Geospatial Data Analysis Research Articles

An interconnected IoT-inspired network architecture for data visualization in remote sensing domain

Geospatial Data Analytics (GDA) is a futuristic platform for analyzing and processing volumetric data in remote sensing and GIS applications. GDA utilizes the Internet of Spatial Things (IoST), mist, fog, and cloud computing architecture as a backend tool for analyzing and processing big geospatial data. This paper introduces utilizing these interconnected network architectures of cloud, fog, and mist to process a large volume of geospatial data. Also, the paper presents a flexible, interconnected distributed network system, i.e., IoST-mist-fog-cloud GIS architecture, to analyze and manage geospatial data. The proposed system helps cloud platforms when MIST devices are trying to cut down on latency and boost throughput at the edge of the IoST tier. It also performs the geospatial crime data visualization of the total number of stolen vehicles from 2001 to 2011 from all the states of India as a case study by using the proposed model. It explains the mathematical and analytical queueing model of the proposed system. In addition, it performs a performance evaluation and experimental findings on the proposed architecture and uses graphs to represent the various arithmetic outcomes. The experimentation result proves the proposed interconnected network architecture's efficacy in terms of reliability and efficiency.

Use of historical isoscapes to develop an estuarine nutrient baseline.

Coastal eutrophication is a prevalent threat to the healthy functioning of ecosystems globally. While degraded water quality can be detected by monitoring oxygen, nutrient concentrations, and algal abundance, establishing regulatory guidelines is complicated by a lack of baseline data (e.g., pre-Anthropocene). We use historical carbon and nitrogen isoscapes over ~300 years from sediment cores to reconstruct spatial and temporal changes in nutrient dynamics for a central California estuary, Elkhorn Slough, where development and agriculture dramatically enhanced nutrient inputs over the past century. We found strong contrasts between current sediment stable isotopes and those from the recent past, demonstrating shifts exceeding those in previously studied eutrophic estuaries and substantial increases in nutrient inputs. Comparisons of contemporary with historical isoscapes also revealed that nitrogen sources shifted from a historical marine-terrestrial gradient with higher δ15N near the inlet to amplified denitrification at the head and mouth of the modern estuary driven by increased N inputs. Geospatial analysis of historical data suggests that an increase in fertilizer application - rather than population growth or increases in the extent of cultivated land - is chiefly responsible for increasing nutrient loads during the 20th century. This study demonstrates the ability of isotopic and stoichiometric maps to provide important perspectives on long-term shifts and spatial patterns of nutrients that can be used to improve management of nutrient pollution.

Association between immigrant concentration and mental health service utilization in the United States over time: A geospatial big data analysis.

Immigrants (foreign-born United States [US] citizens) generally have lower utilization of mental health services compared with US-born counterparts, but extant studies have not investigated the disparities in mental health service utilization within immigrant population nationwide over time. Leveraging mobile phone-based visitation data, we estimated the average mental health utilization in contiguous US census tracts in 2019, 2020, and 2021 by employing two novel outcomes: mental health service visits and visit-to-need ratio (i.e., visits per depression diagnosis). We then investigated the tract-level association between immigration concentration and mental health service utilization outcomes using mixed-effects linear regression models that accounted for spatial lag effects, time effects, and covariates. This study reveals spatial and temporal disparities in mental health service visits and visit-to-need ratio among different levels of immigrant concentration across the US, both before and during the pandemic. Tracts with higher concentrations of Latin American immigrants showed significantly lower mental health service utilization visits and visit-to-need ratio, particularly in the US West. Tracts with Asian and European immigrant concentrations experienced a more significant decline in mental health service utilization visits and visit-to-need ratio from 2019 to 2020 than those with Latin American concentrations. Meanwhile, in 2021, tracts with Latin American concentrations had the least recovery in mental health service utilization visits. The study highlights the potential of geospatial big data for mental health research and informs public health interventions.

Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review

In India, agriculture serves as the backbone of the economy, and is a primary source of employment. Despite the setbacks caused by the COVID-19 pandemic, the agriculture and allied sectors in India exhibited resilience, registered a growth of 3.4% during 2020–2121, even as the overall economic growth declined by 7.2% during the same period. The improvement of the agriculture sector holds paramount importance in sustaining the increasing population and safeguarding food security. Consequently, researchers worldwide have been concentrating on digitally transforming agriculture by leveraging advanced technologies to establish smart, sustainable, and lucrative farming systems. The advancement in remote sensing (RS) and machine learning (ML) has proven beneficial for farmers and policymakers in minimizing crop losses and optimizing resource utilization through valuable crop insights. In this paper, we present a comprehensive review of studies dedicated to the application of RS and ML in addressing agriculture-related challenges in India. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and evaluated research articles published from 2015 to 2022. The objective of this study is to shed light on the application of both RS and ML technique across key agricultural domains, encompassing “crop management”, “soil management”, and “water management, ultimately leading to their improvement. This study primarily focuses on assessing the current status of using intelligent geospatial data analytics in Indian agriculture. Majority of the studies were carried out in the crop management category, where the deployment of various RS sensors led yielded substantial improvements in agricultural monitoring. The integration of remote sensing technology and machine learning techniques can enable an intelligent approach to agricultural monitoring, thereby providing valuable recommendations and insights for effective agricultural management.

GIS-Based Scientific Workflows for Automated Spatially Driven Sea Level Rise Modeling

Sea level rise (SLR) poses a significant threat to shorelines and the environment in terms of flooding densely populated areas and associated coastal ecosystems. Scenario analysis is often used to investigate potential SLR consequences, which can help stakeholders make informed decisions on climate change mitigation policies or guidelines. However, SLR scenario analysis requires considerable geospatial data analytics and repetitive execution of SLR models for alternative scenarios. Having to run SLR models many times for scenario analysis studies leads to heavy computational needs as well as a large investment of time and effort. This study explores the benefits of incorporating cyberinfrastructure technologies, represented by scientific workflows and high-performance computing, into spatially explicit SLR modeling. We propose a scientific workflow-driven approach to modeling the potential loss of marshland in response to different SLR scenarios. Our study area is the central South Carolina coastal region, USA. The scientific workflow approach allows for automating the geospatial data processing for SLR modeling, while repetitive modeling and data analytics are accelerated by leveraging high-performance and parallel computing. With support from automation and acceleration, this scientific workflow-driven approach allows us to conduct computationally intensive scenario analysis experiments to evaluate the impact of SLR on alternative land cover types including marshes and tidal flats as well as their spatial characteristics.

Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework.

To propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs). A combination of geospatial data analysis and artificial intelligence methods for evaluating vulnerability factors at the local level and allocate vaccines according to a dynamic mechanism for updating vulnerability and vaccine uptake. A novel approach is introduced including (I) Vulnerability data collection (including country-specific data on demographic, socioeconomic, epidemiological, healthcare, and environmental factors), (II) Vaccination prioritization through estimation of a unique Vulnerability Index composed of a range of factors selected and weighed through an Artificial Intelligence (AI-enabled) expert elicitation survey and scientific literature screening, and (III) Values consideration by identification of the most effective GIS-assisted allocation of vaccines at the local level, considering context-specific constraints and objectives. We showcase the performance of the 3Vs strategy by comparing it to the actual vaccination rollout in Kenya. We show that under the current strategy, socially vulnerable individuals comprise only 45% of all vaccinated people in Kenya while if the 3Vs strategy was implemented, this group would be the first to receive vaccines.

Machines as mapmakers and map users: key questions to ponder upon?

Abstract. This article explores the convergence of cartography and robotic mapping, addressing key challenges and opportunities that arise as machines increasingly serve as both mapmakers and map users. The author investigates three critical questions: (1) how to best represent geographical data and maps for machines, (2) what dynamic information about our environment should be made readily available to machines, and (3) which ethical, religious, and cultural norms should be considered for autonomous entities. By addressing these questions, the author aims to facilitate the development of geospatial data representation, management, and analysis for autonomous systems, while ensuring harmonious coexistence with humans. In the scope of this paper, author tries to propose an approach to bridge the gap between traditional cartography and the emerging needs of machines as user and makers by building common ground through cross-disciplinary collaboration, joint research groups, and the development of common standards and frameworks. In this proposal, particularly by using design thinking approach, an event-mapping principle, as an approach that represents spatial information as events, is highlighted as a promising common framework for integrating static and dynamic spatial information. Since, event-based mapping and models can improve the representation of geographical data for machines, enabling them to better understand the environment and make informed decisions in complex and dynamic contexts. This convergence will ultimately transform the way we think about maps and geographical information systems in the age of machines.

Flaring volumes in the intermountain west region: A geospatial analysis of satellite and operator-reported data with viable mitigation strategies

Burning associated gas has been a prevailing problem across the world for decades. This practice consumes billions of (US) dollars' worth of valuable natural gas, contributes billions of metric tons of carbon dioxide (CO2) to the atmosphere, and releases volatile chemicals to nearby communities. To assess the prevalence of wellbore flaring within the Intermountain West (I-West) region, we analyzed data from the Nightfire project and contrasted it with wellbore surface hole locations. Consequently, we will permit the analysis of the flare data on a geospatial scale and compare it with operator self-reported flaring volumes. Through this analysis, we found that New Mexico is by far the largest flaring state in the I-West region, with most of its flare gas coming from the Permian Basin. Additionally, we found that satellite data estimated volumes that were 165% larger than those self-reported by the operators. Although some of this could be an overestimation from the Nightfire project, the size of the discrepancy indicates that there may be an underestimation of flared volumes that operators report to the state. A better understanding of the discrepancy source can be identified by linking the satellite flare volume to individual wells and operators, and potential solutions may be implemented to assist New Mexico's recent waste laws in reducing Permian flared volumes. We also proposed economic solutions that could substantially reduce the flared volume through flare gas utilization through on-site processing, the construction of small spur lines, and the development of a local sink for methane.

Geospatial Analysis of Scour in Offshore Wind Farms

Climate change has highlighted the need to promote renewable energies. The offshore wind industry is growing exponentially because of some political strategies supported by various organizations, such as the European Union. The implementation of these strategies is commonly associated with significant investments, public acceptance, or achieving better installations and greater cumulative capacities. To ensure that offshore renewable energy projects could reach their ambitious targets, this study promotes the implementation of political strategies or planning decisions using data mining techniques and analytical tools. Strategic decisions based on real data analysis could help to achieve more suitable and optimal infrastructures. The scour phenomenon jeopardizes the operability of offshore wind farms, making it necessary to study its evolution over the years. In this work, extensive research on the scour phenomenon in offshore wind farms using real data (from the Lynn and Inner Dowsing offshore wind farms located in the UK) was performed, which revealed an evident lack of consideration of this phenomenon for data-driven decision-making processes. As a novelty, this research develops a detailed geospatial analysis of data, studying the possible autocorrelation of scour data measured from each turbine between 2011 and 2015. The conclusions obtained could be used to improve future planning tasks in offshore wind farms.

Racial residential segregation and COVID-19 vaccine uptake: an analysis of Georgia USA county-level data

BackgroundFoundational literature demonstrates that racial residential segregation results in poorer health outcomes for Black people than white people due to a variety of social determinants of health. COVID-19 vaccine uptake is important for better health outcomes, regardless of race. The COVID-19 pandemic has elevated concerns about racial health disparities but with little discussion of racial residential segregation as a predictor of disparate health outcomes. This paper investigates the relationship between racial residential segregation and COVID-19 vaccine uptake using county level data from the State of Georgia (USA).MethodsUsing publicly available data, regression analysis is conducted for 138 of the 159 counties in Georgia USA, using a dissimilarity index that describes county level differences in racial residential segregation. The primary independent variable is Black-white differences in vaccine uptake at the county level. The analytic methods focus on a spatial analysis to support information for county level health departments as the basis for health policy and resource allocation.ResultsConstructing a variable of the difference in vaccination rates between Black and white residents we find that Black-white differences in COVID-19 vaccination are most notable in the 69 most segregated of the 159 counties in Georgia. A ten-point lower segregation index is associated with an improvement in the Black-white vaccination gap of 1.5 percentage points (95% CI -0.31, -0.00). Income inequality and access to health care resources, such as access to a primary care physician, also predict Black-white differences in vaccination rates at the county level. Suggested mapping approaches of publicly available data at a state county level, provides a resource for local policy makers to address future challenges for epidemic and pandemic situations.ConclusionCounty level and geospatial data analysis can inform policy makers addressing the impact of racial residential segregation on local health outcomes, even for pandemic and epidemic issues.

The Use of Augmented Reality Technologies and Georeferencing Data for the Construction of Water Supply Systems

The use of mobile devices, especially smartphones and mobile technology has increased significantly. Accordingly, various applications have been developed to meet the needs of mobile users, which include navigation. The integration of these applications with maps and various localization technologies such as augmented reality (AR) will improve the reliability of navigation systems for wayfinding under certain constraints. AR allows the user to experience an interactive environment by observing the real world augmented by digital objects. However, this technology cannot store and control data. A geospatial information system (GIS) provides storage, visualization, and analysis of geospatial data that can be used to address the challenges and shortcomings of AR. In addition, the capabilities offered by GIS can be useful for achieving sustainability goals in areas such as effective planning and resource allocation. Traditional GIS, especially two-dimensional maps, provide a weak intuitive experience for users, leading to difficulties in interpreting the map and reaching conclusions. Therefore, AR can be used as a bridge between 2D and 3D visualization. The involvement of GIS in human activities and the transition of AR use from traditional terrain outreach to industrial applications makes it worthwhile to integrate GIS with AR to benefit from the two technologies simultaneously. The simultaneous visualization of real and virtual geospatial information leads to an interactive approach. The paper analyzes the capabilities of augmented reality and geo-referenced data approaches for the construction of water systems, describes their weaknesses and strengths, notes the main characteristics of the logistic regression algorithm and considers the tasks performed by this algorithm. A comparative analysis of the algorithms is carried out.

Topic-Clustering Model with Temporal Distribution for Public Opinion Topic Analysis of Geospatial Social Media Data

Analysis of the spatiotemporal distribution of online public opinion topics can help understand the hotspots of public concern. The topic model is employed widely in public opinion topic clustering for social media data. In order to handle topic-clustering of low-quality geospatial social media data, such as microblog data, with short text and timeliness characteristics, this study proposed a Dirichlet multinomial mixture over time (DMMOT) model to cluster microblog topic for public opinion analysis. The DMMOT model assumes that a single document belongs to a single topic, in line with the characteristics of a short text, and it introduces the probability distribution of “topic-time” in the process of topic generation. The model parameter inference process was presented in detail by exploring the Gibbs sampling method. Results generated using the DMMOT model in case study show that the “topic-word” distribution is semantically aggregated within various topics, and “topic-time” distribution clustered within a time window under each topic. Furthermore, the characteristics of the trend of each topic over time are basically consistent with the corresponding trend of topic in reality in terms of content. These indicate that the DMMOT model improves topic clustering for short text to some extent. Furthermore, the DMMOT model performed well in both temporal and spatial analysis of public opinion topics based on microblog data.

RESEARCH ON THE DISTRIBUTION OF AIRBNB ACCOMMODATION IN MAJOR EUROPEAN DESTINATIONS

The Airbnb platform, one of the most popular platforms of its kind, is present worldwide, with a variety of accommodations, depending on the type of accommodation it offers (single room, whole apartment, etc.). In this article, we set out to analyse the spatial allocation of accommodation and the density of Airbnb listings in comparison with hotel establishments in Europe. For the spatial analysis of Airbnb accommodations we used data from the Inside Airbnb website and QGIS, an application used for geographic information systems (GIS) that supports visualization, editing, and analysis of geospatial data. In this way, we created a geospatial database that helped us better understand the dynamics of each location. After analysing the data we concluded that a consequence of the concentration of Airbnb accommodation is that life in these areas has become more expensive and more crowded for locals. Airbnb accommodation has caused inconvenience not only to hotel owners but also to locals. In addition, Airbnb is having a strong impact on the rental market by increasing rental prices. Another trend that can be seen from the distribution of Airbnb accommodation is that in cities with a seafront location, accommodation is located close to beaches and most accommodation is of the entire home/apartment type. Differences in the frequency of use and prices of listings highlight the geographical inequality in the benefits and impact of Airbnb’s activity. The Airbnb offer is not a uniform segment of tourist accommodation and its effects on destinations are important and should therefore be analysed in relation to the territorial context.

ROLE OF 3D CITY MODEL DATA AS OPEN DIGITAL COMMONS: A CASE STUDY OF OPENNESS IN JAPAN'S DIGITAL TWIN "PROJECT PLATEAU"

Abstract. This study aims to clarify the development and utilization of highly accurate and open 3D city models (3DCMs), which began in Japan in 2020. The background of the project is explained based on a review of past efforts in Japan for making geospatial information accessible, the flow of data standardization around CityGML, and the introduction of Free and Open Source Software for Geo-spatial (FOSS4G) tools for data management and visualization. Two aspects of the analysis are reported: quantitative geospatial analysis of LOD1 building data and qualitative evaluation of 40 use cases using these data. The results indicate that approximately 18 million building data points covering 150 cities in Japan have been converted to open data in formats such as CityGML with high accuracy, complementing OpenStreetMap (OSM) data in urban areas. In addition, a total of 40 use cases for these data are demonstrated in new fields, such as urban planning, citizen participation, and even entertainment, with a few of these tools becoming open-source software. Through this project, data related to 3DCMs, which have not necessarily been produced in a unified format or specification in Japan, can now be easily handled as CityGML through a one-stop viewer, and the data are expected to be enhanced, and new geo-services using 3DCMs will be provided under the concept of openness, regardless of the city scale.

GIS-based modeling of landslide susceptibility zonation by integrating the frequency ratio and objective–subjective weighting approach: a case study in a tropical monsoon climate region

Accurate detection of landslide spatial patterns is vital in susceptibility, hazard, and risk disaster mapping. Geographic Information System (GIS)-based quantitative approaches provide a rigorous procedure for gaining deep insight into natural and anthropogenic landslides from different scales. This study aims to implement a comprehensive solution for retrieving the landslide susceptibility index. For that purpose, a landslide inventory was performed in a tropical monsoon climate region, with a magnitude of elevation spanning from −65 m to 1,900 m above the sea, considering 15 fundamental causative factors belonging to the groups of topography, hydrology, geology, land cover conditions and anthropogenic activities, and weather. The frequency ratio (FR) was implemented to rank subclasses in each causative factor. For factor weight estimation, different approaches were applied, including the subjective-based analytic hierarchy process (AHP), objective-based Shannon entropy (SE), and a synergy of both methods (AHP–SE), built on these two approaches. Out of the 271 identified landslide locations, 70% (196 points) were used for training and the remaining 30% (71 points) were applied for validation. The results showed that the integrated AHP–SE outperformed the two individual approaches, with the area under the receiver operating characteristic curve (AUC) reaching 0.876, following SE (AUC = 0.848) and AHP (AUC = 0.818). In the synergy approach, the climate pattern under tropical monsoons was confirmed as the most crucial landslide-predisposing factor. The research contributes to a novel discussion by integrating knowledge-based consultation and statistical data analysis of accurate geospatial data, incorporating significant explanatory factors toward a reliable landslide-prone zonation over space and time dimensions.

A New Approach to Estimate Fuel Budget and Wildfire Hazard Assessment in Commercial Plantations Using Drone-Based Photogrammetry and Image Analysis

Increased demand for sustainable timber products has resulted in large investments in agroforestry in Australia, with plantations growing various Pinus species, selected to suit a plantation’s environment. Juvenile Pinus species have a low fire tolerance. With Australia’s history of wildfires and the likelihood of climate change exacerbating that risk, the potential for a total loss of invested capital is high unless cost-effective targeted risk minimisation is part of forest management plans. Based on the belief that the understory profiles within the juvenile plantations are a major factor determining fuel hazard risks, an accurate assessment of these profiles is required to effectively mitigate those risks. At present, assessment protocols are largely reliant on ground-based observations, which are labour-intensive, time consuming, and expensive. This research project investigates the effectiveness of using geospatial analysis of drone-derived photographic data collected in the commercial pine plantations of south-eastern Queensland as a cost-saving alternative to current fuel hazard risk assessment practices. Understory composition was determined using the supervised classification of orthomosaic images together with derivations of canopy height models (CHMs). The CHMs were subjected to marker-controlled watershed segmentation (MCWS) analysis, isolating and removing the plantation pine trees, enabling the quantification of understory fuel profiles. The method used proved highly applicable to immature forest environments with minimal canopy closure, but became less reliable for close canopied older plantations.

Hydrogeochemical Study and Geospatial Analysis of Water Quality Using GIS based Water Index and Multivariate Statistics in Kombolcha City, Ethiopia

The study was carried out to evaluate hydro geochemistry and the risk of groundwater and surface water pollution in the Kombolcha area. To achieve this, hydrogeochemical analysis, water heavy metal, geospatial data analysis, ion ratio, correlation matrix, principal component analysis, Heavy metal Pollution Index (HPI), and Water Quality Index (WQI) methodologies were employed. A total of 36 samples (both water and effluent samples) had been collected and assessed for major physicochemical variables and heavy metals and samples were taken from hand dug wells, Borehole, springs, and rivers in the area. Hydrogeochemical methods showed groundwater mineralization due to (1) silicate weathering, (2) cation exchange processes, and (3) anthropogenic sources (i.e., contaminated discharge of sulphate, carbonate, and trace metal effluents). The study result revealed that major ions dominating the area are Ca2+ >Na+ > Mg2+ >K+, HCO3- >SO4 2- > Cl- > NO3-, and Fe> Mn >Pb> Cr> Cd for cations, anions and trace metals respectively with all heavy netals had mean concentrations above the WHO recommended limits. Calculated Pollution indices revealed 50.7% of the sample belongs to a low level of pollution, while 35% and 14.3% belong to a medium and high level of pollution respectively which consequently translating the area into high groundwater pollution zones. The correlation matrix revealed that no significant correlation exists between the water quality variables (Cl and NO3- with Fe, Pb, Cr, Mn, and Cd). PCA was applied on the data set to identify the spatial sources of pollution in groundwater and in the first principal component analysis, Mn, Fe, Cr, Pb, and Cd (in descending order) were found in amounts greater than 0.5, confirming that these metals were from anthropogenic sources. The combined assessments based on WQI and HPI, the study showed that water samples in the proximity of industrial sites are polluted by factories effluent and uncontrolled waste disposal due to urbanization.

The Impact of Opioid Prescribing Limits on Drug Usage in South Carolina: A Novel Geospatial and Time Series Data Analysis.

The opioid crisis in the United States has had devastating effects on communities across the country, leading many states to pass legislation that limits the prescription of opioid medications in an effort to reduce the number of overdose deaths. This study investigates the impact of South Carolina's prescription limit law (S.C. Code Ann. 44-53-360), which aims to reduce opioid overdose deaths, on opioid prescription rates. The study utilizes South Carolina Reporting and Identification Prescription Tracking System (SCRIPTS) data and proposes a distance classification system to group records based on proximity and evaluates prescription volumes in each distance class. Prescription volumes were found to be highest in classes with pharmacies located further away from the patient. An Interrupted Time Series (ITS) model is utilized to assess the policy impact, with benzodiazepine prescriptions as a control group. The ITS models indicate an overall decrease in prescription volume, but with varying impacts across the different distance classes. While the policy effectively reduced opioid prescription volumes overall, an unintended consequence was observed as prescription volume increased in areas where prescribers were located at far distances from patients, highlighting the limitations of state-level policies on doctors. These findings contribute to the understanding of the effects of prescription limit laws on opioid prescription rates and the importance of considering location and distance in policy design and implementation.

Mapping rabies distribution in China: a geospatial analysis of national surveillance data

ObjectivesDog-mediated human rabies remains an important public health problem in China. In this study, we aimed to understand the spatiotemporal variation of rabies and examine its nonmedical ecological factors. MethodsIn this study, we used the annual incidence data for rabies at the province level in China to describe the incidence trends for the period 2004-2019 and used a Bayesian hierarchical spatiotemporal model to determine the impacts of environmental, economic, and demographic factors. ResultsFrom 2004 to 2019, there were 26,593 cases reported in 31 provinces in Mainland China, and the annual incidence increased from 0.02 per 100,000 in 2004 to 0.14 in 2007, substantially decreased in 2008, and was gradually declining thereafter. Guizhou, Guangxi, Hunan, and Hainan were four high-risk provinces, and Yunnan and Anhui provinces showed an increased risk in 2018 and 2019. Temperature and per capita gross domestic product were significantly positively correlated with the disease risk. The standardized morbidity ratio of rabies is likely to increase by 28% (relative risk: 1.28, 95% credible interval: 1.13-1.36) for every 1°C rise in temperature, and 17% (relative risk: 1.17, 95% credible interval: 1.01-1.34) for every 10,000 yuan increase in per capita gross domestic product. ConclusionIn most provinces in China, the risk of rabies has been reduced to a persistently low level. However, the progress of rabies control in six provinces have been less than satisfactory. The study highlights interventions, such as enhancing animal vaccination need to be implemented in these priority areas.

Time-based Geospatial Analysis of Night-Time Light Data and Citizen Movement Restriction During Covid-19 Period

Restrictions on community activities or in some areas also called lockdowns have been carried out by many countries in order to reduce the spread of Covid-19. Various methods are used to monitor the implementation of these restrictions, this study uses a new approach by using satellite imagery at night commonly called Night-Time Light (NTL) Data. This research using a sample of 381 coordinate points was taken in public places in Jakarta. The data was collected using the VIIRS Stray Light Corrected Nighttime Day/Night Band Composites Version 1 dataset from Q1 2019 to Q2 2022. The results of these satellite photos were then converted into numerical form and correlated with the timeline for the restriction of community activities in the Jakarta area. The result is there is a slightly decreasing in light intensity when entering a period of Covid-19 at the beginning of 2020 until 2022 with a percentage of around 1% - 16% in various sectors. This decreasing light intensity has a slight correlation with mobility data for several sectors. The mobility data show a huge difference at the beginning of Covid-19, which shows a decrease in the activity of up to 60%.