lands, increments of vacant lot and remnant cultivated land continued to plague the expanding urban periphery during 2016-2019. In a way, reduced underutilized lands alleviated land use conflicts at the city core. Nearly 23% of the underutilized areas had been regenerated, dominated by realty development, with most converted to residential lands, ecological lands and industrial lands. On the contrary, conversion to transportation lands and parking lots seemed to avoid the rapidly expanding sites. The regeneration rates in a certain area can be increased by a multitude of factors, including denser, simply structured land underutilization, abundant ecosystem services nearby and accessibility to public infrastructures. Site conditions such as residential density and accessibility may have fueled the regeneration associated with residential purposes, while regeneration of industrial development was closely associated with the underutilization density and parcel regularity. This research provides an empirical paradigm for delivering regeneration geo-information across different underutilized lands, particularly for rust belt cities that are caught between a shrinking core and speculative periphery.
LI Wenbo , LI Han , YAN Zhuoran , HU Bingqing , ZHU Yuanli , YANG Yuewen , WANG Dongyan . Regeneration patterns and drivers of different underutilized lands in the rust belt city of developing country: An empirical case study for Northeast China[J]. Journal of Geographical Sciences, 2023 , 33(7) : 1377 -1396 . DOI: 10.1007/s11442-023-2134-6
Figure 1 Location of the study area (Changchun City, Northeast China). City loops are circular traffic arteries determined by urban planning. |
Figure 2 Regeneration types and interpretation marks of underutilized lands in the studied area: (a) regeneration to residential land (RL), (b) regeneration to industrial land (IL), (c) regeneration to ecological land (EL), (d) regeneration to parking lot (PL), and (e) regeneration to transportation land (TL). The photos before conversion were taken during verification in 2017 or 2018, while the photos after conversion were taken in 2020 or 2021. |
Table 1 Description of explanatory variables used for CART analysis |
Category | Explanatory variables | Description |
---|---|---|
Attributes of land parcel | Ave_SIZE, Ave_FRAC, UND_SI, UND_D | Ave_SIZE and Ave_FRAC represent the average value of the size and fractal dimension of an individual land parcel within the grid, respectively. UND_SI and UND_D represent the Simpson Index and density of underutilized lands in each grid. |
Site conditions | COM_D, RES_D, GBS_D, Road_D | The density of commercial lands, residential lands, green and blue space, and road networks. |
Accessibility to public infrastructures | D_BP, D_CR, D_HC, D_H, D_PL, D_PAR, D_SCH | Distance to bank and post office, commercial center, hospitals and clinics, hotels, parking lots, parks, and schools. |
Urban planning impacts | Plan_RES, Plan_PUB, Plan_IND, Plan_GBS | Proportion of planned residential area, public infrastructure area, industrial area, and green and blue space, which was derived from the Urban Planning for Changchun City from 2011 to 2020 |
Figure 3 Spatial patterns of underutilized land in the studied area in (a) 2016 and (b) 2019 |
Table 2 Statistics of underutilized lands in the city loops shown in the studied area from 2016 to 2019 (in ha) |
Time | Underutilized lands | City loops | In total | |||||
---|---|---|---|---|---|---|---|---|
Within the 1st | 1st-2nd | 2nd-3rd | 3rd-4th | 4th-5th | Other areas | |||
2016 | VL | 1.39 | 108.45 | 280.45 | 547.30 | 2213.77 | 1391.13 | 4542.49 |
UV | 0 | 4.11 | 93.20 | 326.34 | 887.02 | 839.12 | 2149.79 | |
AIL | 7.82 | 1.23 | 46.79 | 34.28 | 13.85 | 0.00 | 103.97 | |
RCL | 0 | 0.00 | 59.55 | 348.98 | 1382.75 | 966.00 | 2757.28 | |
2019 | VL | 1.39 | 74.45 | 174.36 | 510.92 | 1735.50 | 1311.55 | 3808.18 |
UV | 0.00 | 2.39 | 75.74 | 229.92 | 605.89 | 771.48 | 1685.42 | |
AIL | 0.81 | 1.23 | 34.52 | 18.56 | 0.00 | 14.05 | 69.17 | |
RCL | 0.00 | 0.00 | 57.78 | 295.80 | 1162.03 | 2020.64 | 3536.25 |
Table 3 Statistics for the regeneration of underutilized lands (in ha) |
Underutilized lands | Remain underutilized | Regenerated area | Increased area | Regeneration rate | ||
---|---|---|---|---|---|---|
Unconverted | to RCL | to VL | ||||
VL | 2706.08 | 285.84 | - | 1550.57 | 477.54 | 34.13% |
UV | 1685.42 | 0.47 | 396.62 | 67.27 | 0.00 | 3.13% |
AIL | 55.12 | 0 | 4.81 | 44.04 | 14.05 | 42.36% |
RCL | 2035.15 | 0 | 223.12 | 499.01 | 1214.79 | 18.10% |
Note: Conversion from one underutilized land category to another is still deemed as underutilized, and conversions to RCL and VL were the primary internal conversion between underutilized lands in the studied area. |
Figure 4 Sankey diagram indicating how the underutilized lands were regenerated. Only proportions over 5% were being labeled. |
Figure 5 Spatial distribution of regenerated underutilized lands: (a) quantitative changes, (b) regeneration to RL, (c) regeneration to IL, (d) regeneration to EL, (e) regeneration to PL, and (f) regeneration to TL. Lefever’s Standard Deviational Ellipse was applied to illustrate the distributional characteristics of regeneration pattern, with XSD representing the X standard distance, YSD representing the Y standard distance and R representing the rotation angle. |
Figure 6 CART analysis results for regeneration scale and types of underutilized land: (a) regression tree of regeneration scale and (b) decision tree of regeneration types. RE: Relative error, 1-RE: Prediction validity of the model, CP: Complexity parameter for total nodes, TVC: Total variable count for a tree, MCS: Minimum count of samples involved at any node, MSE: Mean square error, CP: Complexity parameter for total nodes, N: Number of observations, EL: Expected loss, where a higher EL indicates more mis-classification. NONE in the decision tree indicates the unconverted underutilized type. |
Figure 7 Correlation diagram of selected variables to build the CART model |
Figure 8 Old site of Changchun Tractor Manufacturing Plant, which remained underutilized in recent years: (a) and (b) were photographs taken by the authors in 2017 and 2020, respectively, while (c) and (d) are photos of interior graffiti taken by a social media influencer collected from the Internet. |
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