Evaluation of spatial interpolation methods for estimating rainfall in GIS "Applied study on Madinah region in Saudi Arabia" | تقييم طرق الاشتقاق المكاني لتقدير كمية الأمطار في نظم المعلومات الجغرافية "دراسة تطبيقية على منطقة المدينة المنورة في المملكة العربية السعودية"

2020 ◽  
pp. 83-98
Keyword(s):  
Saudi Arabia ◽  
Information Systems ◽  
Spatial Analysis ◽  
Root Mean Square ◽  
Spatial Interpolation ◽  
Geographical Information Systems ◽  
Geographical Information ◽  
Mean Square ◽  
Interpolation Methods ◽  
Spatial Interpolation Methods

The study of the spatial Interpolation of rain is an important aspect of climatic and hydrological studies, especially as it relates to projects related to the development of the country. Perhaps one of the most important problems related to rain studies within the climate studies is the inability of meteorological stations to cover large areas in Saudi Arabia enough, including the Medina. The ArcGIS program allows for mapping rain-equality by interpolation of areas not covered by rain. The interpolation process involves several different application methods that vary in accuracy depending on the variance of the input data and the arithmetic methods of each model. Significant for the actual Interpolation of rainfall. This study was based on the inductive approach to achieve the objectives of the study, which seeks to evaluate three methods of spatial Interpolation methods within ArcGIS: IDW, Kriging and Spline for rain studies through several interrelated applications. To estimate the general error of the derived values, the researcher used RMSEZ (Root-mean-square standardized errors RMSSE) and then calculated the validity of the Interpolation at 95 % confidence level. The study found that the method of IDW was the best of the three methods in the of spatial Interpolation of rainfall. The overall error value of the RMSE index was (13.2) mm while the mean value of the Interpolation was (25.9) mm, meaning that the Interpolation value would be correct for the actual data within the range of (25.9) mm and (25.9) mm. Geographical Information Systems - spatial Interpolation - Spatial Analysis - Root-mean-square standardized errors RMSSE. Keywords: Geographic Information Systems, Spatial Derivation, Spatial Analysis, Square Root of Average Square Prediction Errors -------------------------------------------------------------------- تعد دراسة التوزيع المكاني للأمطار أحد الجوانب المهمة في الدراسات المناخية والمائية, خاصة مع ارتباطها بالمشاريع التي ترتبط بالتنمية في البلاد. ولعل من أهم المشاكل التي تتعلق بالدراسات المطرية ضمن الدراسات المناخية عدم قدرة محطات الأرصاد الجوية على تغطية مساحات كبيرة في المملكة العربية السعودية بشكل كافٍ بما فيها منطقة المدينة المنورة. ويتيح برنامج ArcGIS رسم خرائط تساوي الأمطار من خلال عمل استيفاء - اشتقاق Interpolationللمناطق غير المغطاة بقيم مطرية، وتشتمل عملية الاشتقاق Interpolation على عدد من الطرق التطبيقية المختلفة التي تتباين من حيث دقتها, تبعا لتباين البيانات المدخلة وطرق التقدير الحسابية لكل نموذج؛ إذ قد تعطي هذه الاشتقاقات اختلافات كبيرة عن التوزيع الفعلي للأمطار. واعتمدت هذه الدراسة على المنهج الاستقرائي لتحقيق أهداف الدراسة التي تسعى إلى تقييم ثلاث طرق من طرق الاشتقاق المكاني داخل برنامج ArcGIS وهي: IDW وKriging وSpline وتحديد أكثرها صحة للدراسات المطرية من خلال عدد من التطبيقات المترابطة فيما بينها. ولتقدير الخطأ العام للقيم المشتقة استخدمت الباحثة مؤشر RMSEZ (الجذر التربيعي لمتوسط أخطاء التنبؤ المربعة) ومن ثم حساب صحة الاشتقاق عند مستوى ثقة 95%. وتوصلت الدراسة إلى أن طريقة IDW كانت أفضل الطرق الثلاث في عملية الاشتقاق المكاني لكمية الأمطار؛ إذ بلغت قيمة الخطأ العام لمؤشر RMSE (13.2) ملم, في حين بلغت قيمة صحة الاشتقاق (25.9) ملم؛ أي أن قيمة الاشتقاق ستكون صحيحة بالنسبة للبيانات الفعلية ضمن مدى يتراوح بين (+25.9ملم) و(-25.9ملم). الكلمات المفتاحية: نظم المعلومات الجغرافية – الاشتقاق المكاني – التحليلات المكانية – الجذر التربيعي لمتوسط أخطاء التنبؤ المربعة.

Intelligence from Space

2011 ◽  
pp. 194-226
Author(s):  
Paul Hendriks
Keyword(s):  
Decision Making ◽  
Information Systems ◽  
Spatial Analysis ◽  
Geographical Information Systems ◽  
Geographical Information ◽  
Specific Class ◽  
Step Procedure ◽  
Decision Making Processes ◽  
Projected Position ◽  
Spatial Element

The spatial element, which is omnipresent in data and information relevant to organizations, is much underused in the decision-making processes within organizations. This applies also to decision-making within the domain of Competitive Intelligence. The chapter explores how the CI function may benefit from developing a spatial perspective on its domain and how building, exploring and using this perspective may be supported by a specific class of information systems designed to handle the spatial element in data: Geographical Information Systems (GIS). The chapter argues that the key element for linking GIS to CI involves the identification of situations in which spatial analysis may support organizational decision-making within the CI domain. It presents a three-step procedure for identifying how CI may recognize spatial decision problems that are useful to boost the operation of the CI function. The first step concerns identifying relevant spatial variables, for instance by analyzing economic, demographic or political trends as to their spatial implications. The second step involves using GIS for positioning the organization with respect to the identified variables (present and projected position). The third step amounts to drawing strategic conclusions from Step 2 by assessing how the competition in relationship with the own organization would be positioned along the identified spatial analysis lines.

scholarly journals Aligning Partners in Pediatric Health: Using Geographical Information Systems to Plan Community Coalitions

2020 ◽  
Vol 11 ◽  
pp. 215013272094051
Author(s):  
Margaret B. Nguyen
Keyword(s):  
Primary Care ◽  
Information Systems ◽  
Spatial Analysis ◽  
Child Health ◽  
Geographical Information Systems ◽  
Pediatric Asthma ◽  
Geographical Information ◽  
Community Coalitions ◽  
Community Partners ◽  
Ed Visits

Introduction: Compared with adults, children have higher emergency department (ED) utilization for asthma exacerbation. While community coalitions have been shown to prevent ED visits for asthma, there is little guidance on where to best implement these efforts. Geographical information systems (GIS) technology can help in the selection and coordination of potential coalition partners. This report proposes a model to be used by clinicians and child health equity advocates to strategize high-impact community health interventions. The aims were to identify the clusters of ED utilization for pediatric asthma, evaluate sociodemographic features of the population within the clusters, and identify potential primary care and school community partners. Methods: This model uses ED visit data from 450 nonmilitary California hospitals in 2012. We obtained ZIP code–level counts and rates for patients younger than 18 years discharged with a diagnosis code of 493 for asthma conditions from the California Office of Statewide Health Planning and Development’s Open Portal. We applied GIS spatial analysis techniques to identify statistically significant cluster for pediatric asthma ED utilization. We then locate the candidate community partners within these clusters. Results: There were 181 720 ED visits for asthma for all age groups in 2012 with 70 127 visits for children younger than 18 years. The top 3 geographic clusters for ED utilization rates were located in Fresno, Inglewood, and Richmond City, respectively. Spatial analysis maps illustrate the schools located within 0.5– and 1-mile radii of primary care clinics and provide a visual and statistical description of the population within the clusters. Conclusion: This study demonstrates a model to help clinicians understand how GIS can aid in the selection and creation of coalition building. This is a potentially powerful tool in the addressing child health disparities.

scholarly journals Cartographic Analysis of the Geomorphological Hazards of the Eastern Part of Sheikh Saad Sub-District by Using GIS

2021 ◽  
Vol 11 (3) ◽  
Author(s):  
Hind Fadhil Ibrahim Al-Jubouri ◽  
Firas S Raheem ◽  
Prof Dr Osama K Abdulridah ◽  
Prof Dr Ali A Kazem
Keyword(s):  
Information Systems ◽  
Spatial Analysis ◽  
Geographical Information Systems ◽  
High Efficiency ◽  
Geographical Area ◽  
Three Dimensional ◽  
Geographical Information ◽  
3D Analysis ◽  
Automated Methods ◽  
Modern Technologies

Geographical information systems are the latest applied computer technologies that contribute to supporting contemporary geographical studies through the possibility of working on preparing a database of geographical phenomena and modeling them in a digital form by providing automated methods and a set of systems and programs for managing and processing data with spatial and non-spatial reference, which is one of the important functions in geographic information systems And the base on which it depends to reach the optimal decisions to reveal the spatial relationships and correlations between geographical phenomena and with high efficiency, to become the contemporary method in the method of processing and spatial analysis of geographical information instead of the old traditional methods of geographical analysis, and the system also allowed the geographical area to enter into the era of modern technologies to evaluate phenomena. Geographical forecasting. The research materials and methods are determined by adopting topographical and geological maps, land-sat satellite visuals, and DEM data to form the search database, and based on the GIS program (Arc Map 9.3) and the (Global Mepper 11) program and the extensions of the (Arc Map 9.3) program, which are (Spatial Analysis) And the three-dimensional analysis (3D analysis), and the outputs are the final process through which the results of the research emerge. These outputs show the type of information that will be processed and presented in the form of three-dimensional maps and shapes, studying the most important causes of geomorphological risks for the study area, and developing solutions and treatments through the conclusions and recommendations of the research.

scholarly journals Pemetaan Sebaran Daerah Asal Mahasiswa Universitas Negeri Manado Berbasis Sistem Informasi Geografis

2020 ◽  
Vol 1 (3) ◽  
pp. 1
Author(s):  
Nikel Tambengi ◽  
Joyce Christian Kumaat
Keyword(s):  
Information System ◽  
Spatial Distribution ◽  
Information Systems ◽  
Spatial Analysis ◽  
Geographical Information Systems ◽  
Geographic Location ◽  
Geographical Information ◽  
Distribution Data ◽  
Present Information ◽  
Area Of Origin

The need for information regarding the spatial distribution of the area of origin of students who are currently studying at the Manado State University (UNIMA) is very important because the information built can provide useful input for planning, development, or evaluation. So that the distribution of the areas of origin of students who are studying at UNIMA can be mapped properly, an information system based on Geographical Information Systems (GIS) can be built according to its geographic location. This study aims to create and present information about the spatial distribution of students from UNIMA through GIS. Quantitative type research methods with a spatial analysis approach (spatial analysis) using secondary data. Data analysis techniques through Geographical Information Systems (GIS) to create a digital map of the spatial distribution of student origin using OpenStreetMap and Quantum GIS Zanzibar 3.8.1. The results showed that the spatial distribution of the area from which UNIMA students used GIS, made it easier to present information through digital maps. The information system created can display the distribution data of the student's area of origin, namely the number of students from each province in Indonesia and especially in the form of a distribution map. The largest distribution of student origin came from North Sumatra Province with 1,209, followed by South Sulawesi Province with 893 and North Maluku Province with 650 students.

scholarly journals Traffic Noise Modelling Using Land Use Regression Model Based on Machine Learning, Statistical Regression and GIS

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 5095
Author(s):  
Ahmed Abdulkareem Ahmed Adulaimi ◽  
Biswajeet Pradhan ◽  
Subrata Chakraborty ◽  
Abdullah Alamri
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Information Systems ◽  
Wind Speed ◽  
Mean Square Error ◽  
Geographical Information Systems ◽  
Traffic Noise ◽  
Geographical Information ◽  
Mean Square ◽  
Statistical Regression

This study estimates the equivalent continuous sound pressure level (Leq) during peak daily periods (‘rush hour’) along the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia, using a land use regression (LUR) model based on machine learning, statistical regression, and geographical information systems (GIS). The research utilises two types of soft computing methods including machine learning (i.e., decision tree, random frost algorithms) and statistical regression (i.e., linear regression, support vector regression algorithms) to determine the best approach to create a prediction Leq map at the NKVE in Shah Alam, Malaysia. The selection of the best algorithm is accomplished by considering correlation, correlation coefficient, mean-absolute-error, mean-square-error, root-mean-square-error, and mean absolute percentage error. Traffic noise level was monitored using three sound level meters (TES 52A), and a traffic tally was done to analyse the traffic flow. Wind speed was gauged using a wind speed meter. The study relied on a variety of noise predictors including wind speed, digital elevation model, land use type (specifically, if it was residential, industrial, or natural reserve), residential density, road type (expressway, primary, and secondary) and traffic noise average (Leq). The above parameters were fed as inputs into the LUR model. Additional noise influencing factors such as traffic lights, intersections, road toll gates, gas stations, and public transportation infrastructures (bus stop and bus line) are also considered in this study. The models utilised parameters derived from LiDAR (Light Detection and Ranging) data, and various GIS (Geographical Information Systems) layers were extracted to produce the prediction maps. The results highlighted the superior performances by the machine learning (random forest) models compared to the statistical regression-based models.

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