Geographic Information System Model Research Articles

Application for simulating public health problems during floods around the Loei River in Thailand: the implementation of a geographic information system and structural equation model

BackgroundFloods cause not only damage but also public health issues. Developing an application to simulate public health problems during floods around the Loei River by implementing geographic information system (GIS) and structural equation model (SEM) techniques could help improve preparedness and aid plans in response to such problems in general and at the subdistrict level. As a result, the effects of public health problems would be physically and mentally less severe.MethodsThis research and development study examines cross-sectional survey data. Data on demographics, flood severity, preparedness, help, and public health problems during floods were collected using a five-part questionnaire. Calculated from the population proportion living within 300 m of the Loei River, the sample size was 560 people. The participants in each subdistrict were recruited proportionally in line with the course of the Loei River. Compared to the empirical data, the data analysis examined the causal model of public health problems during floods, flood severity, preparedness, and help. The standardized factor loadings obtained from the SEM analysis were substituted as the loadings in the equations for simulating public health problems during floods.ResultsThe results revealed that the causal model of public health problems during floods, flood severity, preparation, and help agreed with the empirical data. Flood severity, preparedness, and aid (χ2 = 479.757, df = 160, p value <.05, CFI = 0.985, RMSEA = 0.060, χ2/df = 2.998) could explain 7.7% of public health problems. The computed values were applied in a GIS environment to simulate public health problem situations at the province, district, and subdistrict levels.ConclusionsFlood severity and public health problems during floods were positively correlated; in contrast, preparedness and help showed an inverse relationship with public health problems. A total of 7.7% of the variance in public health problems during floods could be predicted. The analysed data were assigned in the GIS environment in the developed application to simulate public health problem situations during floods.

Effects of the Digital Elevation Model and Hydrological Processing Algorithms on the Geomorphological Parameterization

Hydrological cycle research requires a detailed analysis of the involved parameters to understand watershed behavior comprehensively. In recent decades, both Geographic Information Systems (GIS) and Digital Elevation Models (DEMs) were implemented and took a substantial role in watershed geomorphological parameterization; however, the variability of these instruments remains a challenge, together with high-resolution DEMs being unavailable, requiring digital processing to improve resolution. This research aims to merge DEMs and evaluate GIS geoprocessing algorithms to determine drainage networks and the geomorphological parametrization of a semiarid watershed. DEMs with resolutions of 1.5, 5, 12.5, and 30 m, the Jenson/Domingue (J/D) and Wang/Liu (W/L) fill algorithms; and D8, D, KRA, and MFD flow routing algorithms were used. One of the research findings proved that the divergences of the drainage networks are mainly attributed to filling algorithms and not flow routing algorithms; the shifts between the networks obtained in the processes reach horizontal distances up to 300 m. Since the water movement within the watershed depends on geomorphological characteristics, it is suggested that DEM-based hydrological studies specify both the resolution and the algorithms used in the parametrization to validate the rigidity of the research, improving estimate areas of high hydrological risk.

Composition analysis and distributed assumption GIS model of normal stress on the slope sliding surface

For the limit equilibrium method based on the assumption model of sliding surface normal stress, the more reasonable the assumed sliding surface normal stress model is, the higher the accuracy of the calculation results will be, which is of great significance to improve the theoretical calculation accuracy. Combined with the powerful spatial data analysis ability of Geographic Information Systems (GIS), the expression of the normal stress distribution function of the slip surface is deduced by establishing a GIS-based three-dimensional slope stability analysis model and limit equilibrium equations. By analyzing the composition of the normal stress distribution on the sliding surface, the composition of the normal stress on the sliding surface is obtained, and then the assumed model of the normal stress distribution on the sliding surface is constructed. Finally, the assumed GIS model of the sliding surface normal stress distribution is verified by calculating the proportion. The model overcomes the problem that the stability factor of the slope will cause large errors in the three-dimensional state, and provides a theoretical calculation basis for the establishment of a three-dimensional slope limit equilibrium method based on the assumption of normal stress on the slip surface in GIS.

The gas grid as a vector for regional decarbonisation - a techno economic case study for biomethane injection and natural gas heavy goods vehicles

This paper presents a novel method for incorporating the seasonal variations in gas demand into an assessment of the economic viability of a biomethane production and injection facility. A simulation of a gas distribution (Dx) network was built to investigate the impact of limits imposed by the gas network operator on the quantity of biomethane that can be injected. The results calculated the grid’s capacity to accept biomethane on an hourly basis over the course of a year. Scenarios of maximum, minimum and no demand at a Dx-connected compressed natural gas (CNG) filling station were computed for the 3 potential locations being investigated for the biomethane production and injection facility. This data was then used to determine a range of possible plant sizes for each potential facility location and CNG demand scenario. Next, a spatially explicit geographical information systems (GIS) model was created to map the distribution of feedstock suitable for biomethane production in the surrounding area and determine transportation distances. These two submodels fed into a techno-economic model that calculates the net present value (NPV) and levelised cost of energy (LCOE) for each configuration. Notably, the profitability of the plant was seen to increase proportionally with an increase in demand at the CNG filling station. Location 2 was determined to be the most economically viable site for the biomethane production and injection facility. The most economically competitive configurations resulted in an LCOE of 81.63 €/MWh, 83.59 €/MWh, and 83.73 €/MWh, with corresponding NPVs of M€ 9.56, M€ 3.98 and M€ 0.94, for maximum, minimum and no demand at the CNG filling station respectively. The most competitive configurations are achieved at a plant size of 115 GWh/a for the maximum CNG demand, 82.2 GWh/a for the minimum CNG demand, and 81.8 GWh/a for no CNG demand replacing 40%, 34%, and 35% of annual natural gas demand in the Dx network respectively.

Geographic information system models with fuzzy logic for susceptibility maps of debris flow using multiple types of parameters: a case study in Pinggu District of Beijing, China

Abstract. Debris flow is one of the main causes of loss of life and infrastructure damage in mountainous areas. This hazard should be recognized in the early stage of land development planning. According to field investigation and expert experience, a scientific and effective quantitative susceptibility assessment model was established in Pinggu District of Beijing. This model is based on geographic information system (GIS) combined with grey relational, data-driven and fuzzy logic methods. The influence factors, which are divided into two categories and consistent with the system characteristics of a debris flow gully, are selected, but also a new important factor is proposed. The results of the 17 models are verified using data published by the authority and validated by two other indexes, as well as area under curve (AUC). Through the comparison and analysis of the results, we believe that the streamlining of factors and scientific classification should attract attention from other researchers to optimize a model. We also propose a good perspective to make better use of the watershed feature parameters. These parameters fit well with the watershed units. With full use of insufficient data, scientific calculation and reliable results, the final optimal susceptibility map could potentially help decision makers in determining regional-scale land use planning and debris flow hazard mitigation. The model has advantages in economically weak areas with insufficient data in mountainous areas because of its simplicity, interpretability and engineering usefulness.

Harnessing the Potentiality of Farm Landscape for Trees Based on Satellite Evaluation: A GIS Modeling Perspective

Increasing agroforestry areas is an important step to adapt to climate change, increase food security, and have many ecological and socio-economical benefits. Proper planning and strategies are required for the assessment of land potential and selection of suitable land for the multifunctional benefits of agroforestry. Remote sensing (RS) and geographical information system (GIS) tools are widely used to identify the priority areas for agroforestry and policy-making. The multi-high resolutions of Google pro mosaicked images were used as a base map for precision, detailed analysis, and valid interpretation. To identify the farm landscape suitability areas in the Belpada block of Belangiri district, Odisha, a GIS modeling approach was used based on satellite data measurement. The post-monsoon multi-date monthly cloud-free Landsat-8 data and products of the Digital Elevation Model were used to understand the farm landscape characteristics of agroforestry. Soil wetness, slope, drainage, and Normalized Difference Vegetation Index (NDVI) were used in the preparation of landscape suitability analysis. Overall 27.8% (134.16 sq. km) of land was highly suitable, 50.0% (241.85 sq. km) of land was moderately suitable and 19.7% (94.98 sq. km) was marginally suitable and the remaining 2.5% (12.01 sq. km) of land was found unsuitable for agroforestry. Out of 116 villages, 14 villages are found with high (greater than 70%) farmland potentiality, the highest is found in the Jalia village. The moderate and highly suitable land/villages should be given preference for tree-based farming in various agroforestry arrangements. The high-resolution farm landscape potential grid maps were produced for the first time which was earlier a research gap in the past that will support micro-level agroforestry planning. There is a need for a robust synergic approach when integrated with native and multifunctional trees in potentially suitable agroforestry farmland with adequate watershed management and conservation practices enriched with indigenous knowledge that will significantly support achieving the many sustainable development goals (SDGs) up to the smallest unit (village) level.

Environmental Fragility Zoning Using GIS and AHP Modeling: Perspectives for the Conservation of Natural Ecosystems in Brazil

The degradation of natural ecosystems triggers global environmental, economic, and social problems. To prevent this, it is necessary to identify the aptitude of priority areas for conservation or use by considering land fragility from multiple environmental and spatial perspectives. We applied the concept of environmental fragility to a hydrographic basin in southeastern Brazil that establishes (i) potential fragility levels according to slope, soil classes, geological domains, drainage hierarchy, and rainfall information using an algebraic map, and (ii) emerging fragility levels via the addition of the land-use parameters. The methodological approach involved the integration of the analytic hierarchy process (AHP) and weighted linear combination (WLC) into a geographic information system (GIS). The medium and slightly low fragility classes predominated in terms of potential (~60%), and emerging (~70%) environmental fragility models used to model the basin. The model indicated that high and extremely high potential fragilities were concentrated in the upper basin, a region that is considered a global biodiversity hotspot. The areas with high/extremely high classes of emerging fragility in the upper basin decreased, indicating that the natural cover classes and land-use types are not in danger. We also introduce acceptable conservation practices for land management and use according to the environmental fragility categories established in the present work. The methodology applied in this study can be replicated in other global ecoregions. It provides low-cost territorial and environmental zoning and flexible replication and can be adjusted by administrators who are interested in land-use planning.

Spatial suitability and multi-scenarios for land use: Simulation and policy insights from the production-living-ecological perspective

In the context of the current reorganization of China's territorial spatial planning system, this paper focuses on supporting scientific land use decision-making through multiple policy scenarios and dynamic simulations. Based on the dominant land use function orientation from the production-living-ecological perspective, this paper incorporates the spatial suitability evaluation of a given time-point into a dynamic-driven simulation and dynamic monitoring framework. In this framework, synthetically utilizing GIS (geographic information system) and SD (system dynamics) models with research cases achieve the production-living-ecological spatial suitability evaluation and establishes a system dynamics model that tightly grasps its formation mechanism. The results show that, in terms of multi-scenario simulations, different evolutionary characteristics of the scale, suitability and dynamic monitoring indicators of the production-living-ecological space are observed. In conclusion, what is provided is a complete and effective spatial suitability evaluation framework, in which the production-living-ecological perspective is consistent with the functional orientation. In conclusion, what is offered is an intact and valid framework for spatial suitability evaluation, where the viewpoint of production-living-ecological functions complied with the function orientation. Meanwhile, the combination of a suitability evaluation and scenario simulation was not only favorable in terms of grasping the utilizing actuality of spatial suitability level but also assisted in mastering the evolution trends and monitoring the spatial bearing capacity. Further, the united utilization of the GIS and SD models also provided effectual technical support. Finally, given the study’s results, policy insights are proposed, covering the optimization and scientific decision-making concerning development patterns in production-living-ecological space.

Multilevel urban geographic information system models for overground and underground design

The urban geographic information system includes a wide range of different types of complex data collected from various sources. Objects described vary from industrial to ecological as city itself being tied to environment also affects nature. Preventing pollution and other emergency situations caused by industrial and human activity in general is both topical and burning that is widely recognized so there is need for a comprehensive study of the possibilities to develop geographic information system in order to increase efficiency of city design. The purpose of the article is to develop urban geographic information system that provides a solution to the problem and also helps to reduce city planning costs as a system representing city on different levels and obtaining all the necessary information in one source. Urban geographic information system developed with the latest digital technologies allows to make city planning more flexible to technical, economical, social and other changes. Three-dimensional visualization is one of the up-to-date ways to expand geographic information system functionality and this paper gives information of the subject area: research on nowadays data models and data processing methods was conducted. And on this basis urban geographic information system extended data model including underground infrastructure level was offered and possible approaches to increase efficiency of data processing in it were formed. The three-dimensional model provides a solution for complex underground and overground design with digital technologies on a basis of geographic information system that includes multilevel data organization.

Dynamic simulation of debris flow waste‐shoal land use based on an integrated system dynamics–geographic information systems model

AbstractGiven the increased conflict between human development and land use in fragile mountainous areas, debris flow waste‐shoal land (DFWSL) use has shown beneficial effects and drawn public concern in recent years. However, temporal–spatial future change trends of DFWSL are not yet clear, meanwhile widely applied singular methods, including the system dynamics (SD) model and geographic information systems (GIS), are unsatisfactory. To better assess the sustainability and challenges of DFWSL use and provide reference for future development, we propose a hybrid SD–GIS model for characterizing complex relationships and performing dynamic simulations and spatial spreading. A causality flowchart was plotted and response equation defining the changes was established to predict macro land‐use demands in the SD model component. Additionally, the spatial distribution of land‐use types was determined using the GIS model component. The Awang DFWSL system in the Yunnan Province, China was analyzed using the coupled method. The results revealed a steadily declining trend in cultivated land and water areas accompanied by a substantial increase in construction land. Future projections indicate that from 2018 to 2025, the total area of cultivated land will decrease by nearly half compared with that in 2018 and decrease to one‐quarter of the level in 2018 by 2030. These predictions consider the reciprocal influences of social, economic, and ecological indicators, and risk factors, such as debris flow scale and frequency. This study provides a new approach for estimating future trends of DFWSL use and increases the awareness of the spatiotemporal changes encountered when using marginal land.

Improving performance of mass real estate valuation through application of the dataset optimization and Spatially Constrained Multivariate Clustering Analysis

Mass real estate valuation is a multidimensional and complex matter because it depends on many constant and time-varying factors. It is desirable to have high level of model performance in the development of mass real estate valuation models for the development of sustainable real estate management strategies. For this reason, this study aims to develop a comprehensive methodology that increases the performance of mass real estate valuation models by using optimized datasets and clustering geographical value in Geographic Information Systems (GIS) modeling environment. A case study was carried out in Istanbul and Kocaeli provinces covering neighborhoods with different levels of socio-economic-development. This study was carried out using the big data, which was prepared for 121 criteria incorporating approximately 200.000 real estate values. Firstly, datasets were optimized by using the Boxplot technique concerning dataset-based outliers and Cluster and Outlier Analysis techniques were used regarding the location-based outliers. Next, 22 of the criteria affecting the value was determined with Pearson Correlation technique through analyzing the local relationship between real estate value and the criteria. Based on the result of the Spatially Constrained Multivariate Clustering Analysis (SCMCA) analysis, five different geographical value clusters with similar socio-development characteristics were detected. Mass valuation performances were tested covering all study area and these five clustered areas assessed with the use of Multiple Regression Analysis (MRA) model were used commonly in developing mass real estate valuation models. The model accuracies were evaluated through performance measurement metrics used in machine learning (MAE, MSE, RMSE) and mass real estate valuation (WtR, COD, PRD) technique that was recommended by IAAO. Considering the performances of the models, value prediction models based on geographical value clusters were more successful than the entire of study area model.

Lineaments Tracing of Magnetic Sources Depending on the Aeromagnetic Data: Case Study in Salman Basin and Surrounding Areas, Southern Desert of Iraq

This study deals with the processing and analysis of aeromagnetic data for an area covering the region of Salman Basin within AL- Muthanna, Iraq. The objective of this study is to use the available satellite images and aeromagnetic data to investigate the boundaries of geological structures. For further understanding of the tectonic of the study area, the Centre for Exploration Targeting Grid Analysis extension which is available in Oasis montaj software was applied to the data. It is proposed that it provides unbiased results, but to check the results Tilt Derivative or local phase derivative is used where the zero-contour line of the tilt derivative strongly delineates the edges of structures. Also, the drainage pattern for the basin is done using Geographic Information System and Digital Elevation Model raster to see if the drainage is affected by the subsurface lineaments that are determined using the Centre for Exploration Targeting technique. As a result, the deep main linear features follow the general trend of the main faults that formed the Salman Basin. The new lineaments were discovered using the Exploration Targeting grid analysis, zero contour of. There are two systems of lineaments in the present study area. The first set is taken the NW-NE direction perpendicular to the extensional structures, while the second set has the NS-EW direction.

Implimentasi Geometri BIM-GIS Terhadap Navigasi Pengguna bagi Persekitaran Dalaman: Satu Kajian

The integration between Building Information Modeling (BIM) and Geographic Information System (GIS) is frequently discussed from time to time due to the benefits offered, especially for user navigation in the building’s indoor environment. Inequality in terms of 3D geometry and level of detail was identified as contributing to the difficulty in integrating the two models. Although there are limitations, various studies have been conducted to improve the method of integration so that the data can be utilised successfully. Therefore, this paper focuses on reviewing relevant research papers to (1) identify the basic information and structure of BIM and GIS models, (2) study the relationship and integration that occurs between the two types of models at the model geometry level, and (3) identify the application of the user navigation to the indoor building environment and highlight the future direction for the integration of BIM and GIS study. From this study, it can be identified that the standardised geometry schemes are the keys to the success of BIM and GIS integration. It can indirectly enhance the interoperability of data and helps in the development of a holistic data model for a broader scope of application. Particularly in the era of rapid development of machine learning, deep learning and internet of things (IoT) technology, where the application of integrated products through semantic web methods is seen to have an expansive room to grow significantly for location-based services (LBS) and emergency application.

Mapping Health Information System For Breast Cancer Patients Through An Epidemiological Study At The Prof.W.Z. Johannes General Hospital And Its Distribution In East Nusa Tenggara Province, Indonesia

Mapping the spread of disease patients with the Geographic Information System (GIS) is very helpful in providing information on the location of who comes for treatment at the hospital. This research was conducted on all breast cancer patients who were recorded in the medical records of 2017-2019 at Prof. W.Z. Johannes General Hospital Kupang. This research is an analytic observational with a cross-sectional study approach, using a Geographic Information System (GIS) modeling through spatial analysis to obtain an overview of the distribution of breast cancer based on epidemiological variables. Based on the results of research on breast cancer from 2017 to 2019 at Prof. WZ Johannes General Hospital Kupang according to the host epidemiology variable was based on age in highest are between the age of 51 and 60 with 18 patients in total; based on the education level is highest, there are 112 patients in a total of Senior High School graduate; based on the occupation in highest, there are 81 patients worked as Civil Servant, according to the highest time variable in 2019, there were as many as 91 cases, followed by 83 total cases in 2018, and there were as many as 74 cases in 2017.

A geographical information system model to define COVID-19 problem areas with an analysis in the socio-economic context at the regional scale in the North of Spain

The work presented concerns the spatial behaviour of coronavirus disease 2019 (COVID-19) at the regional scale and the socio-economic context of problem areas over the 2020-2021 period. We propose a replicable geographical information systems (GIS) methodology based on geocodification and analysis of COVID-19 microdata registered by health authorities of the Government of Cantabria, Spain from the beginning of the pandemic register (29th February 2020) to 2nd December 2021. The spatial behaviour of the virus was studied using ArcGIS Pro and a 1x1 km vector grid as the homogeneous reference layer. The GIS analysis of 45,392 geocoded cases revealed a clear process of spatial contraction of the virus after the spread in 2020 with 432 km2 of problem areas reduced to 126.72 km2 in 2021. The socio-economic framework showed complex relationships between COVID-19 cases and the explanatory variables related to household characteristics, socio-economic conditions and demographic structure. Local bivariate analysis showed fuzzier results in persistent hotspots in urban and peri-urban areas. Questions about ‘where, when and how’ contribute to learning from experience as we must draw inspiration from, and explore connections to, those confronting the issues related to the current pandemic.

Applying the integrated model of GIS and AHP for evaluating ecological suitability of Ming aralia (Polyscias fruticosa): a case study of Hai Hau district, Nam Dinh province, Vietnam

The Hai Hau district, located in the southeast region of the Nam Dinh province, belongs to the Red river delta where residents mainly rely on agriculture, fisheries, and handicrafts as their livelihood. In recent years, many households have switched from growing rice to Ming aralia (Polyscias fruticosa), a tropical medicinal plant with high economic value. On the basis of analysing the influence of bioclimatological and soil conditions in Hai Hau district, Nam Dinh province on the growth and development of cloves through the integrated Analytic Hierarchy Process (AHP) - Geographic Information System (GIS) model, this study has divided the adaptive thresholds for each climate factor with corresponding weighted values to evaluate the ecological adaptability of Ming aralia to the criteria’s in Hai Hau district. The research results show that the Hai Hau district has 25.3% of highly suitable area (S1), 33.8% of moderately suitable area (S2), 23.3% of marginally suitable area (S3), and 17.6% of not suitable area (N) for Ming aralia development. The suitable area for cultivation of Ming aralia with high economic efficiency is about 13,522.37 ha, or 59.1% of the total area, located in the northern communes of the district. This finding is a scientific basis for planning, planting, and exploitation of Ming aralia to improve sustainable livelihoods and promote local socioeconomics.

Florida’s Aquifer Vulnerability to Nitrate Contamination: A GIS Model

Groundwater is a crucial natural resource in the state of Florida. since it supports to environmental, social, and economic aspects of the country. Groundwater will not be contaminated easily but it is difficult to restore once it is contaminated. Since its extensive usage in agricultural activities in the state of Florida, groundwater has degraded in recent years, resulting in many direct and indirect impacts, particularly nitrogen content in the form of nitrates using Geographical Information System (GIS) technology, the researchers investigated the effects of groundwater on Nitrogen (NO3) content in the study area by creating a spatial distribution of NO3 contamination, which was then analyzed using GIS, Kriging Interpolation, and the DRASTIC model to determine the susceptibility of groundwater to NO3 contamination. The final result depicts the model’s performance as vulnerability groups, which are based on natural breaks showing places that are more susceptible to nitrogen pollution. The map highlighted that the south zone of Florida was more vulnerable to nitrogen contamination, necessitating more careful wastewater disposal system planning.

Mapping agricultural use of pesticides to enable research and environmental health actions in Belgium

Given the many public health and environmental impacts associated with the use of pesticides, comprehensive pesticide application data are a high priority for environmental and health professionals, government agencies, and community groups in Wallonia (Belgium). In that context, geographic information system (GIS) approaches for mapping estimates of agricultural pesticide use were developed in the present study. Data on pesticide application rates and high-resolution annual datasets of the geographic distribution of crops were used to complete this analysis in Wallonia over the period 2015–2017. The method was implemented in Python in order to allow easy update and improvements of maps, or to segment maps by individual pesticides, chemical groups of pesticides (e.g. insecticides, herbicides), etc. Linked databases were created to classify, select, and possibly weight AIs according to specific requests and criteria. The results provide a first map of agricultural pesticide use in Wallonia, which depicts the best picture up to now of their geographic distribution. Maps of fungicides, herbicides, and plant growth regulators showed quite similar spatial patterns as the map of the combination of all pesticides. In contrast, the insecticide map showed a specific pattern related almost exclusively to dwarf-tree orchards in some municipalities in northern Wallonia. This research work is a preliminary result on the spatial characterization of agricultural pesticide use in Wallonia and give a valuable basis for research and environmental health actions in Belgium. Forthcoming developments will focus on exposure characterization to agricultural pesticides using GIS models. Using this information, policymakers will able to detect potential priority zones and take action to check and reduce agricultural pesticide loads in the environment.

Processes and events in the centre: a dynamic data model for representing spatial change

ABSTRACT Traditional geographic information system models for map representation use superposition of layers to model physical reality, neglecting the integrity of the environment and limiting the ability to express interactions between features in complex phenomenon. This results in limitations regarding dynamic simulation and geographic causality reasoning. In this paper, we extend the framework of the geographic scene by formalizing the relationship between geographic processes and events to construct a dynamic data model: the process-event-centred dynamic data model. The key element of this data model is relationships between processes, events, and states of the natural or man-made phenomenon of interest. The identified relationships can be translated into a network of hierarchical, developmental, and causal graphs and realized in the Neo4j graph database. The implementation in the graph database supports spatio-temporal reasoning in geographic scenes and achieves an organizational framework for simulating spatio-temporal dynamics and complex calculations. The example of a 2019 mega-typhoon process is used to demonstrate the introduced process-event-centred model and its implementation in the graph database. A series of queries to the graph database show the capabilities of the data model for spatial reasoning and dynamic modeling.

Identifying High-risk Areas of Dengue by Meteorological factors in Thailand

Dengue is a public health problem in Thailand. This study aimed to investigate the spatial distribution pattern and meteorological factors associated with dengue in the central region over the past 10 years. Dengue incidence and meteorological data were collected from 2007 to 2016. Geographical information system modeling was done to generate sub-district level risk map of dengue incidences, with five risk levels. The findings revealed that mean temperature, rainfall, relative humidity, and wind speed are important meteorological factors that influence component of dengue disease occurrence. The identified sub-district level risk estimates can be very useful in planning and executing dengue control in Thailand.