scholarly journals GIS-based approaches on the accessibility of referral hospital using network analysis and the spatial distribution model of the spreading case of COVID-19 in Jakarta, Indonesia

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Florence Elfriede Sinthauli Silalahi ◽  
Fahrul Hidayat ◽  
Ratna Sari Dewi ◽  
Nugroho Purwono ◽  
Nadya Oktaviani
Keyword(s):  
Spatial Distribution ◽  
Local Government ◽  
Service Area ◽  
Referral Hospital ◽  
Capital City ◽  
Distribution Model ◽  
Economic Losses ◽  
Cost Matrix ◽  
Referral Hospitals ◽  
Novel Coronavirus

Abstract Background The outbreak of the novel coronavirus (COVID-19) has rapidly spread, causing million confirmed cases, thousands of deaths, and economic losses. The number of cases of COVID-19 in Jakarta is the largest in Indonesia. Furthermore, Jakarta is the capital city of Indonesia which has the densest population in the country. There is need for geospatial analysis to evaluate the demand in contrast to the capacity of Referral Hospitals and to model the spreading case of Covid-19 in order to support and organize an effective health service. Methods We used the data from local government publicity for COVID-19 as trusted available sources. By using the verifiable data by observation from the local government, we estimated the spatial pattern of distribution of cases to estimate the growing cases. We performed service area and Origin-Destination (OD) Cost Matrix in support to existing referral hospital, and to create Standard Deviational Ellipse (SDE) model to determine the spatial distribution of COVID-19. Results We identified more than 12.4 million people (86.7%) based on distance-based service area, live in the well served area of the referral hospital. A total 2637 positive-infected cases were identified and highly concentrated in West Jakarta (1096 cases). The results of OD cost matrix in a range of 10 km show a total 908 unassigned cases from 24 patient’s centroid which was highly concentrated in West Jakarta. Conclusions Our results indicate the needs for additional referral hospitals specializing in the treatment of COVID-19 and spatial illustration map of the growth of COVID-19′ case in support to the implementation of social distancing in Jakarta.

scholarly journals Development of the first georeferenced map of Rhipicephalus (Boophilus) spp. in Mexico from 1970 to date and prediction of its spatial distribution

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
Yazmin Alcala-Canto ◽  
Juan Antonio Figueroa-Castillo ◽  
Froylán Ibarra-Velarde ◽  
Yolanda Vera-Montenegro ◽  
María Eugenia Cervantes-Valencia ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Environmental Variables ◽  
Control Strategies ◽  
Integrated Control ◽  
Area Under The Curve ◽  
Entropy Method ◽  
Distribution Model ◽  
Economic Losses ◽  
Precipitation Seasonality ◽  
Epidemiological Risk

The tick genus Ripicephalus (Boophilus), particularly R. microplus, is one of the most important ectoparasites that affects livestock health and considered an epidemiological risk because it causes significant economic losses due, mainly, to restrictions in the export of infested animals to several countries. Its spatial distribution has been tied to environmental factors, mainly warm temperatures and high relative humidity. In this work, we integrated a dataset consisting of 5843 records of Rhipicephalus spp., in Mexico covering close to 50 years to know which environmental variables mostly influence this ticks’ distribution. Occurrences were georeferenced using the software DIVA-GIS and the potential current distribution was modelled using the maximum entropy method (Maxent). The algorithm generated a map of high predictive capability (Area under the curve = 0.942), providing the various contribution and permutation importance of the tested variables. Precipitation seasonality, particularly in March, and isothermality were found to be the most significant climate variables in determining the probability of spatial distribution of Rhipicephalus spp. in Mexico (15.7%, 36.0% and 11.1%, respectively). Our findings demonstrate that Rhipicephalus has colonized Mexico widely, including areas characterized by different types of climate. We conclude that the Maxent distribution model using Rhipicephalus records and a set of environmental variables can predict the extent of the tick range in this country, information that should support the development of integrated control strategies.

scholarly journals Spatio-temporal distribution patterns of Plutella xylostella (Lepidoptera: Plutellidae) in a fine-scale agricultural landscape based on geostatistical analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jian-Yu Li ◽  
Yan-Ting Chen ◽  
Meng-Zhu Shi ◽  
Jian-Wei Li ◽  
Rui-Bin Xu ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Plutella Xylostella ◽  
Agricultural Landscape ◽  
Seasonal Pattern ◽  
Distribution Patterns ◽  
Geostatistical Analysis ◽  
Distribution Model ◽  
Detailed Knowledge ◽  
Fine Scale ◽  
Spatio Temporal

AbstractA detailed knowledge on the spatial distribution of pests is crucial for predicting population outbreaks or developing control strategies and sustainable management plans. The diamondback moth, Plutella xylostella, is one of the most destructive pests of cruciferous crops worldwide. Despite the abundant research on the species’s ecology, little is known about the spatio-temporal pattern of P. xylostella in an agricultural landscape. Therefore, in this study, the spatial distribution of P. xylostella was characterized to assess the effect of landscape elements in a fine-scale agricultural landscape by geostatistical analysis. The P. xylostella adults captured by pheromone-baited traps showed a seasonal pattern of population fluctuation from October 2015 to September 2017, with a marked peak in spring, suggesting that mild temperatures, 15–25 °C, are favorable for P. xylostella. Geostatistics (GS) correlograms fitted with spherical and Gaussian models showed an aggregated distribution in 21 of the 47 cases interpolation contour maps. This result highlighted that spatial distribution of P. xylostella was not limited to the Brassica vegetable field, but presence was the highest there. Nevertheless, population aggregations also showed a seasonal variation associated with the growing stage of host plants. GS model analysis showed higher abundances in cruciferous fields than in any other patches of the landscape, indicating a strong host plant dependency. We demonstrate that Brassica vegetables distribution and growth stage, have dominant impacts on the spatial distribution of P. xylostella in a fine-scale landscape. This work clarified the spatio-temporal dynamic and distribution patterns of P. xylostella in an agricultural landscape, and the distribution model developed by geostatistical analysis can provide a scientific basis for precise targeting and localized control of P. xylostella.

scholarly journals The development of regional differentiation of office construction in the Czech Republic: 1990–2010

2015 ◽  
Vol 23 (1) ◽  
pp. 21-33
Author(s):  
Pavel Domalewski ◽  
Jan Baxa
Keyword(s):  
Spatial Distribution ◽  
Czech Republic ◽  
Questionnaire Survey ◽  
Large Scale ◽  
Capital City ◽  
Office Building ◽  
Regional Differentiation ◽  
Key Factors ◽  
The Czech Republic ◽  
Dominant Position

Abstract The factors that were crucial for the construction of administrative buildings in the regional capitals of the Czech Republic are subject to examination in this article. One primary question is whether the development of office construction reflects the qualitative importance of the cities, or whether there are some other regularities in the spatial distribution of construction. To identify the key factors, controlled interviews with experts professionally involved in the construction of administrative buildings were carried out, and these data were then extended as part of a large-scale questionnaire survey with other experts on the issue. The results have confirmed the dominant position of the capital city of Prague in terms of its qualitative importance, as the remaining regional capitals have less than one-tenth of the volume of modern office building areas. The greatest differences in the construction of administrative buildings have been noted in Brno and Ostrava, despite the fact that they exhibit similar characteristics when considered in the light of respondent-determined factors.

Exploring the use of gpr and satellite-based snow data for snowmelt runoff predictions

2021 ◽  
Author(s):  
Ilaria Clemenzi ◽  
David Gustafsson ◽  
Jie Zhang ◽  
Björn Norell ◽  
Wolf Marchand ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Distribution Model ◽  
Accurate Estimation ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Distribution ◽  
The Impact

<p>Snow in the mountains is the result of the interplay between meteorological conditions, e.g., precipitation, wind and solar radiation, and landscape features, e.g., vegetation and topography. For this reason, it is highly variable in time and space. It represents an important water storage for several sectors of the society including tourism, ecology and hydropower. The estimation of the amount of snow stored in winter and available in the form of snowmelt runoff can be strategic for their sustainability. In the hydropower sector, for example, the occurrence of higher snow and snowmelt runoff volumes at the end of the spring and in the early summer compared to the estimated one can substantially impact reservoir regulation with energy and economical losses. An accurate estimation of the snow volumes and their spatial and temporal distribution is thus essential for spring flood runoff prediction. Despite the increasing effort in the development of new acquisition techniques, the availability of extensive and representative snow and density measurements for snow water equivalent estimations is still limited. Hydrological models in combination with data assimilation of ground or remote sensing observations is a way to overcome these limitations. However, the impact of using different types of snow observations on snowmelt runoff predictions is, little understood. In this study we investigated the potential of assimilating in situ and remote sensing snow observations to improve snow water equivalent estimates and snowmelt runoff predictions. We modelled the seasonal snow water equivalent distribution in the Lake Överuman catchment, Northern Sweden, which is used for hydropower production. Simulations were performed using the semi-distributed hydrological model HYPE for the snow seasons 2017-2020. For this purpose, a snowfall distribution model based on wind-shelter factors was included to represent snow spatial distribution within model units. The units consist of 2.5x2.5 km<sup>2</sup> grid cells, which were further divided into hydrological response units based on elevation, vegetation and aspect. The impact on the estimation of the total catchment mean snow water equivalent and snowmelt runoff volume were evaluated using for data assimilation, gpr-based snow water equivalent data acquired along survey lines in the catchment in the early spring of the four years, snow water equivalent data obtained by a machine learning algorithm and satellite-based fractional snow cover data. Results show that the wind-shelter based snow distribution model was able to represent a similar spatial distribution as the gpr survey lines, when assessed on the catchment level. Deviations in the model performance within and between specific gpr survey lines indicate issues with the spatial distribution of input precipitation, and/or need to include explicit representation of snow drift between model units. The explicit snow distribution model also improved runoff simulations, and the ability of the model to improve forecast through data assimilation.</p>

scholarly journals Characterizing the spatial variations and correlations of large rainstorms for landslide study

2017 ◽  
Vol 21 (9) ◽  
pp. 4573-4589 ◽  
Author(s):  
Liang Gao ◽  
Limin Zhang ◽  
Mengqian Lu
Keyword(s):  
Spatial Distribution ◽  
Hong Kong ◽  
Spatial Correlation ◽  
Hazard Analysis ◽  
Primary Objective ◽  
Landslide Hazard ◽  
Distribution Model ◽  
Hazard Evaluation ◽  
Topographic Effects ◽  
The Impact

Abstract. Rainfall is the primary trigger of landslides in Hong Kong; hence, rainstorm spatial distribution is an important piece of information in landslide hazard analysis. The primary objective of this paper is to quantify spatial correlation characteristics of three landslide-triggering large storms in Hong Kong. The spatial maximum rolling rainfall is represented by a rotated ellipsoid trend surface and a random field of residuals. The maximum rolling 4, 12, 24, and 36 h rainfall amounts of these storms are assessed via surface trend fitting, and the spatial correlation of the detrended residuals is determined through studying the scales of fluctuation along eight directions. The principal directions of the surface trend are between 19 and 43°, and the major and minor axis lengths are 83–386 and 55–79 km, respectively. The scales of fluctuation of the residuals are found between 5 and 30 km. The spatial distribution parameters for the three large rainstorms are found to be similar to those for four ordinary rainfall events. The proposed rainfall spatial distribution model and parameters help define the impact area, rainfall intensity and local topographic effects for landslide hazard evaluation in the future.

scholarly journals Estimation of the Extreme Distribution Model of Economic Losses Due to Outbreaks Using the POT Method with Newton Raphson Iteration

2021 ◽  
Vol 2 (1) ◽  
pp. 37-45
Author(s):  
Riza Adrian Ibrahim ◽  
Sukono Sukono ◽  
Riaman Riaman
Keyword(s):  
Estimation Method ◽  
Random Variable ◽  
Value Theory ◽  
Extreme Value ◽  
Distribution Model ◽  
Economic Losses ◽  
Extreme Distribution ◽  
Newton Raphson ◽  
The Government ◽  
Raphson Iteration

Extreme distribution is the distribution of a random variable that focuses on determining the probability of small values in the tail areaof the distribution. This distribution is widely used in various fields, one of which is reinsurance. An outbreak catastrophe is non-natural disaster that can pose an extreme risk of economic loss to a country that is exposed to it. To anticipate this risk, the government of a country can insure it to a reinsurance company which is then linkedto bonds in the capital market so that new securities are issued, namely outbreakcatastrophe bonds. In pricing, knowledge of the extreme distribution of economic losses due to outbreak catastrophe is indispensable. Therefore, this study aims to determine the extreme distribution model of economic losses due to outbreak catastrophe whose models will be determined by the approaches and methods of Extreme Value Theory and Peaks Over Threshold, respectively. The threshold value parameter of the model will be estimated by Kurtosis Method, while the other parameters will be estimated with Maximum Likelihood Estimation Method based on Newton-Raphson Iteration. The result of the research obtained is the resulting model of extreme value distribution of economic losses due to outbreak catastrophe that can be used by reinsurance companies as a tool in determining the value of risk in the outbreak catastrophe bonds.

scholarly journals Dokter Tunggu, A Hospital Application for Patient of Healthcare and Social Security Agency

2021 ◽  
Vol 8 (8) ◽  
pp. 450-458
Author(s):  
Muhammad Riyadi ◽  
Rhian Indradewa ◽  
Tantri Yanuar Rahmat Syah
Keyword(s):  
Social Security ◽  
Real Time ◽  
Health Facilities ◽  
Referral Hospital ◽  
Capital City ◽  
Business Planning ◽  
Level I ◽  
A Company ◽  
Social Security Agency

PT. Zaps Technology is a company engaged in technology and information by producing application products with the name Dokter Tunggu (Doku). The application was created to eliminate queues that often occur in Healthcare and Social Security Agency patient services at level I Hospitals and Health Facilities. Place of company at Bekasi Jawa Barat, The location is said to be chosen because Bekasi is one of the supporting areas for the capital city and has a variety of complete business facilities. This company's strategy is to create innovations in Healthcare and Social Security Agency patient services where the application made has various features that are able to eliminate queues. The application has an online referral menu on the application so that Hospitals, Level I Facilities and Healthcare and Social Security Agency patients are easier to take advantage of BPJS services. The waiting doctor application will display real time conditions at the referral hospital so that BPJS users can monitor the condition of BPJS services at the destination Hospital. Keywords: Dokter Tunggu, Hospital, Online Sevice, Business Planning.

scholarly journals Geospatial Distribution of Public Secondary in Gombe Local Government Area, Gombe State of Nigeria

2019 ◽  
Vol 1 (1) ◽  
pp. 30-39
Author(s):  
Yila Caiaphas Makadi ◽  
Abecca Stephen Sati ◽  
Ismail Dankaka
Keyword(s):  
Spatial Distribution ◽  
Local Government ◽  
Spatial Pattern ◽  
Nearest Neighbor ◽  
Secondary Data ◽  
Local Government Area ◽  
Primary Data ◽  
Public Secondary School ◽  
Data Survey ◽  
Senior Secondary

The paper reviews research tradition of accessibility level and spatial distribution of student in public secondary school in gombe local government area, Gombe state. Primary and secondary data were used for the study. Primary data was collected using questionnaire and a hand-held GPS receiver to capture the coordinate points of schools and other relevant data. Secondary data include administrative map, population figures of both students and Teachers, Names and addresses of the secondary schools in the study area. The data were analyzed using geographic information techniques. From the data survey carried out, the result of the analysis showed the accessibility level and spatial distribution of school in Gombe were seventeen (17) public senior secondary and total number of students were nineteen thousand and eleven (19,011). The nearest neighbor analysis (NNA) for the spatial pattern of school were carried out based on each ward in study area which as ten (10) wards in each ward revealed two major spatial distributions. The spatial pattern of the Gombe LGA has Nearest Neighbour Ratio (NNR): 3.385087, Bolari East ward with NNR: 3.385087 and Shamaki wards NNR: 1.600148, which showed dispersed pattern, while Jekada Fari ward with NNR: 0.214890, Pantami ward with NNR: 0.226863, and Herwo Gana wards with NNR: 0.185239, were showed clustered pattern. The nearest neighbor index shows clustered pattern for all the wards in the local government area except Bolari East and Shamaki wards that has dispersed pattern of distribution. The implication of these two patterns means that accessibility is poor in the study area. Students travel than normal to overcome the function of distance.

scholarly journals Journey to Crime Using Dijkstra’s Algorithm

2017 ◽  
Vol 1 (1) ◽  
pp. 1-14
Author(s):  
J. O. Olusina ◽  
J. B. Olaleye
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Shortest Path ◽  
Geographic Information ◽  
Service Area ◽  
Dijkstra’S Algorithm ◽  
Crime Mapping ◽  
Cost Matrix ◽  
Dijkstra's Algorithm ◽  
Journey To Crime

This paper describes some benefits of crime mapping in a Geographic Information Systems (G.I.S.) environment. The underlining principle of Journey to Crime was discussed. Crime Spots and Police Stations in the study area were mapped, Shortest-Path, Closest Facility, Service Area and OD (Origin – Destination) Cost Matrix were determined based on Dijkstra's Algorithm. Results show that the distribution of police stations does not correspond with the spread of crime spots.

Sign in / Sign up

Export Citation Format

Share Document