https://doi.org/10.4081/gh.2021.1024
Explaining the longevity characteristics in China from a geographical perspective: A multi-scale geographically weighted regression analysis
Submitted: 9 June 2021
Accepted: 5 November 2021
Published: 11 November 2021
Accepted: 5 November 2021
Abstract Views: 1482
PDF: 615
HTML: 66
HTML: 66
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Longevity is a near-universal human aspiration that can affect moral progress and economic development at the social level. In rapidly developing China, questions about the geographical distribution and environmental factors of longevity phenomenon need to be answered more clearly. This study calculated the longevity index (LI), longevity index for females (LIF) and longevity index for males (LIM) based on the percentage of the long-lived population among the total number of elderly people to investigate regional and gender characteristics at the county level in China. A new multi-scale geographically weighted regression (MGWR) model and four possible geographical environmental factors were applied to explore environmental effects. The results indicate that the LIs of 2838 counties ranged from 1.3% to 16.3%, and the distribution showed obvious regional and gender differences. In general, the LI was high in the East and low in the West, and the LIF was higher than the LIM in 2614 counties (92.1%). The MGWR model performed well explaining that geographical environmental factors, including topographic features, vegetation conditions, human social activity and air pollution factors have a variable influence on longevity at different spatial scales and in different regions. These findings enrich our understanding of the spatial distribution, gender differences and geographical environmental effects on longevity in China, which provides an important reference for people interested in the variations in the associations between different geographical factors.
Akaike H, 1981. Likelihood of a model and information criteria. J Econom 16:3-14.
DOI: https://doi.org/10.1016/0304-4076(81)90071-3
Ballard K, Bone C, 2021. Exploring spatially varying relationships between Lyme disease and land cover with geographically weighted regression. Appl Geogr 127:102383.
DOI: https://doi.org/10.1016/j.apgeog.2020.102383
Brown S, Versace VL, Laurenson L, Ierodiaconou D, Fawcett J, Salzman S, 2012. Assessment of spatiotemporal varying relationships between rainfall, land cover and surface water area using geographically weighted regression. Environ Model Assess 17:241-54.
DOI: https://doi.org/10.1007/s10666-011-9289-8
Browning MHEM, Lee K, Wolf KL, 2019. Tree cover shows an inverse relationship with depressive symptoms in elderly residents living in U.S. nursing homes. Urban For Urban Gree 41:23-32.
DOI: https://doi.org/10.1016/j.ufug.2019.03.002
Brunsdon C, Fotheringham AS, Charlton ME, 1996. Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr Anal 28:281-98.
DOI: https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
CAGG, 2019. Criteria and methods for recognizing longevity area. Available from: http://www.cagg.org.cn/portal/page/index/id/13.html Accessed: 20 March 2021.
CAGG, 2021. China Association of Gerontology and Geriatrics. Available from: http://www.cagg.org.cn Accessed: 20 March 2021.
Candore G, Balistreri CR, Colonna-Romano G, Lio D, Listi F, Vasto S, Caruso C, 2010. Gender-related immune-inflammatory factors, age-related diseases, and longevity. Rejuv Res 13:292-7.
DOI: https://doi.org/10.1089/rej.2009.0942
Carlson TN, Ripley DA, 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62:241-52.
DOI: https://doi.org/10.1016/S0034-4257(97)00104-1
Chrysohoou C, Skoumas J, Pitsavos C, Masoura C, Siasos G, Galiatsatos N, Psaltopoulou T, Mylonakis C, Margazas A, Kyvelou S, Mamatas S, Panagiotakos D, Stefanadis C, 2011. Long-term adherence to the Mediterranean diet reduces the prevalence of hyperuricaemia in elderly individuals, without known cardiovascular disease: The Ikaria study. Maturitas 70:58-64.
DOI: https://doi.org/10.1016/j.maturitas.2011.06.003
Cupido K, Fotheringham AS, Jevtic P, 2020. Local modelling of US mortality rates: a multiscale geographically weighted regression approach. Popul Space Place: 1-28.
DOI: https://doi.org/10.1002/psp.2379
Ebener S, Murray C, Tandon A, Elvidge CC, 2005. From wealth to health: modelling the distribution of income per capita at the sub-national level using night-time light imagery. Int J Health Geogr 4:1-17.
DOI: https://doi.org/10.1186/1476-072X-4-5
Elvidge CD, Baugh KE, Kihn EA, Kroehl HW, Davis ER, Davis CW, 1997. Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. Int J Remote Sens 18:1373-9.
DOI: https://doi.org/10.1080/014311697218485
Elvidge CD, Imhoff ML, Baugh KE, Hobson VR, Nelson I, Safran J, Dietz JB, Tuttle BT, 2001. Night-time lights of the world: 1994-1995. Isprs J Photogramm 56:81-99.
DOI: https://doi.org/10.1016/S0924-2716(01)00040-5
Fan ZY, Zhan QM, Yang C, Liu HM, Zhan M, 2020. How did distribution patterns of particulate matter air pollution (PM(2.5)and PM10) change in China during the COVID-19 Outbreak: A spatiotemporal investigation at Chinese city-level. Int J Environ Res Public Health 17:6274.
DOI: https://doi.org/10.3390/ijerph17176274
Fei XF, Chen WZ, Zhang SQ, Liu QM, Zhang ZH, Pei Q, 2018. The spatio-temporal distribution and risk factors of thyroid cancer during rapid urbanization-A case study in China. Sci Total Environ 630:1436-45.
DOI: https://doi.org/10.1016/j.scitotenv.2018.02.339
Fotheringham AS, Brunsdon CF, Charlton ME, 2002. Geographically weighted regression: The analysis of apatially varying relationships. Wiley, New York, NY, USA.
Fotheringham AS, Charlton M, Brunsdon C, 1996. The geography of parameter space: An investigation of spatial non-stationarity. Int J Geogr Inf Syst 10:605-27.
DOI: https://doi.org/10.1080/026937996137909
Fotheringham AS, Yang WB, Kang W, 2017. Multiscale geographically weighted regression (MGWR). Ann Am Assoc Geogr 107:1247-65.
DOI: https://doi.org/10.1080/24694452.2017.1352480
Fotheringham AS, Yue H, Li ZQ, 2019. Examining the influences of air quality in China's cities using multi-scale geographically weighted regression. T Gis 23:1444-64.
DOI: https://doi.org/10.1111/tgis.12580
Gao JB, Jiao KW, Wu SH, 2019. Revealing the climatic impacts on spatial heterogeneity of NDVI in China during 1982-2013. Acta Geograph Sinica 74:534-43.
Giorgio M, Renzi C, Oliveri S, Pravettoni G, 2016. Maternal care determinant of longevity? Arch Ital Biol 154:14-25.
DOI: https://doi.org/10.12871/00039829201613
Harris P, Lanfranco B, Lu BB, Comber A, 2020. Influence of geographical effects in hedonic pricing models for grass-fed cattle in Uruguay. Agriculture-Basel 10.
DOI: https://doi.org/10.3390/agriculture10070299
Hu K, Keenan K, Hale JM, Borger T, 2020. The association between city-level air pollution and frailty among the elderly population in China. Health Place 64.
DOI: https://doi.org/10.1016/j.healthplace.2020.102362
Jia P, Lakerveld J, Wu J, Stein A, Root E, Sabel C, Vermeulen R, Remais J, Chen X, Brownson R, Amer S, Xiao Q, Wang L, Verschuren M, Wu T, Wang Y, James P, 2019. Top 10 research priorities in spatial lifecourse epidemiology. Environ Health Persp 127:74501.
DOI: https://doi.org/10.1289/EHP4868
Jia P, Yu C, Remais J, Stein A, Liu Y, Brownson R, Lakerveld J, Wu T, Yang L, Smith M, Amer S, Pearce J, Kestens Y, Kwan M, Lai S, Xu F, Chen X, Rundle A, Xiao Q, Xue H, Luo M, Zhao L, Cheng G, Yang S, Zhou X, Li Y, Panter J, Kingham S, Jones A, Johnson B, Shi X, Zhang L, Wang L, Wu J, Mavoa S, Mwenda K, Wang Y, Verschuren M, Vermeulen R, James P, 2020. Spatial lifecourse epidemiology reporting standards (ISLE-ReSt) statement. Health Place 61:102243.
DOI: https://doi.org/10.1016/j.healthplace.2019.102243
Johnson O, Diggle P, Giorgi E, 2020. Dealing with spatial misalignment to model the relationship between deprivation and life expectancy: a model-based geostatistical approach. Int J Health Geograph 19.
DOI: https://doi.org/10.1186/s12942-020-00200-w
Li C, Li FB, Wu ZF, Cheng J, 2017a. Exploring spatially varying and scale-dependent relationships between soil contamination and landscape patterns using geographically weighted regression. Appl Geogr 82:101-14.
DOI: https://doi.org/10.1016/j.apgeog.2017.03.007
Li H, Wei YD, Zhou Y, 2017b. Spatiotemporal analysis of land development in transitional China. Habitat Int 67:79-95.
DOI: https://doi.org/10.1016/j.habitatint.2017.07.003
Li J, Lu D, Xu C, Li Y, Chen M, 2017c. Spatial heterogeneity and its changes of population on the two sides of Hu line. Acta Geograph Sinica 72:148-60.
Li R, Cui LL, Li JL, Zhao A, Fu HB, Wu Y, Zhang LW, Kong LD, Chen JM, 2017d. Spatial and temporal variation of particulate matter and gaseous pollutants in China during 2014-2016. Atmos Environ 161:235-46.
DOI: https://doi.org/10.1016/j.atmosenv.2017.05.008
Li YC, Wang XP, Zhu QS, Zhao H, 2014. Assessing the spatial and temporal differences in the impacts of factor allocation and urbanization on urban-rural income disparity in China, 2004-2010. Habitat Int 42:76-82.
DOI: https://doi.org/10.1016/j.habitatint.2013.10.009
Liu E, Feng Y, Yue Z, Zhang Q, Han T, 2019. Differences in the health behaviors of elderly individuals and influencing factors: Evidence from the Chinese longitudinal healthy longevity survey. Int J Health Plann Manage 34:e1520-32.
DOI: https://doi.org/10.1002/hpm.2824
Liu XH, Yang QK, A TG, 2001. Extraction and application of relief of china based on DEM and GIS method. Bull Soil Water Conserv 21:57-59,62.
Liu YL, Luo KL, Lin XX, Gao X, Ni RX, Wang SB, Tian XL, 2014. Regional distribution of longevity population and chemical characteristics of natural water in Xinjiang, China. Sci Total Environ 473:54-62.
DOI: https://doi.org/10.1016/j.scitotenv.2013.11.134
Ljungquist B, Berg S, Lanke J, McClearn GE, Pedersen NL, 1998. The effect of genetic factors for longevity: A comparison of identical and fraternal twins in the Swedish twin registry. J Gerontol a-Biol 53:M441-6.
DOI: https://doi.org/10.1093/gerona/53A.6.M441
Lv JM, Wang WY, Li YH, 2011. Effects of environmental factors on the longevous people in China. Arch Gerontol Geriat 53:200-5.
DOI: https://doi.org/10.1016/j.archger.2010.10.012
Magnolfi SU, Petruzzi E, Pinzani P, Malentacchi E, Pazzagli M, Antonini FM, 2007. Longevity index (LI%) and centenarity index (CI%): new indicators to evaluate the characteristics of aging process in the Italian population. Arch Gerontol Geriat 44:271-6.
DOI: https://doi.org/10.1016/j.archger.2006.05.006
Mansour S, Al Kindi A, Al-Said A, Al-Said A, Atkinson P, 2021. Sociodemographic determinants of COVID-19 incidence rates in Oman: geospatial modelling using multiscale geographically weighted regression (MGWR). Sustain Cities Soc 65.
DOI: https://doi.org/10.1016/j.scs.2020.102627
Mei L, Xue Y, de Leeuw G, Guang J, Wang Y, Li Y, Xu H, Yang L, Hou T, He X, Wu C, Dong J, Chen Z, 2011. Integration of remote sensing data and surface observations to estimate the impact of the Russian wildfires over Europe and Asia during August 2010. Biogeosciences 8:3771-91.
DOI: https://doi.org/10.5194/bg-8-3771-2011
Montesanto A, De Rango F, Pirazzini C, Guidarelli G, Domma F, Franceschi C, Passarino G, 2017. Demographic, genetic and phenotypic characteristics of centenarians in Italy: Focus on gender differences. Mech Ageing Dev 165:68-74.
DOI: https://doi.org/10.1016/j.mad.2017.04.008
Oshan TM, Li ZQ, Kang W, Wolf LJ, Fotheringham AS, 2019. MGWR: A python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. Isprs Int J Geo-Inf 8.
DOI: https://doi.org/10.3390/ijgi8060269
Oshan TM, Smith JP, Fotheringham AS, 2020. Targeting the spatial context of obesity determinants via multiscale geographically weighted regression. Int J Health Geograph 19.
DOI: https://doi.org/10.1186/s12942-020-00204-6
PCOSC and NBSC, 2012. Tabulation on the 2010 population censuses of the people’s republic of China by county. China Statistic Press, Beijing, China.
Pes GM, Tolu F, Poulain M, Errigo A, Masala S, Pietrobelli A, Battistini NC, Maioli M, 2013. Lifestyle and nutrition related to male longevity in Sardinia: An ecological study. Nutr Metab Cardiovas 23:212-9.
DOI: https://doi.org/10.1016/j.numecd.2011.05.004
Poulain M, Herm A, Pes G, 2013. The Blue Zones: areas of exceptional longevity around the world. Vienna Yearbook Popul Res 2013:87-108.
DOI: https://doi.org/10.1553/populationyearbook2013s87
Poulain M, Pes GM, Grasland C, Carru C, Ferrucci L, Baggio G, Franceschi C, Deiana L, 2004. Identification of a geographic area characterized by extreme longevity in the Sardinia island: the AKEA study. Exp Gerontol 39:1423-9.
DOI: https://doi.org/10.1016/j.exger.2004.06.016
Robine JM, Cubaynes S, 2017. Worldwide demography of centenarians. Mech Ageing Dev 165:59-67.
DOI: https://doi.org/10.1016/j.mad.2017.03.004
Romanski J, Wu W, Anderson PJ, Austin PC, Rochon PA, 2015. Visualising the distribution of individuals of advanced age in Canada: linking census data to maps. Age Ageing 44:511-4.
DOI: https://doi.org/10.1093/ageing/afu203
Salleh SA, Abd Latif Z, Mohd WMNW, Chan A, 2012. Albedo pattern recognition and time-series analyses in malaysia. Int Arch Photogramm 39:235-40.
DOI: https://doi.org/10.5194/isprsarchives-XXXIX-B7-235-2012
State Council of China, 2019. Notice on the launch of the seventh national population census. Available from: http://www.gov.cn/zhengce/content/2019-11/08/content_5450146.htm Accessed: 20 March 2021.
Song WJ, Li YH, Hao Z, Li HR, Wang WY, 2016. Public health in China: an environmental and socio-economic perspective. Atmos Environ 129:9-17.
DOI: https://doi.org/10.1016/j.atmosenv.2015.12.021
Spann SJ, Ottinger MA, 2018. Longevity, metabolic disease, and community health. Prog Mol Biol Transl 155:1-9.
DOI: https://doi.org/10.1016/bs.pmbts.2017.11.015
Su SL, Xiao R, Zhang Y, 2012. Multi-scale analysis of spatially varying relationships between agricultural landscape patterns and urbanization using geographically weighted regression. Appl Geogr 32:360-75.
DOI: https://doi.org/10.1016/j.apgeog.2011.06.005
Vaupel JW, Carey JR, Christensen K, Johnson TE, Yashin AI, Holm NV, Iachine IA, Kannisto V, Khazaeli AA, Liedo P, Longo VD, Zeng Y, Manton KG, Curtsinger JW, 1998. Biodemographic trajectories of longevity. Science 280:855-60.
DOI: https://doi.org/10.1126/science.280.5365.855
Vierboom YC, Preston SH, 2020. Life beyond 65: Changing spatial patterns of survival at older ages in the United States, 2000-2016. J Gerontol B-Psychol 75:1093-103.
DOI: https://doi.org/10.1093/geronb/gbz160
Wang B, Bao JW, Peng JF, Zhang J, Wang P, 2018. An NDVI synthesis method for multi-temporal remote sensing images based on K-NN learning: a case based on GF-1 data. Remote Sens Lett 9:541-9.
DOI: https://doi.org/10.1080/2150704X.2018.1452059
Wang L, Li YH, Li HR, Holdaway J, Hao Z, Wang WY, Krafft T, 2016. Regional aging and longevity characteristics in China. Arch Gerontol Geriat 67:153-9.
DOI: https://doi.org/10.1016/j.archger.2016.08.002
Wang L, Wei BG, Li YH, Li HR, Zhang FY, Rosenberg M, Yang LS, Huang JX, Krafft T, Wang WY, 2014. A study of air pollutants influencing life expectancy and longevity from spatial perspective in China. Sci Total Environ 487:57-64.
DOI: https://doi.org/10.1016/j.scitotenv.2014.03.142
Wang SB, Luo KL, Liu YL, Zhang SX, Lin XX, Ni RX, Tian XL, Gao X, 2015. Economic level and human longevity: Spatial and temporal variations and correlation analysis of per capita GDP and longevity indicators in China. Arch Gerontol Geriat 61:93-102.
DOI: https://doi.org/10.1016/j.archger.2015.03.004
Wu XD, Xiao Q, Wen JG, You DQ, Hueni A, 2019. Advances in quantitative remote sensing product validation: overview and current status. Earth-Sci Rev 196.
DOI: https://doi.org/10.1016/j.earscirev.2019.102875
Xu LY, Kang P, Wei JJ, 2010. Evaluation of urban ecological carrying capacity: a case study of Beijing, China. Procedia Environ Sci 2:1873-80.
DOI: https://doi.org/10.1016/j.proenv.2010.10.199
Xu W, Sun J, Liu Y, Xiao Y, Tian Y, Zhao B, Zhang X, 2019. Spatiotemporal variation and socioeconomic drivers of air pollution in China during 2005-2016. J Environ Manage 245:66-75.
DOI: https://doi.org/10.1016/j.jenvman.2019.05.041
Xu X, Ma Y, Zhang X, Maisumu D, Zhao X, Gao J, Yasheng M, 2009. Serum sex hormone levels associated with aging and arterial blood pressure in the Uygur longevity people in Hetian Xinjiang Uygur Autonomous Region. Chinese J Hypertens 17:925-9.
Yang DY, Ye C, Wang XM, Lu DB, Xu JH, Yang HQ, 2018. Global distribution and evolvement of urbanization and PM2.5 (1998-2015). Atmos Environ 182:171-8.
DOI: https://doi.org/10.1016/j.atmosenv.2018.03.053
Yu H, Fotheringham AS, Li Z, Oshan T, Kang W, Wolf LJ, 2019. Inference in multiscale geographically weighted regression. Geogr Anal 52:87-106.
DOI: https://doi.org/10.1111/gean.12189
Zafeiris KN, 2020. Gender differences in life expectancy at birth in Greece 1994-2017. J Popul Res 37:73-89.
DOI: https://doi.org/10.1007/s12546-019-09239-4
Zhang W, Wei M, 2016. The evaluation of the mortality and life expectancy of Chinese population. Popul J 38:18-28.
Zhao Z, Cheng Z, Shan J, Tu L, 2003. Marital, sexual behavior, sex hormone of Uygur Iongevity people in Hetian,XiJiang Uygur Autonomous Region. Chinese J Geriatr 22:490-493.
Zhu XB, Ma MG, Yang H, Ge W, 2017. Modeling the spatiotemporal dynamics of gross domestic product in China using extended temporal coverage nighttime light data. Remote Sens 9.
DOI: https://doi.org/10.3390/rs9060626
How to Cite
Yang, R., Ren, F., Ma, X., Zhang, H., Xu, W., & Jia, P. (2021). Explaining the longevity characteristics in China from a geographical perspective: A multi-scale geographically weighted regression analysis. Geospatial Health, 16(2). https://doi.org/10.4081/gh.2021.1024
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
