Detection of temporal changes in the spatial distribution of cancer rates using local Moran’s I and geostatistically simulated spatial neutral models

This paper presents the first application of spatially correlated neutral models to the detection of changes in mortality rates across space and time using the local Morans I statistic. Sequential Gaussian simulation is used to generate realizations of the spatial distribution of mortality rates under increasingly stringent conditions: 1) reproduction of the sample histogram, 2) reproduction of the pattern of spatial autocorrelation modeled from the data, 3) incorporation of regional background obtained by geostatistical smoothing of observed mortality rates, and 4) incorporation of smooth regional background observed at a prior time interval. The simulated neutral models are then processed using two new spatio-temporal variants of the Morans I statistic, which allow one to identify significant changes in mortality rates above and beyond past spatial patterns. Last, the results are displayed using an original classification of clusters/outliers tailored to the space-time nature of the data. Using this new methodology the space-time distribution of cervix cancer mortality rates recorded over all US State Economic Areas (SEA) is explored for 9 time periods of 5 years each. Incorporation of spatial autocorrelation leads to fewer significant SEA units than obtained under the traditional assumption of spatial independence, confirming earlier claims that Type I errors may increase when tests using the assumption of independence are applied to spatially correlated data. Integration of regional background into the neutral models yields substantially different spatial clusters and outliers, highlighting local patterns which were blurred when local Morans I was applied under the null hypothesis of constant risk.This research was funded by grants R01 CA92669 and 1R43CA105819-01 from the National Cancer Institute and R43CA92807 under the Innovation in Biomedical Information Science and Technology Initiative at the National Institute of Health. The views stated in this publication are those of the authors and do not necessarily represent the official views of the NCI. The authors also thank three anonymous reviewers for their comments that helped improve the presentation of the methodology.     

Dynamics of inequality and growth in Europe: may spatial models solve the puzzle?

Journal of Geographical Systems - The link between income inequality and economic growth remains poorly understood. The global economic crisis challenged numerous growth studies by highlighting...     

NDVI dynamics as reflected in climatic variables: spatial and temporal trends – a case study of Hungary

Understanding climate change and revealing its future paths on a local level is a great challenge for the future. Beside the expanding sets of available climatic data, satellite images provide a valuable source of information. In our study we aimed to reveal whether satellite data are an appropriate way to identify global trends, given their shorter available time range. We used the CARPATCLIM (CC) database (1961–2010) and the MODIS NDVI images (2000–2016) and evaluated the time period covered by both (2000–2010). We performed a regression analysis between the NDVI and CC variables, and a time series analysis for the 1961–2008 and 2000–2008 periods at all data points. The results justified the belief that maximum temperature (TMAX), potential evapotranspiration and aridity all have a strong correlation with the NDVI; furthermore, the short period trend of TMAX can be described with a functional connection with its long period trend. Consequently, TMAX is an appropriate tool as an explanatory variable for NDVI spatial and temporal variance. Spatial pattern analysis revealed that with regression coefficients, macro-regions reflected topography (plains, hills and mountains), while in the case of time series regression slopes, it justified a decreasing trend from western areas (Transdanubia) to eastern ones (The Great Hungarian Plain). This is an important consideration for future agricultural and land use planning; i.e. that western areas have to allow for greater effects of climate change.     

Spatiotemporal change patterns of urban lakes in China’s major cities between 1990 and 2015

China has experienced unprecedented urbanization in the past decades, resulting in dramatic changes in the physical, limnological, and hydrological characteristics of lakes in urban landscapes. However, the spatiotemporal dynamics in distribution and abundance of urban lakes in China remain poorly understood. Here, we characterized the spatiotemporal change patterns of urban lakes in China’s major cities between 1990 and 2015 using remote-sensing data and landscape metrics. The results showed that the urban lake landscape patterns have experienced drastic changes over the past 25 years. The total surface area of the urban lakes has decreased by 17,620.02?ha, a decrease of 24.22%, with a significant increase in the landscape fragmentation and a reduction in shape complexity. We defined three lake-shrinkage types and found that vanishment was the most common lake-shrinkage pattern, followed by edge-shrinkage and tunneling in terms of lake area. Moreover, we also found that urban sprawl was the dominant driver of the lake shrinkage, accounting for 67.89% of the total area loss, and the transition from lakes to cropland was also an important factor (19.86%). This study has potential for providing critical baseline information for government decision-making in lake resources management and urban landscape design.     

Expansion dynamics of deciduous rubber plantations in Xishuangbanna,China during 2000–2010

Monoculture rubber plantations have been replacing tropical rain forests substantially in Southern China and Southeast Asia over the past several decades, which have affected human wellbeing and ecosystem services. However, to the best of our knowledge on the extent of rubber plantation expansion and their stand ages is limited. We tracked the spatiotemporal dynamics of deciduous rubber plantations in Xishuangbanna, the second largest natural rubber production region in China, from 2000 to 2010 using time-series data from the Phased Array type L-band Synthetic Aperture Radar (PALSAR), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS). We found that rubber plantations have been expanding across a gradient from the low-elevation plains to the high elevation mountains. The areas of deciduous rubber plantations with stand ages ≤5, 6–10, and ≥11-year old were ~1.2 × 10⁵ ha, ~0.8 × 10⁵ ha, and ~2.9 × 10⁵ ha, respectively. Older rubber plantations were mainly located in low-elevation and species-rich regions (500–900 m) and younger rubber trees were distributed in areas of relative high-elevation with fragile ecosystems. Economic and market factors have driven the expansion of rubber plantations, which is not only a threat to biodiversity and environmental sustainability, but also a trigger for climatic disasters. This study illustrates that the integration of microwave, optical, and thermal data is an effective method for mapping deciduous rubber plantations in tropical mountainous regions and determining their stand ages. Our results demonstrate the spatiotemporal pattern of rubber expansions over the first decade of this century.     

A preliminary evaluation of the performance of multiple ionospheric models in low- and mid-latitude regions of China in 2010–2011

Ionospheric delay is a dominant error source in Global Navigation Satellite System (GNSS). Single-frequency GNSS applications require ionospheric correction of signal delay caused by the charged particles in the earth’s ionosphere. The Chinese Beidou system is developing its own ionospheric model for single-frequency users. The number of single-frequency GNSS users and applications is expected to grow fast in the next years in China. Thus, developing an appropriate ionospheric model is crucially important for the Chinese Beidou system and worldwide single-frequency Beidou users. We study the performance of five globally accessible ionospheric models Global Ionospheric Map (GIM), International Reference Ionosphere (IRI), Parameterized Ionospheric Model (PIM), Klobuchar and NeQuick in low- and mid-latitude regions of China under mid-solar activity condition. Generally, all ionospheric models can reproduce the trend of diurnal ionosphere variations. It is found that all the models have better performances in mid-latitude than in low-latitude regions. When all the models are compared to the observed total electron content (TEC) data derived from GIM model, the IRI model (2012 version) has the best agreement with GIM model and the NeQuick has the poorest agreement. The RMS errors of the IRI model using the GIM TEC as reference truth are about 3.0–10.0 TECU in low-latitude regions and 3.0–8.0 TECU in mid-latitude regions, as observed during a period of 1 year with medium level of solar activity. When all the ionospheric models are ingested into single-frequency precise point positioning (PPP) to correct the ionospheric delays in GPS observations, the PIM model performs the best in both low and mid-latitudes in China. In mid-latitude, the daily single-frequency PPP accuracy using PIM model is ~10 cm in horizontal and ~20 cm in up direction. At low-latitude regions, the PPP error using PIM model is 10–20 cm in north, 30–40 cm in east and ~60 cm in up component. The single-frequency PPP solutions indicate that NeQuick model has the lowest accuracy among all the models in both low- and mid-latitude regions of China. This study suggests that the PIM model may be considered for single-frequency GNSS users in China to achieve a good positioning accuracy in both low- and mid-latitude regions.     

Monitoring intra and inter annual dynamics of forest degradation from charcoal production in Southern Africa with Sentinel – 2 imagery

The contribution of forest degradation to changes in forest carbon stocks remains poorly quantified and constitutes a main source of uncertainty in the forest carbon budget. Charcoal production is a major source of forest degradation in sub-Saharan Africa. We used multitemporal Sentinel-2 imagery to monitor and quantify forest degradation extent in the main supplying area of a major urban center of southern Africa over a 4-year period. We implemented an indirect approach combining Sentinel-2 imagery to map kiln and field measurements to estimate AGB removals and carbon losses from charcoal production. This work generated 10 m resolution maps of forest degradation extent from charcoal production in the study area at quarterly intervals from 2016–2019. These maps reveal an intense and rapid forest degradation process and expose the spatial and temporal patterns of forest degradation from charcoal production with high detail. The total area under charcoal production over the study period reached 26,647 ha (SD = 320.8) and the forest degradation front advanced 10.5 km in a 4-year period, with an average of 19.4 ha of woodlands degraded daily. By the end of 2019, charcoal production disturbed most mopane stands in the study area and woodland fragmentation increased in 70.4 % of the mopane woodlands. We estimated that charcoal production was responsible for 2,568,761 Mg (SD = 42,130) of aboveground biomass extracted from the forest and 1,284,381 Mg (SD = 21,075) of carbon loss. The magnitude of these figures underlines the relevance of charcoal production as a main cause of forest cover change and remarks the existing uncertainties in the quantification of forest degradation processes. These results illustrate the potential of multitemporal medium resolution imagery to quantify forest degradation in sub-Saharan Africa and improve REDD + Monitoring, Reporting, and Verification systems in compliance with international reporting commitments.     

The dynamics in the distribution of mangrove forests in Pichavaram,South India – perception by user community and remote sensing

This study aims to monitor the forest cover of Pichavaram mangroves, South India over a period of 40 years using remote sensing, and to record the status of mangroves as perceived by the local community. Out of 1471 ha of total reserved forest area, mangroves occupy 906 ha. The remote sensing maps show that there was a loss of 471 ha from 1970 to 1991 and a gain of 531 ha in 2011. Nearby 20 hamlets depend on mangroves for their livelihood. A village survey conducted at Pichavaram shows that more than 90% of the local community is well aware of the prevailing species, their importance especially after the 2004 tsunami and the impact of management practices, increased rainfall and contribution of local community in the recent increased area of mangroves. The same can be noticed from the high-resolution IKONOS image showing the artificial canal network in the restored region and from rainfall records.     

Temperature dependence of variations in the end of the growing season from 1982 to 2012 on the Qinghai–Tibetan Plateau

A growing number of studies have focused on variations in vegetation phenology and their correlations with climatic factors. However, there has been little research on changes in spatial heterogeneity with respect to the end of the growing season (EGS) and on responses to climate change for alpine vegetation on the Qinghai–Tibetan Plateau (QTP). In this study, the satellite-derived normalized difference vegetation index (NDVI) and the meteorological record from 1982 to 2012 were used to characterize the spatial pattern of variations in the EGS and their relationship to temperature and precipitation on the QTP. Over the entire study period, the EGS displayed no statistically significant trend; however, there was a strong spatial heterogeneity throughout the plateau. Those areas showing a delaying trend in the EGS were mainly distributed in the eastern part of the plateau, whereas those showing an advancing trend were mostly scattered throughout the western part. Our results also showed that change in the vegetation EGS was more closely correlated with air temperature than with precipitation. Nonetheless, the temperature sensitivity of the vegetation EGS became lower as aridity increased, suggesting that precipitation is an important regulator of the response of the vegetation EGS to climate warming. These results indicate spatial differences in key environmental influences on the vegetation EGS that must be taken into account in current phenological models, which are largely driven by temperature.     

Analysing spatial–temporal changes in rice cultivation practices in the Senegal River Valley using MODIS time-series and the PhenoRice algorithm

In this study we used the PhenoRice algorithm to track recent variations of rice cultivation practices along the Senegal River Valley. Time series of MODIS imagery with 250 m spatial resolution and a nominal 8-days frequency were used as input for the algorithm to map the spatial and temporal variations of rice cultivated area and of several important phenological metrics (e.g., crop establishment and harvesting dates, length of season) for the 2003–2016 period in both the dry and the wet rice cultivation seasons. Comparison between PhenoRice results and ancillary and field data available for the Senegal part of the study area showed that the algorithm is able to track the interannual variations of rice cultivated area, despite the total detected rice area being consistently underestimated. PhenoRice estimates of crop establishment and harvesting dates resulted accurate when compared with field observations available for two sub-regions for a period of 10 years, and thus allow assessing interannual variability and tracking changes in agronomic practices. An analysis of interannual trends of rice growing practices based on PhenoRice results highlighted a clear shift of rice cultivation from the wet to the dry season starting approximately from 2008. The shift was found to be particularly evident in the delta part of the SRV. Additionally, a statistically significant trend was revealed starting 2006 towards a longer dry season (r² = 0.81; Slope = 1.24 days y⁻¹) and a shorter wet season (r² = 0.65; Slope = 0.53 days y⁻¹). These findings are in agreement with expert knowledge of changes ongoing in the area. In particular the shorter wet season is attributed to shortage of labor and equipment leading to a delay in completion of harvesting operations in the dry season, which led to the adoption of short-duration rice varieties by farmers in the wet season to avoid risk of yield losses due to climatic constraints. Aforementioned results highlight the usefulness of the PhenoRice algorithm for providing insights about recent variations in rice cultivation practices over large areas in developing countries, where high-quality up to date information about changes in agricultural practices are often lacking.     

Conditional versus unconditional industrial agglomeration: disentangling spatial dependence and spatial heterogeneity in the analysis of ICT firms’ distribution in Milan

A series of recent papers have introduced some explorative methods based on Ripley’s K-function (Ripley in J R Stat Soc B 39(2):172–212, 1977) analyzing the micro-geographical patterns of firms. Often the spatial heterogeneity of an area is handled by referring to a case–control design, in which spatial clusters occur as over-concentrations of firms belonging to a specific industry as opposed to the distribution of firms in the whole economy. Therefore, positive, or negative, spatial dependence between firms occurs when a specific sector of industry is seen to present a more aggregated pattern (or more dispersed) than is common in the economy as a whole. This approach has led to the development of relative measures of spatial concentration which, as a consequence, are not straightforwardly comparable across different economies. In this article, we explore a parametric approach based on the inhomogeneous K-function (Baddeley et al. in Statistica Nederlandica 54(3):329–350, 2000) that makes it possible to obtain an absolute measure of the industrial agglomeration that is also able to capture spatial heterogeneity. We provide an empirical application of the approach taken with regard to the spatial distribution of high-tech industries in Milan (Italy) in 2001.     

Forecasting China’s GDP at the pixel level using nighttime lights time series and population images

China’s rapid economic development greatly affected not only the global economy but also the entire environment of the Earth. Forecasting China’s economic growth has become a popular and essential issue but at present, such forecasts are nearly all conducted at the national scale. In this study, we use nighttime light images and the gridded Landscan population dataset to disaggregate gross domestic product (GDP) reported at the province scale on a per pixel level for 2000–2013. Using the disaggregated GDP time series data and the statistical tool of Holt–Winters smoothing, we predict changes of GDP at each 1 km × 1 km grid area from 2014 to 2020 and then aggregate the pixel-level GDP to forecast economic growth in 23 major urban agglomerations of China. We elaborate and demonstrate that lit population (brightness of nighttime lights × population) is a better indicator than brightness of nighttime lights to estimate and disaggregate GDP. We also show that our forecast GDP has high agreement with the National Bureau of Statistics of China’s demographic data and the International Monetary Fund’s predictions. Finally, we display uncertainties and analyze potential errors of this disaggregation and forecast method.     

Theory and Methods of the Earth＇s Gravity Field Model Recovery from GOCE Data

研究GOCE卫星测量恢复地球重力场模型的理论与方法。论文的主要工作和创新点有： （1）建立扰动重力梯度张量各分量没有奇异性的详细计算模型,解决重力梯度张量Txx分量在两极地区计算的奇异性难题。     

Data democracy – increased supply of geospatial information and expanded participatory processes in the production of data

The global landscape in the supply, co-creation and use of geospatial data is changing very rapidly with new satellites, sensors and mobile devices reconfiguring the traditional lines of demand and supply and the number of actors involved. In this paper we chart some of these technology-led developments and then focus on the opportunities they have created for the increased participation of the public in generating and contributing information for a wide range of uses, scientific and non. Not all this information is open or geospatial, but sufficiently large portions of it are to make it one of the most significant phenomena of the last decade. In fact, we argue that while satellite and sensors have exponentially increased the volumes of geospatial information available, the participation of the public is transformative because it expands the range of participants and stakeholders in society using and producing geospatial information, with opportunities for more direct participation in science, politics and social action.     

Using Landsat Thematic Mapper records to map land cover change and the impacts of reforestation programmes in the borderlands of southeast Yunnan,China: 1990–2010

At the beginning of the new millennium, after a severe drought and destructive floods along the Yangtze River, the Chinese government implemented two large ecological rehabilitation and reforestation projects: the Natural Forest Protection Programme and the Sloping Land Conversion Programme. Using Landsat data from a decade before, during and after the inception of these programmes, we analyze their impacts along with other policies on land use, land cover change (LULCC) in southwest China. Our goal is to quantify the predominant land cover changes in four borderland counties, home to tens of thousands of ethnic minority individuals. We do this in three time stages (1990, 2000 and 2010). We use support vector machines as well as a transition matrix to monitor the land cover changes. The land cover classifications resulted in an overall accuracy and Kappa coefficient for forested area and cropland of respectively 91% (2% confidence interval) and 0.87. Our results suggest that the total forested area observed increased 3% over this 20-year period, while cropland decreased slightly (0.1%). However, these changes varied over specific time periods: forested area decreased between 1990 and 2000 and then increased between 2000 and 2010. In contrast, cropland increased and then decreased. These results suggest the important impacts of reforestation programmes that have accelerated a land cover transition in this region. We also found large changes in LULC occurring around fast growing urban areas, with changes in these peri-urban zones occurring faster to the east than west. This suggests that differences in socioeconomic conditions and specific local and regional policies have influenced the rates of forest, cropland and urban net changes, disturbances and net transitions. While it appears that a combination of economic growth and forest protection in this region over the past 20 years has been fairly successful, threats like drought, other extreme weather events and land degradation remain.     

Understanding long-term (1982–2013) patterns and trends in winter wheat spring green-up date over the North China Plain

Monitoring the spring green-up date (GUD) has grown in importance for crop management and food security. However, most satellite-based GUD models are associated with a high degree of uncertainty when applied to croplands. In this study, we introduced an improved GUD algorithm to extract GUD data for 32 years (1982–2013) for the winter wheat croplands on the North China Plain (NCP), using the third-generation normalized difference vegetation index form Global Inventory Modeling and Mapping Studies (GIMMS3g NDVI). The spatial and temporal variations in GUD with the effects of the pre-season climate and soil moisture conditions on GUD were comprehensively investigated. Our results showed that a higher correlation coefficient (r = 0.44, p < 0.01) and lower root mean square error (22 days) and bias (16 days) were observed in GUD from the improved algorithm relative to GUD from the MCD12Q2 phenology product. In spatial terms, GUD increased from the southwest (less than day of year (DOY) 60) to the northeast (more than DOY 90) of the NCP, which corresponded to spatial reductions in temperature and precipitation. GUD advanced in most (78%) of the winter wheat area on the NCP, with significant advances in 37.8% of the area (p < 0.05). GUD occurred later at high altitudes and in coastal areas than in inland areas. At the interannual scale, the average GUD advanced from DOY 76.9 in the 1980s (average 1982–1989) to DOY 73.2 in the 1990s (average 1991–1999), and to DOY 70.3 after 2000 (average 2000–2013), indicating an average advance of 1.8 days/decade (r = 0.35, p < 0.05). Although GUD is mainly controlled by the pre-season temperature, our findings underline that the effect of the pre-season soil moisture on GUD should also be considered. The improved GUD algorithm and satellite-based long-term GUD data are helpful for improving the representation of GUD in terrestrial ecosystem models and enhancing crop management efficiency.