Geo-spatial Assessment of Flood Vulnerability Areas of the Gaza Strip Towards Preparedness and Humanitarian Response Planning

Nowadays, flood and drought will become more common as climate change causes. Due to climate change consequences, flood occurrence and its impact on Gaza people have been of great concern to the Palestinian water authority, as it has a negative influence on various humanitarian and social issues. The hazards and damages resulted by flooding cause loss of life, property, displacement of people and disruption of socioeconomic activities. This research focuses on assessing Gaza Strip vulnerability to flooding using analysis of GIS-based spatial information. Not only did it consider the physical-environmental flood vulnerability, it also investigated social flood vulnerability aspects e.g., population densities. Soil and slope were considered to have the highest weight in the vulnerability mapping, as they represent the main factors in urban hydro-ecosystem structure. The long term average rainfall, a climate function factor, has the lowest weight, because it could be considered as a threat factor in addition to a vulnerability factor. This research demonstrates that urban area and population density as strong factors influencing flood vulnerability for humanitarian and saving life purposes. The findings of Geospatial analysis were used to map vulnerable areas likely to be affected in the event of flood hazard and suggest future interventions and related adaptation strategies in Gaza areas for flood mitigation.

Comparison of Logistics Time of R3A and R3B for Transportation of Lychee Product

This research presents the logistics management information system (LMIS) for the supply chain of lychee products of Phayao Province, Thailand. The main aim of this research is to develop a management application for Phayao’s agricultures to improve their competitive abilities on Chinese markets by utilizing a prediction method for traffic congestion based on both real-time and anticipated road traffic. The loss of productivity caused by traffic congestion has become a huge and increasingly heavy burden on Phayao farmers. Therefore, the prediction of urban road network traffic flow and the rapid and accurate evaluation of traffic congestion is of great significance to solve this problem. By using traffic data obtained by distance, road conditions, transportation safety, traffic density, and customs clearance, the local farmers in Phayao can deliver lychee products on time and reduce the loss of high emissions and environmental pollution caused by traffic congestion effectively.

A GIS-Based Multicriteria Analysis of Land Suitability for Groundnut Crop in Nghe An Province, Vietnam

This study focuses on identifying the potential lands for growing groundnut in Dien Chau district of Nghe An province (Vietnam), where groundnut is one of the major crops and brings high income for farmers. Based on the ecological requirements of groundnut, six criteria, including Soil Type, Soil Texture, Soil Depth, Slope, Average Temperature, and Average Total Rainfall in the planting season, were used. The Analytic Hierarchy Process method, commonly used in agricultural land use planning, was utilized to determine each criterion's weights via experts’ opinions. A pairwise comparison matrix was established to support this assessment process. The results revealed that Soil Texture showed the highest weight (0.31727) for groundnut farming, which was followed by Average Temperature (0.21131), Soil Type (0.17426), and Soil Depth (0.13982). Slope and Average Total Rainfall were the lowest weight factors, with 0.08122 and 0.07612, respectively. The weighted sum overlay analysis was implemented by ArcGIS software to generate the spatial distribution of land suitability of groundnut. The land suitability map indicated that 6830.07 ha (22.26%) of the studied area was highly suitable (S1), 10413.85 ha (33.95%) was moderately suitable (S2), 4336.76 ha (14.14%) was marginally suitable (S3), and 424.99 ha (1.39%) was not suitable (N). The total area of constrained area, including Waterbody and Built-up Land, was 8671.39 ha, accounting for 28.27% of the total area. Finally, the proposed land for groundnut cultivation was 12928.69 ha. The outcomes of this study may be regarded as a good reference for local government in agricultural land use planning.

Automatic Lineaments Extraction using the Line Algorithm in the Denguélé District (North West of Ivory Coast)

The aim of this work was to apply the LINE Algorithm (Segment Extraction Algorithm) on Landsat 8 images for automatic lineament extraction in the Denguélé district. The Landsat 8 images had previously been subjected to the technique of Principal Component Analysis (PCA). After that, we implemented the LINE algorithm. Indeed, the LINE algorithm uses the following six (6) parameters : RADI (Radius of the filter) for improving the quality of the input image, GTHR (Threshold of the contour gradient), LTHR (Threshold of the contour length), FTHR (Threshold of mounting error), ATHR (Angular difference threshold between two contours ) and DTHR (Distance chaining threshold to link two contours ) for lineament discrimination. Analysis of the principal components PCA 1, PCA2 and PCA3 of bands 1, 2, 3, 4, 5 and 7 of the Landsat 8 images shows that they contain respectively 79.57; 15.88 and 2.15%, this represents overall 97.6% of all channels. 3468 lineaments were extracted. The minimum and maximum lengths of the lineaments extracted are respectively 4201.08 m and 16167.59 m and their cumulative length is 18 919 517.9 m. The lineaments average lengths are 5.55 km; 5.75 km; 5.6 km and 5.40 km respectively for NE-SW, NS, E-W and NW-SE directions. The analysis of the directions of the lineaments using a rose diagram with 10 ° of frequency, shows that the dominant directions are NE-SW (31.83% of the total lineaments), EW (28.71% of the total lineaments) and NS (27.91% of the total lineaments).

Analysis of Spatial Accessibility and Capacity of Multi-Level Healthcare Facilities in Greater Irbid Municipality, Jordan

Measuring the spatial accessibility and capacity of healthcare facilities is an important task to improve the quality of health services and reduce the pressure on them. This research assesses the current spatial accessibility and capacity of two-level of healthcare facilities (comprehensive healthcare centers and hospitals) in the Greater Irbid Municipality using the enhanced two-step floating catchment area (E2SFCA) method. To do this, Network analysis techniques including original-destination matrix (OD), service area, and location-allocation were employed for determining the travel time from residents' points towards every healthcare facility, the service coverage and capacity within travel time zones, and the number of served areas by every healthcare facility. Then, optimum locations for new healthcare facilities that improve the accessibility and capacity rates were determined. The results show that while all areas in the study area are located within a 30-minute drive from the hospital's locations, 18 out of 23 areas are within 15 minutes drive towards the comprehensive health centers. This means that 28.80% of the population needs more than 15 minutes of driving time to access the second level of healthcare services. In addition, the annual average of the actual patient-doctor ratio ranges from 1338 to 2900 patients per doctor in the hospitals, and 2676 to 8524 patients per doctor in the comprehensive healthcare centers, and thus, the health services are inadequate in the study area. Furthermore, the suggested new healthcare facilities in terms of the numbers and optimum location would improve the spatial accessibility and the capacity ratio.

Towards a Reliable and Sensitive Deep Learning Based Approach for Multi-Ship Detection in SAR Imagery

Synthetic Aperture Radar (SAR) images show promising results in monitoring maritime activities. Recently, Deep learning-based object detection techniques have impressive results in most detection applications but unfortunately there are challenging problems such as difficulty of detecting multiple ships, especially inshore ones. In this paper, a three-step ship detection process is described and a reliable and sensitive hybrid deep learning model is proposed as an efficient classifier in the middle step. The proposed model combines the finetuned Inception-Resnet-V2 model and the Long Short Term Memory model in two different approaches: parallel approach and cascaded approach. In experiments, the region proposal algorithm and the Non-Maxima suppression algorithm are applied in the first and last step in the three-step detection process. The comparative results show that the proposed approach in cascaded form outperforms the competitive recent state-of-the-art approaches by enhancement up to 16.3%, 16.5%, and 18.9% in terms of recall, precision and mean average precision, respectively. Moreover, the proposed approach shows high relative sensitivity for challenged cases of both inshore and offshore scenes by enhancement ratios up to 81.88% and 24.58%, respectively in recall perspective.

The Comparison of Spatial Models in Peak Ground Acceleration (PGA) Study

This study was conducted to compare the performance of three different spatial analysis models: Inverse Distance Weighted (IDW), Ordinary Kriging,­­ and Regularized Spline interpolation technique to determine the best fit model representing Peak Ground Acceleration (PGA) in West Java Province, Indonesia. The three models are commonly used in spatial visualization, but have different calculation methods. The calculations were performed using available formulas while the spatial modeling was conducted using the algorithms in GIS software. Meanwhile, the accuracy of the spatial model and factual calculation was determined through the Root Mean Square Error (RMSE). The results showed differences for both spatial distribution and maximum and minimum values for each model. However, IDW was observed to be the model which approaches the factual value of the PGA calculation as indicated by its RMSE value of 0.772352 in comparison with the 7.169879 (Ordinary Kriging) and 1.140802 (Regularized Spline).

Forecasting Mobility Trends in Southeast Asia during the Coronavirus (Covid-19) Pandemic by Machine Learning Approaches

The novel coronavirus (COVID-19) was declared as the 2019-20 coronavirus pandemic by the World Health Organization (WHO) in March 2020. The COVID-19 virus has rapidly spread nationwide and internationally and caused 188 countries to report more than ten million cases of individuals contracting COVID-19. Typically, the virus is conveyed from person to person via respiratory droplets produced by coughing and sneezing. The time period between exposure and onset of symptoms is typically between two and fourteen days, and on average five days. The COVID-19 pandemic has affected many businesses relating to transportation including tourism, import-export commerce, the aviation business, and so forth. Governmental intervention in each country has had an impact on mobility trends depending on the degree of restriction such as social distancing, sharing mobility, and public transport. A COVID-19 surveillance system is one of the principal methods used for detecting COVID-19 epidemics, using short-period monitoring. However, while these networks present information on the activities of COVID-19, acquiring completed surveillance data from every medical station is profusely difficult due to many factors. This research aims to propose a performance model of machine learning approaches for COVID-19 pandemic forecasting of mobility trends in each country in Southeast Asia. Spatial data and non-spatial data are used to build the machine learning models. The experiments conducted showed that the model gave a forecasting accuracy in walking and driving mobility of 94.40% and 92.00%, respectively. The proposed forecasting model was developed to be of benefits to health authorities in the planning and administration of a suitable strategy to make decisions concerning transportation planning in each country.

Address Geocoding Services in Geospatial-based Epidemiological Analysis: A Comparative Reliability for Domestic Disease Mapping

Geographical information system (GIS) has been used for geospatial epidemiology. Through the process, it begins with geocoding, i.e. assigning geographic coordinates to an address on a map. This process is a bridge between spatial information and its attribute data. Fortunately, some open geocoding services are available. The paper aims to examine the mapping reliability of some online geocoding services to map the spread of tuberculosis (TB) in Sarawak, Malaysia towards practical implementation in the domestic health department. The features examined the common platforms, namely QGIS, Google Map, and ArcGIS Online, were selected and explored in terms of the following variables; positional quality, speed, cost, and coverage. Based on our exploratory experiment, ArcGIS Online offers relevant mapping features for the local geocoding services of the TB locations compared to the other two platforms. But the chosen geocoding methods or services may depend on the nature of the project, cost restrictions, and the experience of an analyst. Comparison of the positional accuracy with manual reference methods (e.g GPS measurement and manual digitizing) could be further studied.

Spatial Interaction between Lifestyles and Tuberculosis: An Expert and Public Participatory GIS in Malaysia

Human lifestyle factors are potential contributing factors to tuberculosis (TB) transmission in Malaysia, but the previous studies have not comprehensively explored these factors by using geospatial and public perspectives. The aim of the study was to examine the impacts of lifestyle risk factors on TB cases in Shah Alam using an expert and public participatory GIS (PPGIS) approach with the 5-risk scale from 1 to 5. Local health experts have suggested that the overall risk scale of TB-lifestyle factors are 2.33 (medium risk) as revealed in the public perception and GIS map (2.08). Key findings have shown that the factors of obesity/weights, diet routine, living conditions, physical exercise and socioeconomic status become a potential threat to the local community to get TB infection, especially if all these factors combine together.