Changes In Spatial Patterns Research Articles

Accelerated Shrinkage of Glaciers in the Altai Mountains From 2000 to 2020

Mountain glaciers are an important component of the global hydrological cycle. Existing research about glacier changes in the Altai focused on limited regions. Study about recent glacier changes in the entire Altai Mountains is still lacking. We presented a consistent method for identifying glacier margins. The two new glacier inventories in 2000 and 2020 were derived from Landsat satellite imagery. Glacier surface elevation change and mass balance were obtained by comparing the 2000 Shuttle Radar Topography Mission (SRTM) and 2020 Digital Elevation Models (DEMs) generated from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. The spatial pattern of glacier changes was discussed in conjunction with climate trends. We mapped a total area of 1,096.06 ± 53.32 km2around 2020, which amounts to 1,927 glaciers in the Altai Mountains. That was 12.02 ± 3.01% (or 0.60 ± 0.15%·a−1) less than the 1,245.75 ± 58.52 km2around 2000. The geodetic mass balance of the monitoring glaciers in the Aktru basin for the period 2000–2011 was used to validate the geodetic survey. The average geodetic mass balance of -0.32 ± 0.09 m w. e.·a−1on monitoring glaciers was slightly exaggerated than the observed mass balance of -0.26 m w. e.·a−1, but it was proved that the geodetic mass balance could reflect glacier changes in the Altai Mountains. An average mass loss of 14.55 ± 1.32 m w. e. (or 0.74 ± 0.07 m w. e.·a−1) was found during 2000–2020 in the Altai Mountains. Although the glacier area changes and mass balance were characterized by spatial heterogeneity, the glaciers in the Altai had experienced an accelerated shrinkage from 2000 to 2020 compared to the 20th century. The rising temperature is the foremost reason for glacier area shrinkage and mass loss according to the Climatic Research Unit (CRU) reanalysis data.

Multi-Scenario Simulation of Production-Living-Ecological Space in the Poyang Lake Area Based on Remote Sensing and RF-Markov-FLUS Model

With industrialization and urbanization, the competition among land production, living, and ecological (PLE) spaces has intensified. Particularly in ecological reserves, competition among various types of land use restricts the coordinated development of PLE space. To explore spatial sustainable development, this study starts from a PLE spatial perspective, based on Landsat long time series images. Object-based image analysis (OBIA) and landscape index analysis were selected to monitor the spatial and temporal land use and landscape pattern changes in the Poyang Lake region (PYL region) from 1989 to 2020. The RF-Markov-FLUS coupled model was used to simulate spatial changes in 2030 under four scenarios: production space priority (PSP), living space priority (LSP), ecological space priority (ESP), and an integrated development (ID). Finally, the goal-problem-principle was used to enhance PLE space. The results showed that: (1) production space and ecological spaces decreased in general from 1989 to 2020 by 3% and 7%, respectively; living space increased by 11%. (2) From 1989 to 2020, the overall landscape spread in the Poyang Lake (PYL) area decreased, connectivity decreased, fragmentation increased, landscape heterogeneity increased, and landscape geometry became more irregular. (3) Compared with the other three scenarios, the ID scenario maintained steady production space growth in 2030, the expansion rate of living space slowed, and the area of ecological space decreased the least. (4) Spatial pattern optimization should start with three aspects: the transformation of the agricultural industry, improving the efficiency of urban land use, and establishing communities of “mountains, water, forests, fields, lakes and grasses”. The results provide scientific planning and suggestions for the future ecological protection of Poyang Lake area with multiple scenarios and perspectives.

Degradation or Restoration? The Temporal-Spatial Evolution of Ecosystem Services and Its Determinants in the Yellow River Basin, China

Ecosystem services (ESs) are irreplaceable natural resources, and their value is closely related to global change and to human well-being. Research on ecosystem services value (ESV) and its influencing factors can help rationalize ecological regulatory policies, and is especially relevant in such an ecologically significant region as the Yellow River Basin (YRB). In this study, the ecological contribution model was used to measure the contribution of intrinsic land use change to ESV, the bivariate spatial autocorrelation model was applied to investigate the relationship between land use degree and ESV, and the geographical detector model (GDM) and geographically weighted regression (GWR) were applied to reveal the impact of natural and socio-economic factors on ESV. Results showed that: (1) The total ESV increased slightly, but there were notable changes in spatial patterns of ESV in the YRB. (2) Land use changes can directly lead to ESV restoration or degradation, among which, conversion from grassland to forest land and conversion from unused land to grassland are vital for ESV restoration in the YRB, while degradation of grassland is the key factor for ESV deterioration. (3) According to GDM, NDVI is the most influential factor affecting ESV spatial heterogeneity, and the combined effect of multiple factors can exacerbate ESV spatial heterogeneity. (4) GWR reveals that NDVI is always positively correlated with ESV, GDP is mainly positively correlated with ESV, and population density is mainly negatively correlated with ESV, while positive and negative correlation areas for other factors are roughly equal. The findings can provide theoretical support and scientific guidance for ecological regulation in the YRB.

Integrating Land Use, Ecosystem Service, and Human Well-Being: A Systematic Review

Global change, population growth, and urbanization have been exerting a severe influence on the environment, including the social system and ecosystem. To find solutions based on nature, clarifying the complicated mechanisms and feedback among land use/land cover changes, ecosystem services, and human well-being, is increasingly crucial. However, the in-depth linkages among these three elements have not been clearly and systematically illustrated, present research paths have not been summarized well, and the future research trends on this topic have not been reasonably discussed. In this sense, the purpose of this paper is to provide an insight into how land use/land cover changes, ecosystem services, and human well-being are linked, as well as their relationships, interacting ways, applications in solving ecological and socioeconomic problems, and to reveal their future research trends. Here, we use a systematic literature review of the peer-reviewed literature to conclude the state of the art and the progress, emphasize the hotspot, and reveal the future trend of the nexus among the three aspects. Results show that (1) ecosystem services are generally altered by the changes in land use type, spatial pattern, and intensity; (2) the nexus among land use change, ecosystem services, and human well-being is usually used for supporting poverty alleviation, ecosystem health, biodiversity conservation, and sustainable development; (3) future research on land use/land cover changes, ecosystem services, and human well-being should mainly focus on strengthening multiscale correlation, driving force analysis, the correlation among different group characteristics, land use types and ecosystem service preferences, and the impact of climate change on ecosystem services and human well-being. This study provides an enhanced understanding of the nexus among the three aspects and a reference for future studies to mitigate the relevant problems.

Forest fragmentation in a forest Biosphere Reserve: Implications for the sustainability of natural habitats and forest management policy in Ethiopia

Forest fragmentation is a landscape-level process that comprises forest loss and changes in spatial patterns. Effective forest conservation programs thus require a comprehensive understanding of the relationship between forest cover and the spatial patterns of forest fragments. However, these matters remain less known for ecosystems in tropical regions. Hence, the study examines forest fragmentation of the Kaffa Biosphere Reserve in Ethiopia, which is a forested ecosystem of 744.9 thousand hectares. The study covers 33 years from 1986 to 2019 and forecasts for 2034 and 2049 using Landsat imagery. The land-use and land cover change maps were produced using a supervised classification following the maximum likelihood approach. The study quantified the spatial and temporal trends in forest loss and fragmentation using a multi-temporal satellite and FRAGSTAT raster dataset. The results display the forest areas declined from 46.9 to 30.5% from 1986 to 2019. The fragmentation caused an increase in the number of forest fragments from 4,067 to 8,832 from 1986 to 2019 but is expected to decline to nearly 2,000 in 2049 due to the complete conversion of small size forest fragments into other land use types. In addition, the size of the large core forest areas reduced from 43.7% in 1986 to 6.1% in 2019. The edge density of the forest declined from 30.5 to 27.1 m/ha from 1986 to 2019 and declined to 13 m/ha in 2049. The results confirmed natural and anthropogenic processes (e.g. agricultural and built-up area expansions and road network development) caused forest fragmentation and degradation in the Kaffa Biosphere Reserve. Therefore, the policy-makers should look at new options and revised the existed natural resource conservation strategies to safeguard the remaining natural forest covers.

R‐CTM: A data‐driven macroscopic simulation model for heterogeneous traffic

AbstractThere is a well‐known trade‐off between computational efficiency and computational accuracy in the field of traffic simulation. In this article, we propose a novel recurrent neural network based model with an integrated attention mechanism, called R‐CTM, to simulate heterogeneous traffic flow with multiple types of vehicles. It can effectively extract the traffic flow patterns of spatial and temporal changes from training traffic data, which can be real‐world traffic data or synthetic traffic data via microscopic simulation models. Through experiments and comparisons, we show that it can significantly outperform the state of the art methods in terms of simulation accuracy. Besides accuracy, we also demonstrate its scalability: its runtime consumption does not linearly increase with respect to the spatial extent.

Spatial Patterns of Deltaic Deposition/Erosion Revealed by Streaklines Extracted From Remotely‐Sensed Suspended Sediment Concentration

AbstractDeltas are vulnerable landscapes, making it crucial to understand their spatial patterns of deposition/erosion. Here, we used patterns in suspended sediment concentration (SSC) measured by a NASA airborne spectrometer, AVIRIS‐NG, to infer deposition/erosion within Wax Lake Delta, Louisiana. Conceptually, change in SSC within a fluid parcel traveling downstream reflects settling and/or entrainment from the bed. We found that remotely‐sensed SSC displays curvilinear features, which we interpret as streaklines. We developed a semi‐automated technique for extracting streaklines using a cost function based on SSC and its geometric curvature. We measured SSC change along streaklines, which when combined with flow velocities obtained from a hydrodynamic model, allowed us to infer instantaneous deposition/erosion rates. These rates are realistic in magnitude and record coherent spatial patterns across the delta. Our novel method provides a promising avenue for relating spatial patterns of land change to flow conditions over wide areas in vulnerable deltas.

Sustainable Development Goals and childhood measles vaccination in Ekiti State, Nigeria: Results from spatial and interrupted time series analyses

Measles remains an important cause of childhood mortality in many resource-limited countries. With Sustainable Development Goals (SDG), there has been increasing emphasis on measles vaccination as a key strategy to remarkably improve child survival. While progress has been made towards measles vaccination coverage due to SDG in some settings, there has been no prior study evaluating its impact in Nigeria. To assess the effectiveness of SDG policy implementation on measles vaccination coverage, we examined the changes in first dose of measles vaccination coverage rates among children aged 9–15 months following the implementation of SDG, and changes in spatial patterns of measles vaccination from 2014 to 2019 in Ekiti State, Southwest Nigeria. Using state and local government area-level District Health Information data from January 2014 to December 2019, we conducted interrupted time series (ITS) and spatiotemporal analyses. The ITS evaluated the immediate and continuous effects of SDG policy implementation on the monthly childhood measles vaccination coverage by comparing longitudinal changes in rates between pre-intervention period (January 2014-December 2015) and during-intervention period (January 2016-December 2019). The low and high coverage clusters across the years were detected with spatial cluster analysis. The average state-level measles vaccination coverage rates from 2014 to 2019 was 70.67%. In 2019, coverage rate was 49%—a 35.53% decline from 76% in 2014 and a state-level gap of 46%. The geographical distribution of measles vaccination varied considerably across the local government areas from 2014 to 2019. There was an initial acceleration of vaccination rates (β^ = 24.07, p-value = 0.012), but a significant decrease in coverage rates after implementation of SDG policy in Ekiti State (β^ = −1.08, p-value < 0.001). No local government area had accelerated monthly coverage rates following SDG-implementation. Evidence suggests immediate acceleration in coverage rates which could not be sustained during SDG-era and calls for a rethink measles immunization coverage strategy in the state and other resource-limited jurisdictions to ensure vaccination scale-up.

Sphingomonas Relies on Chemotaxis to Degrade Polycyclic Aromatic Hydrocarbons and Maintain Dominance in Coking Sites.

Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants widely present in industrial sites. Microbial degradation is an effective method of removing PAHs. The identification of microorganisms that have important ecological functions at the site is of great significance for PAH removal. We collected soil samples at three depths in the range of 0–100 cm at 70-day intervals at the coking site and explored the degradation of PAHs. We combined molecular ecology networking, metagenomics, and genome assembly to search for microorganisms that persist, dominate, and affect the microbial community construction in the degradation process and analyzed their adaptation strategies. The results showed that 15.78 mg/kg of PAHs naturally decayed, and 13.33 mg/kg of PAHs migrated from 30–100 cm to 0–30 cm in the soil. Sphingomonas, which occupied a niche advantage, was both the core and keystone microorganism, and its spatial distribution pattern and temporal change dynamics were consistent with those of PAHs. We assembled the genome of Sphingomonas sp., revealing its multiple potential for degrading PAHs and other pollutants. Additionally, flagellar assembly and bacterial chemotaxis genes ranked high in the assembled genome of Sphingomonas sp., which might help it obtain a competitive advantage in the soil. The findings underscored the strategy of Sphingomonas to maintain dominance, enriched the understanding of PAH-degrading microorganisms in site soil, and provided references for the remediation of PAHs.

Which came first, the gentrification or the Airbnb? Identifying spatial patterns of neighbourhood change using Airbnb data

Over time, neighbourhoods experience different types of change. Neighbourhood change is a spatiotemporal process that involves analysing how attributes of a location change over time. In this article, we explore the capability of short-term property rental data to identify neighbourhoods where change is occurring. The article focuses on the relationship between data from the Airbnb platform and gentrification. Through a study in Dublin, Ireland, Airbnb data was analysed using state-of-the-art Emerging Hotspot Analysis to identify locations with high potential to experience gentrification and areas that have undergone recent development. The locations identified by the method are correlated with gentrification and neighbourhood change described in other literature. The spatiotemporal approach reveals more about locational changes in the city than other methods, which focus only on one component, such as space or time. The study highlights the importance of access to real estate market data, such as data about the short-term rental market, to extract spatiotemporal changes occurring in neighbourhoods. The results contribute to the discourse on the impacts of short-term rental and Airbnb on cities and can be used by policymakers, observers, and citizens to motivate the distribution of services. The question as to whether gentrification or Airbnb came first remains an open one.

CHANGES OF SPATIAL DISPARITIES IN SATELLITE-DERIVED PM2.5 EXPOSURES OVER 2010–2019 IN CHINA

Abstract. Air pollution, especially fine particulate matter (PM2.5), has attracted extensive attention due to its adverse impacts on public health. Although PM2.5 pollution was significantly reduced in China over time, while little is known how the spatial disparity of PM2.5 exposure has evolved, especially from both absolute and relative perspectives. Here, we estimate the long-term PM2.5 exposures in China based on satellite observations and convolutional neural network, and characterize the spatial disparity of PM2.5 exposure using Theil index and rank-rank relationship. The result shows that both PM2.5 exposure and absolute spatial disparity were substantially reduced between 2010 and 2019. The nation-wide concentrations (Theil index) declined from 48.0µg/m3 (0.13) to 35.5µg/m3 (0.054). The inter-provincial disparities dominate the overall disparity in 2010, while the intra-provincial disparity contributed the most in 2019. However, while absolute disparities have diminished, relative disparities persist. PM2.5 exposures in the least 20th percentile polluted cities have increased over time, while exposures in other regions declined. On average, the more (less) polluted cities in 2010 were still the more (less) polluted cities in 2019 (except for the very most 2 percentile polluted cities), indicating that the population in more polluted cities still experiences more air pollution than others. Spatial pattern of relative disparity changes was also observed. Overall, understanding not only absolute spatial disparity but also relative disparity is required to help formulate targeted policies for an equitable environment, leaving nobody behind.

Expanding the temporal and spatial scales of environmental DNA research with autonomous sampling

AbstractEnvironmental DNA (eDNA) is an emerging and powerful method for use in marine research, conservation, and management, yet time‐ and resource‐intensive protocols limit the scale of implementation. Long‐range autonomous underwater vehicles equipped with autonomous environmental sample processors (LRAUV‐ESPs) provide a new means for scaling up marine eDNA sample collection and processing. Here, we used eDNA metabarcoding of four marker genes (mitochondrial 12S rRNA, bacterial and archaeal 16S rRNA, nuclear 18S rRNA, and mitochondrial COI), which encompass the diversity of marine species from microbes to vertebrates, to demonstrate the efficacy of an LRAUV‐ESP in sampling eDNA and assessing community structure in the Monterey Bay National Marine Sanctuary. The sequencing results from samples that were autonomously collected were comparable with those collected from a ship at similar locations, times, and depths, supporting previous results that found no significant differences using targeted qPCR. This study demonstrates the potential of equipping autonomous underwater vehicles with ESPs to greatly expand the scale of eDNA sample collection and processing and provide much needed information regarding the changing spatial and temporal patterns of marine biodiversity, especially in many data‐poor regions of the world's oceans.

Locus Coeruleus Integrity from 7 T MRI Relates to Apathy and Cognition in Parkinsonian Disorders.

BackgroundNeurodegeneration in the locus coeruleus (LC) contributes to neuropsychiatric symptoms in both Parkinson's disease (PD) and progressive supranuclear palsy (PSP). Spatial precision of LC imaging is improved with ultrahigh field 7 T magnetic resonance imaging.ObjectivesThis study aimed to characterize the spatial patterns of LC pathological change in PD and PSP and the transdiagnostic relationship between LC signals and neuropsychiatric symptoms.MethodsTwenty‐five people with idiopathic PD, 14 people with probable PSP‐Richardson's syndrome, and 24 age‐matched healthy controls were recruited. Participants underwent clinical assessments and high‐resolution (0.08 mm3) 7 T‐magnetization‐transfer imaging to measure LC integrity in vivo. Spatial patterns of LC change were obtained using subregional mean contrast ratios and significant LC clusters; we further correlated the LC contrast with measures of apathy and cognition, using both mixed‐effect models and voxelwise analyses.ResultsPSP and PD groups showed significant LC degeneration in the caudal subregion relative to controls. Mixed‐effect models revealed a significant interaction between disease‐group and apathy‐related correlations with LC degeneration (β = 0.46, SE [standard error] = 0.17, F(1, 35) = 7.46, P = 0.01), driven by a strong correlation in PSP (β = −0.58, SE = 0.21, t(35) = −2.76, P = 0.009). Across both disease groups, voxelwise analyses indicated that lower LC integrity was associated with worse cognition and higher apathy scores.ConclusionsThe relationship between LC and nonmotor symptoms highlights a role for noradrenergic dysfunction across both PD and PSP, confirming the potential for noradrenergic therapeutic strategies to address transdiagnostic cognitive and behavioral features in neurodegenerative disease. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

A Spatial Patterns Identification Method of Rural Residential Land Change Integrating Dynamic and Multi-Scale Information

Affected by rapid urbanization, the spatial layout of rural residential land (RRL) is facing urgent reconstruction and adjustment. The study of spatial change patterns of RRL can provide a basis for optimization and adjustment. The Pinggu of Beijing was chosen as the study area. Taking into account the layout characteristics of RRL from 2005 to 2015, a combination matrix method was developed to identify the spatial change patterns. The distribution characteristics of the change patterns under different environmental gradients were further analyzed. The study shows that the layout types of RRL in Pinggu in 2015 were dominated by the dispersion-regular-large scale cluster type and concentration-regular-large scale cluster type. The area of RRL patches in Pinggu increased by 686.89 hm2 from 2005 to 2015, which was approximately 12%. The main spatial change patterns of the RRL are the cluster leapfrog dispersion change pattern and cluster edge-expansion concentration change pattern. The distribution of the pattern of RRL shows a decreasing trend under the topography and location gradients set in this paper. And the spatial patterns of RRL are changing at an accelerated rate due to the advanced production environment represented. Our study analyzes the changing pattern of RRL from multiple environmental gradients and provides a basis for the formulation of RRL layout optimization strategies.

Shipping in the north-east Atlantic: Identifying spatial and temporal patterns of change

Maritime traffic is increasing globally, with a four-fold increase in commercial vessel movements between 1992 and 2012. Vessels contribute to noise and air pollution, provide pathways for non-native species, and collide with marine wildlife. While knowledge of shipping trends and potential environmental impacts exists at both local and global levels, key information on vessel density for regional-scale management is lacking. This study presents the first in-depth spatio-temporal analysis of shipping in the north-east Atlantic region, over three years in a five-year period. Densities increased by 34%, including in 73% of Marine Protected Areas. Western Scotland and the Bay of Biscay experienced the largest increases in vessel density, predominantly from small and slow vessels. Given well-documented impacts that shipping can have on the marine environment, it is crucial that this situation continues to be monitored – particularly in areas designated to protect vulnerable species and ecosystems which may already be under pressure.

Effect of Land-Use Change on the Changes in Human Lyme Risk in the United States

The spatial extent and incidence of Lyme disease is increasing in the United States, particularly in the Upper Midwest and Northeast. Many previous studies have explored the drivers of its spatial pattern, however, few studies tried to explore the drivers for the changes of Lyme disease. We here compared the spatial patterns of changes of human Lyme cases and incidence in the Northeast and Upper Midwest between 2003–2005 and 2015–2017, and applied two different approaches (i.e., a statistical regularization approach and model averaging) to investigate the climatic and landscape factors affecting the risk change between the two periods. Our results suggested that changes in land-use variables generally showed different relationships with changes of human Lyme risk between the two regions. Changes of variables related to human-use areas showed opposite correlations in two regions. Besides, forest area and forest edge density generally negatively correlated with the change of human Lyme risk. In the context of ongoing habitat change, we consider this study may provide new insight into understanding the responses of human Lyme disease to these changes, and contribute to a better prediction in the future.

Optimization of Spatial Pattern of Land Use: Progress, Frontiers, and Prospects.

Due to high-intensity human disturbance and rapid climate change, optimizing the spatial pattern of land use has become a pivotal path to restoring ecosystem functions and realizing the sustainable development of human–land relationships. This review uses the literature analysis method combined with CiteSpace to determine current research progress and frontiers, challenges, and directions for further improvement in this field. The main conclusions include the following: (a) research on the optimization of spatial pattern of land use has transformed from pattern description orientation to sustainable development orientation to ecological restoration orientation. Its research paradigm has changed from pattern to function to well-being; (b) the research frontier mainly includes spatial pattern of land use that takes into account the unity of spatial structure and functional attributes, the ecological mechanism and feedback effect of change in spatial pattern of land, the theoretical framework and model construction of land use simulation and prediction based on multiple disciplines and fields, and the adaptive management of sustainable land use in the context of climate change; (c) based on current research challenges, we integrate the research on landscape ecology and ecosystem service flows to develop an “element sets–network structure–system functions–human well-being” conceptual model. We also propose the strengthening of future research on theoretical innovation, spatiotemporal mechanism selection, causal emergence mechanism, the transformation threshold, and uncertainty. We provide innovative ideas for achieving sustainable management of land systems and territorial spatial planning with the aim of improving the adaptability of land use spatial optimization. This is expected to strengthen the ability of land systems to cope with ecological security and climate risks.

Monitoring and projecting sustainable transitions in urban land use using remote sensing and scenario-based modelling in a coastal megacity

Under the framework of United Nations Sustainable Development Goals, an urban land use efficiency indicator (SDG11.3.1) was put forward to support formulating informed policies through the analysis of land consumption rate against population growth rate. The assessment of SDG11.3.1 indicator played an import role in understanding sustainable transitions in urban land use from local to regional and global scales. Based on remote sensing and scenario modeling, this study attempts to develop an approach for monitoring and making projections of the urban land use efficiency indicator to inform urban management and planning. Taking the coastal megacity of Tianjin, China as a case study, the spatial patterns of urban land use change were first mapped using multi-temporal satellite datasets and an urban sprawl matrix method. Then, the urban land use changes for the periods up to 2025 and 2030 under an environmental protection scenario were predicted by integrating local policy constraints into a cellular automata–Markov (CA–Markov) model using analytic hierarchy process and multi-criteria evaluation methods. Finally, values of the urban land use efficiency indicator SDG11.3.1 were derived for the period 2000 to 2030. The results showed that built-up area in Tianjin in 2020 doubled compared with 1990 and that 63.95% of the newly increased built-up land was converted from agricultural lands. The model prediction results indicate that the expansion of built-up land is more concentrated and land consumption and population growth tend to be more closely correlated when constrained by environmental protection measures. Extensive growth of built-up areas predicted in coastal areas might increase ecological and disaster risks, and should be strictly planned. The land use change modeling and analysis framework can be applied to growing coastal cities in other regions to inform sustainable land use planning.

Spatio-temporal evolution of Chinese migration in Melbourne, Australia

ABSTRACT This paper examines the changing spatial and temporal patterns of residential choices across different waves of migrants from the mainland China between 1941 and 2011. Moran’s I and GetisOrd Indices are computed to validate the spatial assimilation theory to identify the prevalence of spatial clustering of Chinese migrants at a local area level. The spatial autocorrelation indicates the spatial segregation of Chinese migrants during the first three migration waves. This segregation propensity, however, has marginally declined in the recent wave. Getis hotspot analysis nevertheless shows no significant locational shift in the spatial clusters of Chinese migrants. This indicates the existence of path dependency, whereby the recent wave of immigrants from China exhibits spatial segregation behaviour similar to those manifested by earlier waves. Key spatial migrant clustersaresustained over time. The mapped outputs show that Chinese migrants congregate at the higher end of the housing market, particularly in the south-eastern suburbs of Melbourne. The analysis, therefore, rejects the conventional wisdom that migrants tend to spatially assimilate over time and they create urban ghettos or refuge for sheltering disadvantaged communities.

Spatial and temporal variation and driving factors of wetland in the Amu Darya River Delta, Central Asia

The Aral Sea in Central Asia has shrunk by 90% since 1960. The resulting desertification and salt and dust storms have diminished biodiversity in the Amu Darya River Delta, triggering the Aral Sea Crisis. Timely monitoring of trends and spatial patterns in deltaic wetlands is beneficial for biodiversity conservation and ecological restoration. Existing studies have mainly focused on the response of vegetation and microbial communities to ecological changes while ignoring the indicative role of wetlands-associated changes. This study analyzed the spatial and temporal patterns of long time series wetland changes extracted from remote sensing imagery through cover frequency maps and landscape indices. The hydrological data, land use/land cover change maps, and statistical data were combined to explore the driving factors of wetland change over the past 40 years. The results showed that the wetland area increased by 2242.96 km2 (90.72%) from 1977 to 2019. The wetland area increased at 268.01 km2/a from the 1970s to the early 1990s, with a fluctuating change since 1994. Wetlands are mainly distributed in the transition zone between the delta and the Aral Sea, accounting for approximately 81.61% of the total wetland area. The proportion of wetlands on the east and west sides significantly expanded along the canal system, increasing from 0.13% in 1977 to 9.74% in 2019. The degree of fragmentation of the wetland landscape in the entire delta increased by 1.7 times. With decreasing runoff into the delta, the expansion of cultivated land and the construction of reservoirs and irrigation canals changed the spatio-temporal distribution of water resources. In contrast, agricultural irrigation has raised the groundwater level. Human activities, such as the expansion of cultivated land and the construction of reservoirs and irrigation canals, are the main reasons for the spatio-temporal changes in wetland distribution, and exacerbated the decrease in water flow into the Aral Sea and its shrinkage. The restoration of wetlands is not a proof of ecological improvement, it resulted from the redistribution of water resources in the delta. The increase in wetland area represents an apparent ecological contrast with the drying up of the Aral Sea, and the phenomenon is not conducive to the ecological restoration of the delta.