Spatio-temporal analysis of remote sensing images provides early warning signals of forest mortality

Ecosystems are under unprecedented pressures, reflected in rapid changes in the regime of disturbances that may cause negative impacts on them. This highlights the importance of characterizing the state of an ecosystem and its response to disturbances, which is known as a notoriously difficult task. The state-of-the-art knowledge has been tested rarely in real ecosystems for a number of reasons such as mismatches between the time scale of ecosystem processes and data accessibility as well as weaknesses in the performance of available methods. This study aims to use remotely sensed spatio-temporal data to identify early warning signals of forest mortality using satellite images. For this purpose, I propose a new approach that measures local spatial autocorrelation (using local Moran’s I and local Geary’s c method) at each time, which proved to produce robust results in multiple different study sites examined in this article. This new approach successfully generates early warning signals from time series of local spatial autocorrelation values in unhealthy study sites 2 years prior to forest mortality occurrence. Furthermore, I develop a new R package, called “stew”, that enables users to explore spatio-temporal analysis of ecosystem state changes. This work corroborates the suggestion that spatio-temporal indicators have the potential to diagnose early warning signals to identify upcoming climate-induced forest mortality, up to two years before its occurrence.

A spatial-temporal study of complementary and alternative medicine (CAM) by type: exploring localization economies implications in urban areas in Ontario

Objectives This study adds to the geography of complementary and alternative medicine (CAM) literature by comparing the spatial-temporal patterns of five types of CAM within 19 cities in light of clustering benefits from localization economies. Methods CAM office location points and nearest neighbour, standard distance, local spatial autocorrelation, and Mann–Whitney analyses are utilized to test potential clustering tendencies of CAM types over time. Results It is shown that ‘within’ (chiropractors near chiropractors, for example) and ‘amongst’ (chiropractors proximate to other CAM types) spatial clustering occurs in 2007 and 2017. This implies the persistent influence of localization economies. Conclusions Continued clustering of CAM within urban locations already replete with CAM offices will widen spatial disparities through time. This has implications for policy-makers concerned with dispersing medical resources over space for better accessibility.

A longitudinal study of e-commerce diversity in Europe

Owing to increased access to the Internet and the development of electronic commerce, e-commerce has become a common method of shopping in all countries. The purpose of this study is more precisely to research e-commerce diversity in Europe at the regional level and develop the conception of “E-commerce Supply Chain Management”. Statistical data derived from the European Statistical Office were applied to analyse the spatial diversity of e-retailing. Assessments of the regional diversity of e-retailing applied geographic information systems and exploratory spatial data analysis methods such us global and local spatial autocorrelation statistics. Clusters of regions with similar household preferences related to online shopping were identified. A spatial visualisation of the e-retailing diversity phenomenon may be utilised for the reconfiguration of supply chains and to adapt them to actual household preferences related to shopping methods.

Recognizing the Aggregation Characteristics of Extreme Precipitation Events Using Spatio-Temporal Scanning and the Local Spatial Autocorrelation Model

Precipitation is an essential climate variable in the hydrologic cycle. Its abnormal change would have a serious impact on the social economy, ecological development and life safety. In recent decades, many studies about extreme precipitation have been performed on spatio-temporal variation patterns under global changes; little research has been conducted on the regionality and persistence, which tend to be more destructive. This study defines extreme precipitation events by percentile method, then applies the spatio-temporal scanning model (STSM) and the local spatial autocorrelation model (LSAM) to explore the spatio-temporal aggregation characteristics of extreme precipitation, taking China in July as a case. The study result showed that the STSM with the LSAM can effectively detect the spatio-temporal accumulation areas. The extreme precipitation events of China in July 2016 have a significant spatio-temporal aggregation characteristic. From the spatial perspective, China’s summer extreme precipitation spatio-temporal clusters are mainly distributed in eastern China and northern China, such as Dongting Lake plain, the Circum-Bohai Sea region, Gansu, and Xinjiang. From the temporal perspective, the spatio-temporal clusters of extreme precipitation are mainly distributed in July, and its occurrence was delayed with an increase in latitude, except for in Xinjiang, where extreme precipitation events often take place earlier and persist longer.

Spatial Dimension of Unemployment: Space-Time Analysis Using Real-Time Accessibility in Czechia

This paper focuses on the analysis of unemployment data in Czechia on a very detailed spatial structure and yearly, extended time series (2002–2019). The main goal of the study was to examine the spatial dimension of disparities in regional unemployment and its evolutionary tendencies on a municipal level. To achieve this goal, global and local spatial autocorrelation methods were used. Besides spatial and space-time analyses, special attention was given to spatial weight matrix selection. The spatial weights were created according to real-time accessibilities between the municipalities based on the Czech road network. The results of spatial autocorrelation analyses based on network spatial weights were compared to the traditional distance-based spatial weights. Despite significant methodological differences between applied spatial weights, the resulting spatial pattern of unemployment proved to be very similar. Empirically, relative stability of spatial patterns of unemployment with only slow shift of differentiation from macro- to microlevels could be observed.

Mapping of Spatial Distribution and Spatial Autocorrelation Patterns of Poverty in All Regencies/Cities in Indonesia

Poverty alleviation programs in Indonesia are the same and uniform in all regions. Of course this ignores the characteristics and causes of poverty that vary in each region. The uniformity of poverty alleviation programs affects the slow pace of decline in the poor population. Spatial influence on poverty can be identified by spatial autocorrelation; there is a relationship of poverty in one region with other regions that are closed together. This study was aimed to analyzing poverty spatial distribution in all regencies/cities in Indonesia; analyzing the spatial distribution patterns of poverty in all regencies/cities in Indonesia; and knowing local spatial autocorrelation of poverty in all regencies/cities in Indonesia. The research methods used are Moran Index analysis, Moran’s scatterplot analysis, and Local Indicators of Spatial Autocorrelation (LISA) analysis. The analysis results show that the highest average of poor population percentage was in Papua and the lowest one was in Kalimantan. The results of analysis of Moran Index showed that the spatial distribution pattern of poverty in regencies/cities in Indonesia was clustered, it was called by poverty pocket. Pockets of poverty that occured do not correspond to government administrative boundaries, therefore poverty alleviation needs an integrative approach. In addition, this study also results that not all regencies/cities have significant spatial autocorrelation. This means that not all poverty conditions in a regencies/cities have a relationship with other regencies/cities. The fact that there are heterogeneity of poverty characteristics like this shows that poverty alleviation programs must vary in each regency/city. Keywords: City, LISA, Moran, Povety, Regency, Spatial

Phenylketonuria incidence in China between 2013 and 2017 based on data from the Chinese newborn screening information system: a descriptive study

ObjectiveThis study examines the incidence and spatial clustering of phenylketonuria (PKU) in China between 2013 and 2017.MethodsData from the Chinese Newborn Screening Information System were analysed to assess PKU incidence with 95% CIs by province, region and disease severity. Spatial clustering of PKU cases was analysed using global and local spatial autocorrelation analysis in the geographic information system.ResultsThe database contained 4925 neonates with confirmed PKU during the study period, corresponding to an incidence of 6.28 (95% CI: 6.11 to 6.46) per 100 000 neonates screened. Incidence was highest in the provinces of Gansu, Ningxia and Qinghai, where it ranged from 19.00 to 28.63 per 100 000 neonates screened. Overall incidence was higher in the northern part of the country, where classical disease predominated, than in the southern part, where mild disease predominated. PKU cases clustered spatially (global Moran’s I=0.3603,Z=5.3097, p<0.001), and local spatial autocorrelation identified four northern provinces as high–high clusters (Gansu, Qinghai, Ningxia and Shanxi).ConclusionsChina shows an intermediate PKU incidence among countries, and incidence differs substantially among Chinese provinces and between northern and southern regions. Our results suggest the need to focus efforts on screening, diagnosing and treating PKU in high-incidence provinces.