Mixed land use (MLU) is a pivotal strategy for sustainable urban development, but accurate measurement remains challenging. By combining multi-source geospatial data, such as land use, Points of Interest (POI), and cellular signaling data, this study creates a novel approach for MLU assessment. A method for urban function zoning based on MLU is suggested, and a theoretical framework taking into account spatial scale, morphology, functional relationships, and quantity is established. Applying the approach to the central urban area of Xuzhou, China, the findings indicate that (1) the proposed method integrates the advantages of multi-source data, captures the essential characteristic of MLU, and provides a comprehensive and accurate measurement of urban MLU. (2) The observed pattern of decreasing MLU from the city center to the periphery is attributed to the varying location requirements of different industries and their associated land-use characteristics. (4) Applying MLU in urban functional zoning can enhance the scientific basis of zoning, with spatial regulation to cultivate composite functions, contributing to urban vitality and capacity. This study demonstrates the value of multi-source data in urban MLU assessment and promotes the practical application of MLU in urban planning.
AbdullahiSPradhanBMansorS, et al. (2015) GIS-Based modeling for the spatial measurement and evaluation of mixed land use development for a compact city. GIScience and Remote Sensing52(1): 18–39. https://doi.org/10.1080/15481603.2014.993854
Carpio-PinedoJBenito-MorenoMLamiquiz-DaudénPJ (2021) Beyond land use mix, walkable trips. An approach based on parcel–level land use data and network analysis. Journal of Maps17(1): 23–30. https://doi.org/10.1080/17445647.2021.1875063
5.
ChinWCBFuYLimKH, et al. (2023) Identifying urban functional zones by analysing the spatial distribution of amenities. Environment and Planning B: Urban Analytics and City Science51(6): 1274–1289. https://doi.org/10.1177/23998083231217376
6.
CliftonKEwingRKnaapG, et al. (2008) Quantitative analysis of urban form: a multidisciplinary review. Journal of Urbanism: International Research on Placemaking and Urban Sustainability1(1): 17–45. https://doi.org/10.1080/17549170801903496
7.
CrivellariAReschB (2022) Investigating functional consistency of mobility-related urban zones via motion-driven embedding vectors and local POI-Type distributions. Computational Urban Science2(1): 19. https://doi.org/10.1007/s43762-022-00049-8
8.
Du PlessisDJ (2015) Land-use mix in South African cities and the influence of spatial planning: innovation or following the trend?South African Geographical Journal97(3): 217–242. https://doi.org/10.1080/03736245.2014.924870
9.
EomSSuzukiTLeeM-H (2020) A land-use mix allocation model considering adjacency, intensity, and proximity. International Journal of Geographical Information Science34(5): 899–923. https://doi.org/10.1080/13658816.2019.1687899
10.
FoordJ (2010) Mixed-use trade-offs: how to live and work in a compact city neighbourhood. Built Environment36(1): 47–62. https://doi.org/10.2148/benv.36.1.47
GehrkeSRCliftonKJ (2015) Toward a spatial-temporal measure of land-use mix. Journal of Transport and Land Use9(1): 725. https://doi.org/10.5198/jtlu.2015.725
13.
GehrkeSRCliftonKJ (2017) An activity-related land use mix construct and its connection to pedestrian travel. Environment and Planning B: Urban Analytics and City Science46(1): 9–26. https://doi.org/10.1177/2399808317690157
HajnaSDasguptaKJosephL, et al. (2014) A call for caution and transparency in the calculation of land use mix: measurement bias in the estimation of associations between land use mix and physical activity. Health & Place29(9): 79–83. https://doi.org/10.1016/j.healthplace.2014.06.002
16.
HeJLiXLiuP, et al. (2021) Accurate estimation of the proportion of mixed land use at the street-block level by integrating high spatial resolution images and geospatial big data. IEEE Transactions on Geoscience and Remote Sensing59(8): 6357–6370. https://doi.org/10.1109/tgrs.2020.3028622
17.
HoppenbrouwerELouwE (2005) Mixed-use development: theory and practice in Amsterdam's eastern docklands. European Planning Studies13(7): 967–983. https://doi.org/10.1080/09654310500242048
18.
HuJGaoYWangX, et al. (2023) Recognizing mixed urban functions from human activities using representation learning methods. International Journal of Digital Earth16(1): 289–307. https://doi.org/10.1080/17538947.2023.2170482
19.
HuQShenWYanJ, et al. (2024) Does existing mixed land development promote the urban spatial composite function? Evidence from beijing, China. Land Use Policy143(3): 107212. https://doi.org/10.1016/j.landusepol.2024.107212
20.
ImHNChoiC (2019) Measuring pedestrian volume by land use mix: presenting a new entropy-based index by weighting walking generation units. Environment and Planning B: Urban Analytics and City Science47(7): 239980831882411. https://doi.org/10.1177/2399808318824112
21.
JacobsJ (1961) The Death and Life of Great American Cities. New York: Random House.
22.
KantorYRietveldPvan OmmerenJ (2014) Towards a general theory of mixed zones: the role of congestion. Journal of Urban Economics83(9): 50–58. https://doi.org/10.1016/j.jue.2014.08.003
23.
LenormandMPicornellMCantu-RosOG, et al. (2015) Comparing and modelling land use organization in cities. Royal Society Open Science2(12): 150449. https://doi.org/10.1098/rsos.150449
24.
LiXMaXHuZ, et al. (2021) Investigation of urban green space equity at the city level and relevant strategies for improving the provisioning in China. Land Use Policy101(2): 105144. https://doi.org/10.1016/j.landusepol.2020.105144
25.
LiuXNiuNLiuX, et al. (2018) Characterizing mixed-use buildings based on multi-source big data. International Journal of Geographical Information Science32(4): 738–756.
26.
LiuHXuYTangJ, et al. (2020) Recognizing urban functional zones by a hierarchical fusion method considering landscape features and human activities. Transactions in GIS24(5): 1359–1381. https://doi.org/10.1111/tgis.12642
27.
MotieyanHAzmoodehM (2021) Mixed-use distribution index: a novel bilevel measure to address urban land-use mix pattern (A case study in Tehran, Iran). Land Use Policy109(1): 105724. https://doi.org/10.1016/j.landusepol.2021.105724
28.
MusakwaWvan NiekerkA (2013) Implications of land use change for the sustainability of urban areas: a case study of stellenbosch, South Africa. Cities32(5): 143–156. https://doi.org/10.1016/j.cities.2013.01.004
29.
ShiBYangJ (2015) Scale, distribution, and pattern of mixed land use in central districts: a case study of nanjing, China. Habitat International46(3): 166–177. https://doi.org/10.1016/j.habitatint.2014.11.008
30.
ShiHZhaoMSimthDA, et al. (2022) Behind the land use mix: measuring the functional compatibility in urban and sub-urban areas of China. Land11(1): 2. https://doi.org/10.3390/land11010002
TaleaiMSharifiASliuzasR, et al. (2007) Evaluating the compatibility of multi-functional and intensive urban land uses. International Journal of Applied Earth Observation and Geoinformation9(4): 375–391. https://doi.org/10.1016/j.jag.2006.12.002
TuYChenBLangW, et al. (2021) Uncovering the nature of urban land use composition using multi-source open big data with ensemble learning. Remote Sensing13(21): 4241. https://doi.org/10.3390/rs13214241
35.
Valle PiliaMRamirez FonsecaF (2024) The influence of the urban model on civic involvement and public time. A study applied to the commuting population of the greater metropolitan area of San Jose, Costa Rica. Cities148(2): 104849. https://doi.org/10.1016/j.cities.2024.104849
36.
WandlAHausleitnerB (2021) Investigating functional mix in Europe's dispersed urban areas. Environment and Planning B: Urban Analytics and City Science48(9): 2862–2879. https://doi.org/10.1177/2399808320987849
37.
WangZLuFLiuZ, et al. (2022) Measuring spatial nonstationary effects of POI-Based mixed use on urban vibrancy using bayesian spatially varying coefficients model. International Journal of Geographical Information Science37(4): 1–21. https://doi.org/10.1080/13658816.2022.2117363
38.
WuLChengXKangC, et al. (2018) A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data. International Journal of Digital Earth13(4): 1–19. https://doi.org/10.1080/17538947.2018.1556353
39.
WuMHuangQGaoS, et al. (2023) Mixed land use measurement and mapping with street view images and spatial context-aware prompts via zero-shot multimodal learning. International Journal of Applied Earth Observation and Geoinformation125(1): 103591. https://doi.org/10.1016/j.jag.2023.103591
40.
XueBXiaoXLiJ, et al. (2023) Multi-source Data-driven identification of urban functional areas: a case of Shenyang, China. Chinese Geographical Science33(1): 21–35. https://doi.org/10.1007/s11769-022-1320-2
41.
YamadaLBrownBBSmithKR, et al. (2012) Mixed land use and obesity: an empirical comparison of alternative land use measures and geographic scales. The Professional Geographer64(2): 157–177. https://doi.org/10.1080/00330124.2011.583592
42.
YaoYLiXLiuX, et al. (2017) Sensing spatial distribution of urban land use by integrating points-of-interest and google Word2Vec model. International Journal of Geographical Information Science31(4): 825–848. https://doi.org/10.1080/13658816.2016.1244608
43.
YueYZhuangYYehAGO, et al. (2017) Measurements of POI-Based mixed use and their relationships with neighbourhood vibrancy. International Journal of Geographical Information Science31(4): 658–675. https://doi.org/10.1080/13658816.2016.1220561
44.
ZhaiWBaiXShiY, et al. (2019) Beyond Word2vec: an approach for urban functional region extraction and identification by combining Place2vec and POIs. Computers, Environment and Urban Systems74(3): 1–12. https://doi.org/10.1016/j.compenvurbsys.2018.11.008
45.
ZhangBZhaoYWangZ (2023a) How to understand the mixed land use of rural settlements? Empirical evidence from the pearl river Delta, China. Habitat International136(1): 102818. https://doi.org/10.1016/j.habitatint.2023.102818
46.
ZhangBJiangGCaiW, et al. (2023b) Improvement in the quality of living environment with mixed land use of rural settlements: a case study of 18 villages in Hebei, China. Applied Geography157(4): 103016. https://doi.org/10.1016/j.apgeog.2023.103016
47.
ZhaoXXiaNLiCM (2023) 3-D multi-aspect mix degree index: a method for measuring land use mix at street block level. Computers, Environment and Urban Systems104(1): 102005. Available at: https://doi.org/10.1016/j.compenvurbsys.2023.102005
48.
ZhengHZhuoYXuZ, et al. (2021) Measuring and characterizing land use mix patterns of China's megacities: a case study of Shanghai. Growth and Change52(1): 2509–2539. https://doi.org/10.1111/grow.12546
49.
ZhuoYZhengHWuC, et al. (2019) Compatibility mix degree index: a novel measure to characterize urban land use mix pattern. Computers, Environment and Urban Systems75(1): 49–60. https://doi.org/10.1016/j.compenvurbsys.2019.01.005
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
