Mapping the transcriptome: Realizing the full potential of spatial data analysis.

RNA sequencing in situ allows for whole-transcriptome characterization at high resolution, while retaining spatial information. These data present an analytical challenge for bioinformatics-how to leverage spatial information effectively? Properties of data with a spatial dimension require special handling, which necessitate a different set of statistical and inferential considerations when compared to non-spatial data. The geographical sciences primarily use spatial data and have developed methods to analye them. Here we discuss the challenges associated with spatial analysis and examine how we can take advantage of practice from the geographical sciences to realize the full potential of spatial information in transcriptomic datasets.

Blockchain-Based Method for Spatial Retrieval and Verification of Remote Sensing Images.

Remote sensing image is a vital basis for land management decisions. The protection of remote sensing images has seen the application of blockchain's notarization function by many scholars. Yet, research on efficient retrieval of such images on the blockchain remains sparse. Addressing this issue, this paper introduces a blockchain-based spatial index verification method using Hyperledger Fabric. It linearizes the spatial information of remote sensing images via Geohash and integrates it with LSM trees for effective retrieval and verification. The system also incorporates IPFS as an underlying storage unit for Hyperledger Fabric, ensuring the safe storage and transmission of images. The experiments indicate that this method significantly reduces the latency in data retrieval and verification without impacting the write performance of Hyperledger Fabric, enhancing throughput and providing a solid foundation for efficient blockchain-based verification of remote sensing images in land registry systems.

Disease mapping for spatially semi-continuous data by estimating equations with application to dengue control.

Disease mapping is a research field to estimate spatial pattern of disease risks so that areas with elevated risk levels can be identified. The motivation of this article is from a study of dengue fever infection, which causes seasonal epidemics in almost every summer in Taiwan. For analysis of zero-inflated data with spatial correlation and covariates, current methods would either cause a computational burden or miss associations between zero and non-zero responses. In this article, we develop estimating equations for a mixture regression model that accommodates spatial dependence and zero inflation for study of disease propagation. Asymptotic properties for the proposed estimates are established. A simulation study is conducted to evaluate performance of the mixture estimating equations; and a dengue dataset from southern Taiwan is used to illustrate the proposed method.

The effect of physician density on colorectal cancer stage at diagnosis: causal inference methods for spatial data applied on regional-level data.

BACKGROUND: The early detection of colorectal cancer (CRC) through regular screening decreases its incidence and mortality rates and improves survival rates. Norway has an extremely high percentage of CRC cases diagnosed at late stages, with large variations across municipalities and hospital catchment areas. This study examined whether the availability of physicians related to CRC primary diagnosis and preoperative investigations, or physician density, contributes to the observed geographical differences in late-stage incidence rates. METHOD: Municipality-level data on CRC stage at diagnosis were obtained from the Cancer Registry of Norway for the period 2012-2020. Physician density was calculated as the number of physicians related to CRC investigations, general practitioners (GPs) and specialists per 10,000 people, using physician counts per municipality and hospital areas from Statistics Norway. The relationship was examined using a novel causal inference method for spatial data-neighbourhood adjustment method via spatial smoothing (NA approach)-which allowed for studying the region-level effect of physician supply on CRC outcome by using spatially referenced data and still providing causal relationships. RESULTS: According to the NA approach, an increase in one general practitioner per 10,000 people will result in a 3.6% (CI -0.064 to -0.008) decrease in late-stage CRC rates. For specialists, there was no evidence of a significant correlation with late-stage CRC distribution, while for both groups, GPs and specialists combined, an increase of 1 physician per 10,000 people would be equal to an average decrease in late-stage incidence rates by 2.79% (CI -0.055 to -0.001). CONCLUSION: The study confirmed previous findings that an increase in GP supply will significantly improve CRC outcomes. In contrast to previous research, this study identified the importance of accessibility to both groups of physicians-GPs and specialists. If GPs encounter insufficient workforces in hospitals and long delays in colonoscopy scheduling, they will less often recommend colonoscopy examinations to patients. This study also highlighted the efficiency of the novel methodology for spatially referenced data, which allowed us to study the effect of physician density on cancer outcomes within a causal inference framework.

A study of primary health care service efficiency and its spatial correlation in China.

BACKGROUND: China's primary health care system has undergone major changes since the new round of medical reform in 2009, but the current status of primary health care institution service efficiency is still unsatisfactory. The purpose of this study is to compare and evaluate the China's primary health care institution service efficiency and provide a reference for improving the efficiency and promoting the development of primary health care institution. METHODS: Based on panel data of 31 provinces (municipalities directly under the central government and autonomous regions) in mainland China from 2011 to 2020, using the super efficiency slack-based measure-data envelopment analysis model, to analyze the data from a static perspective, and the changes in the efficiency of primary health care services were analyzed from a dynamic perspective by using the Malmquist index method. Spatial autocorrelation analysis method was used to verify the spatial correlation of primary health care service efficiency among various regions. RESULTS: The number of Primary health care institutions increased from 918,000 in 2011 to 970,000 in 2020. The average primary health care institution service efficiency in the northeastern region including Jilin (0.324), Heilongjiang (0.460), Liaoning (0.453) and northern regions such as Shaanxi (0.344) and Neimenggu (0.403) was at a low level, while the eastern coastal regions such as Guangdong (1.116), Zhejiang (1.211), Shanghai (1.402) have higher average service efficiency levels. The global Moran's I showed the existence of spatial autocorrelation, and the local Moran's I index suggested that the problem of uneven regional development was prominent, showing a contiguous regional distribution pattern. Among them, H-H (high-efficiency regions) were mainly concentrated in Jiangsu, Anhui and Shanghai, and L-L regions (low-efficiency regions) were mostly in northern and northeastern China. CONCLUSION: The service efficiency of primary health care institution in China showed a rising trend in general, but the overall average efficiency was still at a low level, and there were significant geographical differences, which showed a spatial distribution of "high in the east and low in the west, high in the south and low in the north". The northwestern region, after receiving relevant support, has seen a rapid development of primary health care, and its efficiency was steadily improving and gradually reaching a high level. The average primary health care institution service efficiency in the northeastern region including the northern region of China was at a low level, while the average efficiency in the eastern coastal region and some economically developed regions was high, which also verifies the dependence and high symbiosis of primary health care institution service efficiency on regional economy.

Discovery and Characterisation of Forward Line Formations at Centre Bounces in the Australian Football League.

The extent of player formation usage and the characteristics of player arrangements are not well understood in Australian football, unlike other team-based invasion sports. Using player location data from all centre bounces in the 2021 Australian Football League season; this study described the spatial characteristics and roles of players in the forward line. Summary metrics indicated that teams differed in how spread out their forward players were (deviation away from the goal-to-goal axis and convex hull area) but were similar with regard to the centroid of player locations. Cluster analysis, along with visual inspection of player densities, clearly showed the presence of different repeated structures or formations used by teams. Teams also differed in their choice of player role combinations in forward lines at centre bounces. New terminology was proposed to describe the characteristics of forward line formations used in professional Australian Football.

Smartphone LiDAR Data: A Case Study for Numerisation of Indoor Buildings in Railway Stations.

The combination of LiDAR with other technologies for numerisation is increasingly applied in the field of building, design, and geoscience, as it often brings time and cost advantages in 3D data survey processes. In this paper, the reconstruction of 3D point cloud datasets is studied, through an experimental protocol evaluation of new LiDAR sensors on smartphones. To evaluate and analyse the 3D point cloud datasets, different experimental conditions are considered depending on the acquisition mode and the type of object or surface being scanned. The conditions allowing us to obtain the most accurate data are identified and used to propose which acquisition protocol to use. This protocol seems to be the most adapted when using these LiDAR sensors to digitise complex interior buildings such as railway stations. This paper aims to propose: (i) a methodology to suggest the adaptation of an experimental protocol based on factors (distance, luminosity, surface, time, and incidence) to assess the precision and accuracy of the smartphone LiDAR sensor in a controlled environment; (ii) a comparison, both qualitative and quantitative, of smartphone LiDAR data with other traditional 3D scanner alternatives (Faro X130, VLX, and Vz400i) while considering three representative building interior environments; and (iii) a discussion of the results obtained in a controlled and a field environment, making it possible to propose recommendations for the use of the LiDAR smartphone at the end of the numerisation of the interior space of a building.

Maximizing agricultural reuse of recycled nutrients: A spatially explicit assessment of environmental consequences and costs.

Recovering nutrients from organic materials to reduce artificial fertilizer inputs requires the implementation of processing technologies and can involve considerable logistics and transportation costs. Reducing such costs by directly applying organic materials to agricultural land can contribute to pollution due to potential contaminants and unbalanced nutrient ratios. Assessing the cost of increased recycling requires a spatially explicit approach because availability of organic materials, nutrient demand and agro-ecosystem properties vary spatially. A multi-objective model was developed to estimate the trade-offs between costs of nutrient recovery and improvements in nutrient distribution for a case study area in The Netherlands. The evaluated recovery processes included solid-liquid separation followed by reverse osmosis to recover nutrients from pig manure which was compared to a conventional process via hygienisation and export. Results indicate that, even in a nutrient saturated area, replacement potential of artificial nitrogen (N) and phosphorus (P) fertilizers through locally reclaimed nutrients is limited to about 17% N and 55% P. A cost optimum was found when about 48% of the initial pig manure quantities were processed via nutrient recovery and directed to land. Increasing manure processing for nutrient recovery led to a redistribution of nutrients and trace metals (zinc (Zn) and copper (Cu)), resulting in more localized concentration. Zn and Cu were enriched by about 8% and 2%, respectively, when maximizing nutrient recovery. Our generic model offers a methodology to assess the trade-offs between increased recycling and associated spatial effects to facilitate sustainable recycling infrastructures for achieving more circular agriculture.

What determines how green crop farming can get? Spatial factors or green awareness spillovers.

This study intends to examine if traditional local factors (seasonal weather conditions) and/or green awareness spillovers contribute to the spatial dependency of farmland allocated to organic farming after its uptake in Taiwan. To investigate the push and pull factors to improve the policy targeting on environmentally-friendly farming practices, we assess spatial autocorrelation of the adoption intensity of organic farming with exploratory analysis, and expand that by exploring how explanatory factors affect the adoption intensity using a spatial Tobit regression analysis, taking into consideration that the adoption intensity is a typical example of censored data. Based on township-level data of 323 townships constructed from 213,534 rice farm households drawn from the 2015 Agriculture Census, we find high-high clusters (hot spots) are mostly in the northern and the eastern parts of Taiwan, whereas the majority of low-low clusters (cold spots) locate in central and southern Taiwan. Such spatial aspects of organic adoption intensity suggest that a spatially targeted program in promoting environmental awareness is pertinent to fostering the development of organic agriculture. The results from the spatial lag Tobit regression estimation provide empirical evidence supporting the role of local weather conditions and green awareness spillovers in explaining the spatial patterns of organic agriculture in Taiwan. In light of the stylized fact that the majority of the rice farm households in Taiwan are small with 84% having farmland areas less than 1 ha, the findings provide practical references to policy practitioners in tailoring farm programs or policies in line with the notion of inclusive and sustainable development.

Multi-objective Allocation Optimization of Soil Conservation Measures Under Data Uncertainty.

Many regions worldwide face soil loss rates that endanger future food supply. Constructing soil and water conservation measures reduces soil loss but comes with high labor costs. Multi-objective optimization allows considering both soil loss rates and labor costs, however, required spatial data contain uncertainties. Spatial data uncertainty has not been considered for allocating soil and water conservation measures. We propose a multi-objective genetic algorithm with stochastic objective functions considering uncertain soil and precipitation variables to overcome this gap. We conducted the study in three rural areas in Ethiopia. Uncertain precipitation and soil properties propagate to uncertain soil loss rates with values that range up to 14%. Uncertain soil properties complicate the classification into stable or unstable soil, which affects estimating labor requirements. The obtained labor requirement estimates range up to 15 labor days per hectare. Upon further analysis of common patterns in optimal solutions, we conclude that the results can help determine optimal final and intermediate construction stages and that the modeling and the consideration of spatial data uncertainty play a crucial role in identifying optimal solutions.

Evaluating the impact of dam construction on extreme shrinkage of Urmia Lake using spatial data.

In this study, the extreme shrinkage of Urmia Lake is investigated, aiming to assess the impact of anthropogenic factors, particularly the over-construction of dams and natural anomalies associated with climate change. Historically available multispectral spatial data obtained from Landsat missions 4-5 TM and Landsat 8 OLI were utilized which totally covers a period of 36 years (1967-2020). Additionally, this data was employed to identify the locations of constructed water reservoirs and determine their construction timelines by analyzing the normalized difference vegetation index (NDVI). To examine the temporal patterns of annual precipitation in the lake basin, we obtained time series data from historical precipitation records, which were then converted into rasterized format. Our findings indicate that approximately 22% of the lake basin has been designated for feeding dam reservoirs. The impact of precipitation anomalies on the lake's water level was found to be relatively less significant when compared to the increased storage capacity of the dams. Furthermore, the construction of dams prior to 2000 contributed to enhancing the lake's stability during periods of drought. However, the substantial increase in the total storage capacity of dams after 2000 has significantly accelerated the shrinkage process. As a result, it was concluded that any effective rescue plan should prioritize ignoring a considerable portion of the reservoirs' storage capacity by releasing stored water, thereby allowing the lake to attain a stable condition.

A Three-Dimensional ResNet and Transformer-Based Approach to Anomaly Detection in Multivariate Temporal-Spatial Data.

Anomaly detection in multivariate time series is an important problem with applications in several domains. However, the key limitation of the approaches that have been proposed so far lies in the lack of a highly parallel model that can fuse temporal and spatial features. In this paper, we propose TDRT, a three-dimensional ResNet and transformer-based anomaly detection method. TDRT can automatically learn the multi-dimensional features of temporal-spatial data to improve the accuracy of anomaly detection. Using the TDRT method, we were able to obtain temporal-spatial correlations from multi-dimensional industrial control temporal-spatial data and quickly mine long-term dependencies. We compared the performance of five state-of-the-art algorithms on three datasets (SWaT, WADI, and BATADAL). TDRT achieves an average anomaly detection F1 score higher than 0.98 and a recall of 0.98, significantly outperforming five state-of-the-art anomaly detection methods.

Africa's spatial data science landscape in the context of covid-19 pandemic.

The emergence of Covid-19 pandemic in late 2019 presented daunting challenges for designing and implementing sustainable solutions at both local and global levels. The situation was dire in many developing economies with limited resources and vulnerable healthcare systems especially in Africa. Spatial data science (SDS) can be adopted and utilized to assist countries and local communities in understanding and effectively responding to Covid-19 pandemic. This article's study reviewed recent literature with the main goal to assess the application of this data-driven and technology-oriented modern approach in addressing Covid-19 in the African continent. Findings indicate that while examples of applications involving traditional geospatial technologies especially geographic information systems are abound, the use of more advanced SDS elements is limited and fragmented. Additionally, various studies leveraged SDS to address one or more complex questions against the backdrop of challenges largely influenced by the digital divide within Africa and across the globe. The article identifies and discusses these challenges as well as opportunities for increased use of SDS in Africa to understand and respond to disasters like Covid-19 and other complex problems. The argument is made for a more complete use of multiple elements of SDS.

Durations of Dockless E-Scooter Trips Before and During the COVID-19 Pandemic in Austin, TX: An Analysis Using Hazard-Based Duration Models.

The pandemic arising from the 2019 coronavirus disease has significantly affected all facets of human life across the world, including economies and transportation systems, thereby changing people's travel behaviors. This research was aimed at exploring the relationship between socio-economic factors and e-scooter trip durations before and during the pandemic. We developed a hazard-based duration approach and estimated multiple spatial and non-spatial models on the basis of 2019 and 2020 dockless e-scooter data collected from the City of Austin's Open Data Portal. The results indicated an overall increase in e-scooter trip durations after the pandemic. Moreover, analysis of variables revealed potential changes in users' behavior before and during the pandemic. In particular, whereas e-scooter trip durations were found to be positively associated with aggregate travel time to work before the pandemic, this trend was reversed during the pandemic. In addition, during the pandemic, e-scooter travel time was positively correlated with the ratio of individuals with bachelor's degrees or greater to those with associate degrees or lower. However, no specific pattern was observed before the pandemic. Lastly, the results showed the presence of disparities within the study area; therefore, it is vital to extend e-scooter service areas to cover underserved communities.

Shared E-Scooter Trajectory Analysis During the COVID-19 Pandemic in Austin, Texas.

By March of 2020, most cities worldwide had enacted stay-at-home public health orders to slow the spread of COVID-19. Restrictions on nonessential travel had extensive impacts across the transportation sector in the short term. This study explores the effects of COVID-19 on shared e-scooters by analyzing route trajectory data in the pre- and during-pandemic periods in Austin, TX, from a single provider. Although total shared e-scooter trips decreased during the pandemic, partially owing to vendors pulling out of the market, this study found average trip length increased, and temporal patterns of this mode did not meaningfully change. A count model of average daily trips by road segment found more trips on segments with sidewalks and bus stops during the pandemic than beforehand. More trips were observed on roads with lower vehicle miles traveled and fewer lanes, which might suggest more cautious travel behavior since there were fewer trips in residential neighborhoods. Stay-at-home orders and vendor e-scooter rebalancing operations inherently influence and can limit trip demand, but the unique trajectory data set and analysis provide cities with information on the road design preferences of vulnerable road users.

Sampling and modelling rare species: Conceptual guidelines for the neglected majority.

Biodiversity conservation faces a methodological conundrum: Biodiversity measurement often relies on species, most of which are rare at various scales, especially prone to extinction under global change, but also the most challenging to sample and model. Predicting the distribution change of rare species using conventional species distribution models is challenging because rare species are hardly captured by most survey systems. When enough data are available, predictions are usually spatially biased towards locations where the species is most likely to occur, violating the assumptions of many modelling frameworks. Workflows to predict and eventually map rare species distributions imply important trade-offs between data quantity, quality, representativeness and model complexity that need to be considered prior to survey and analysis. Our opinion is that study designs need to carefully integrate the different steps, from species sampling to modelling, in accordance with the different types of rarity and available data in order to improve our capacity for sound assessment and prediction of rare species distribution. In this article, we summarize and comment on how different categories of species rarity lead to different types of occurrence and distribution data depending on choices made during the survey process, namely the spatial distribution of samples (where to sample) and the sampling protocol in each selected location (how to sample). We then clarify which species distribution models are suitable depending on the different types of distribution data (how to model). Among others, for most rarity forms, we highlight the insights from systematic species-targeted sampling coupled with hierarchical models that allow correcting for overdispersion and spatial and sampling sources of bias. Our article provides scientists and practitioners with a much-needed guide through the ever-increasing diversity of methodological developments to improve the prediction of rare species distribution depending on rarity type and available data.

Malaria-VisAnalytics: a tool for visual exploratory analysis of Brazilian public malaria data.

BACKGROUND: Data integration and visualisation techniques have been widely used in scientific research to allow the exploitation of large volumes of data and support highly complex or long-lasting research questions. Integration allows data from different sources to be aggregated into a single database comprising variables of interest for different types of studies. Visualisation allows large and complex data sets to be manipulated and interpreted in a more intuitive way. METHODS: Integration and visualisation techniques were applied in a malaria surveillance ecosystem to build an integrated database comprising notifications, deaths, vector control and climate data. This database is accessed through Malaria-VisAnalytics, a visual mining platform for descriptive and predictive analysis supporting decision and policy-making by governmental and health agents. RESULTS: Experimental and validation results have proved that the visual exploration and interaction mechanisms allow effective surveillance for rapid action in suspected outbreaks, as well as support a set of different research questions over integrated malaria electronic health records. CONCLUSION: The integrated database and the visual mining platform (Malaria-VisAnalytics) allow different types of users to explore malaria-related data in a user-friendly interface. Summary data and key insights can be obtained through different techniques and dimensions. The case study on Manaus can serve as a reference for future replication in other municipalities. Finally, both the database and the visual mining platform can be extended with new data sources and functionalities to accommodate more complex scenarios (such as real-time data capture and analysis).

Evaluating the performance of temporal and spatial early warning statistics of algal blooms.

Regime shifts have large consequences for ecosystems and the services they provide. However, understanding the potential for, causes of, proximity to, and thresholds for regime shifts in nearly all settings is difficult. Generic statistical indicators of resilience have been proposed and studied in a wide range of ecosystems as a method to detect when regime shifts are becoming more likely without direct knowledge of underlying system dynamics or thresholds. These early warning statistics (EWS) have been studied separately but there have been few examples that directly compare temporal and spatial EWS in ecosystem-scale empirical data. To test these methods, we collected high-frequency time series and high-resolution spatial data during a whole-lake fertilization experiment while also monitoring an adjacent reference lake. We calculated two common EWS, standard deviation and autocorrelation, in both time series and spatial data to evaluate their performance prior to the resulting algal bloom. We also applied the quickest detection method to generate binary alarms of resilience change from temporal EWS. One temporal EWS, rolling window standard deviation, provided advanced warning in most variables prior to the bloom, showing trends and between-lake patterns consistent with theory. In contrast, temporal autocorrelation and both measures of spatial EWS (spatial SD, Moran's I) provided little or no warning. By compiling time series data from this and past experiments with and without nutrient additions, we were able to evaluate temporal EWS performance for both constant and changing resilience conditions. True positive alarm rates were 2.5-8.3 times higher for rolling window standard deviation when a lake was being pushed towards a bloom than the rate of false positives when it was not. For rolling window autocorrelation, alarm rates were much lower and no variable had a higher true positive than false positive alarm rate. Our findings suggest temporal EWS provide advanced warning of algal blooms and that this approach could help managers prepare for and/or minimize negative bloom impacts.

Spatial effects and impact factors of food nitrogen footprint in China based on spatial durbin panel model.

Reactive nitrogen (Nr) has far-reaching advantages and disadvantages on human beings. Nitrogen footprint (NF) is a tool to quantify the use of Nr in the environment. Food nitrogen footprint (FNF) accounts for the largest proportion of the total NF, and the differences between provinces in China exist objectively. In order to explore the spatial correlation and socio-economic driving factors of China's FNF, this paper uses N-calculator tool to calculate the FNF of 30 provinces in China from 2000 to 2018, and uses exploratory spatial data to analyze the spatial correlation and changes of provincial FNF, The driving factors and spatial effects of FNF change in the province were analyzed by using spatial Durbin panel model and spatial regression partial differential method. The results showed that: (1) There is a significant and stable positive spatial dependence and heterogeneity in the FNF among provinces; (2) The direct effect factors of promoting the growth of FNF in the province are urban household Engel coefficient, per capita disposable income of rural residents and rural household Engel coefficient. The main factors of restraining the growth of FNF in the province are wastewater discharge per unit GDP and per capita GDP; (3) the spillover effect is mainly manifested as the negative effect of the increase of urban household Engel coefficient on neighboring provinces, and the spillover effect of per capita disposable income of urban residents and nitrogen fertilizer application rate per unit grain yield on the growth of FNF of neighboring provinces is significant. From the policy level, it is necessary to guide healthy and scientific eating habits, reduce the proportion of meat and fish in the diet structure, reduce the nitrogen fertilizer application per unit grain yield, and improve the efficiency of chemical fertilizer utilization. When formulating relevant policies, government departments should give consideration to the cooperation between provincial and regional governments.

Minimization of measuring points for the electric field exposure map generation in indoor environments by means of Kriging interpolation and selective sampling.

In a world with increasing systems accessing to radio spectrum, the concern for exposure to electromagnetic fields is growing and therefore it is necessary to check limits in those areas where electromagnetic sources are working. Therefore, radio and exposure maps are continuously being generated, mainly in outdoor areas, by using many interpolation techniques. In this work, Surfer software and Kriging interpolation have been used for the first time to generate an indoor exposure map. A regular measuring mesh has been generated. Elimination of Less Significant Points (ELSP) and Geometrical Elimination of Neighbors (GEN) strategies to reduce the measuring points have been presented and evaluated. Both strategies have been compared to the map generated with all the measurements by calculating the root mean square and mean absolute errors. Results indicate that ELSP method can reduce up to 70% of the mesh measuring points while producing similar exposure maps to the one generated with all the measuring points. GEN, however, produces distorted maps and much higher error indicators even for 50% of eliminated measuring points. As a conclusion, a procedure for reducing the measuring points to generate radio and exposure maps is proposed based on the ELSP method and the Kriging interpolation.