A Spatial Lattice Model Applied for Meteorological Visualization and Analysis

The geographical information system (GIS) has provided the effective tools to capture, store, manage, analyze and express the spatial information. Gradually, the research region has included the dynamical mechanism and the spatialtime characteristics of the geography process, transmission mechanisms, uncertainty (multi-solutions), predictability, and so on. Corresponding, GIS modeling is one of hotspots of current GIS research. The paper is the embodiment of GIS modeling methods applied to geo-simulation research. Also it provides the model support for the management of the ground water resources. In this paper, the methods for constructing the ground-water conceptual model through 3D stratum model are discussed. Firstly, the constructing method of delaunay triangulation with constraint conditions is proposed, which is suitable to the ground-water flow simulation. Then stratum (or aquifer) multi-DEMs are completed. Based on the multi-DEMs, the Su-Xi-Chan hydrogeological conceptual model is reconstructed by irregular Tri-prism. In order to prevent dispersed irregular tri-prism voxels from disunity, the spatial index mechanism is established for groundwater model. Finally, the 3D groundwater simulation model is integrated with GIS, which can provide data for 3D numerical simulation. The results indicate that the integration of 3D GIS and geo-analysis model be convenient and can accelerate process simulation of geo-science.

In this paper, one approach of Geographic Information System (GIS) and Remote Sensing (RS) assessment of potential natural hazard areas (excess erosion, landslides, flash floods and fires) is presented. For that purpose Pehchevo Municipality in the easternmost part of the Republic of Macedonia is selected as a case study area because of high local impact of natural hazards on the environment, social-demographic situation and local economy. First of all, most relevant static factors for each type of natural hazard are selected (topography, land cover, anthropogenic objects and infrastructure). With GIS and satellite imagery, multi-layer calculation is performed based on available traditional equations, clustering or discreditation procedures. In such way suitable relatively “static” natural hazard maps (models) are produced. Then, dynamic (mostly climate related) factors are included in previous models resulting in appropriate scenarios correlated with different amounts of precipitation, temperature, wind direction etc. Finally, GIS based scenarios are evaluated and tested with field check or very fine resolution Google Earth imagery showing good accuracy. Further development of such GIS models in connection with automatic remote meteorological stations and dynamic satellite imagery (like MODIS) will provide on-time warning for coming natural hazard avoiding potential damages or even causalities.

In order to solve the problem that all levels of civil aviation meteorological centers are unable to provide all the possible meteorological information services that may reach the low-altitude airspace for general aviation flight, this paper proposes a real-time low-altitude meteorological intelligence image collection and processing method. Based on this method, a real-time low-altitude meteorological intelligence image collection system can be designed and implemented. The real-time low-altitude meteorological intelligence image collection system is an information system that provides real-time low-altitude airspace visibility and weather condition information services for image-based flight photography, transmission, processing, and meteorological information dissemination. It includes the image collection subsystem and image processing subsystem, meteorological information publishing Web Server. Among them: The image collection subsystem is mainly used for automatically taking images of the surrounding environment at regular intervals, and transmitting the photographs and their time and place information to the image processing system installed in the low-altitude navigational information service department through the wireless communication device; the image processing subsystem collects images and related information for further processing; the meteorological information publication Web server mainly provides general airline users with services such as consultation, inquiry, and submission of low-altitude meteorological intelligence information of relevant airspace related to images.

A GIS model for personal exposure to PM10 for Dublin commuters

Introduction Evidence suggests that Extracorporeal Cardiopulmonary Resuscitation (ECPR) can improve survival rates for nontraumatic out-of-hospital cardiac arrest (OHCA). However, when ECPR is indicated over 50% of potential candidates are unable to qualify in the current hospital-based system due to geographic limitations. This study employs a Geographic Information System (GIS) model to estimate the number of ECPR eligible patients within the United States in the current hospital-based system, a prehospital ECPR ground-based system, and a prehospital ECPR Helicopter Emergency Medical Services (HEMS)-based system. Methods We constructed a GIS model to estimate ground and helicopter transport times. Time-dependent rates of ECPR eligibility were derived from the Resuscitation Outcome Consortium (ROC) database, while the Cardiac Arrest Registry to Enhance Survival (CARES) registry determined the number of OHCA patients meeting ECPR criteria within designated transportation times. Emergency Medical Services (EMS) response time, ECPR candidacy determination time, and on-scene time were modeled based on data from the EROCA trial. The combined model was used to estimate the total ECPR eligibility in each system. Results The CARES registry recorded 736,066 OHCA patients from 2013 to 2021. After applying clinical criteria, 24,661 (3.4%) ECPR-indicated OHCA were identified. When considering overall ECPR eligibility within 45 min from OHCA to initiation, only 11.76% of OHCA where ECPR was indicated were eligible in the current hospital-based system. The prehospital ECPR HEMS-based system exhibited a four-fold increase in ECPR eligibility (49.3%), while the prehospital ground-based system showed a more than two-fold increase (28.4%). Conclusions The study demonstrates a two-fold increase in ECPR eligibility for a prehospital ECPR ground-based system and a four-fold increase for a prehospital ECPR HEMS-based system compared to the current hospital-based ECPR system. This novel GIS model can inform future ECPR implementation strategies, optimizing systems of care.

The objective of this paper is to argue for a new model of Geographic Information System (GIS) implementation, Web-based GIS, for supporting decision-making in local government. In this paper, the GIS implementation in Ascension Parish Government, Louisiana is used as a case study to show how Web-based GIS implementation can improve the system use and maximize the value of GIS infrastructures for the community. The author first defines the concept of implementation and role of GIS in local government as a base for further discussion. Special attention is given to analyze the variables that influence the parish employees’ acceptance and use of the GIS in the work. A three-level evaluation framework is established to evaluate GIS implementation strategy and GIS impacts on decision-making in local government. Research findings will be used to direct future implementation strategy. The paper concludes that Web-based GIS, by combining GIS technology and Internet technology, can distribute GIS resources and geo-processing tools to a wider range of potential users. In addition, flexible user-oriented GIS applications can best be developed with intensive user participation and feedback. One of the arguments proposed is that the Web-based GIS can be used to help the local government employees to provide better service for the public and improve the performance of a specific local government agency in processing land development permit applications.

Environmental risk assessment based on the allergenicity of urban green infrastructure: Case study of an urban park

Modeling BVOC isoprene emissions based on a GIS and remote sensing database

Context In New Zealand, the introduced brushtail possum, Trichosurus vulpecula, is a reservoir of bovine tuberculosis and as such poses a major threat to the livestock industry. Aerial 1080 poisoning is an important tool for possum control but is expensive, creating an ongoing need for ever more cost-effective ways of using this technique. Aims To develop geographic information system (GIS) models to better predict spatial variation in the distribution of unmanaged possum populations, to facilitate better targeting of control activities. Methods Relative abundance of possums and their distribution among habitat types were surveyed in a dry high-country area of the northern South Island. Two GIS-based models were developed to predict the relative abundance of possums on trap lines. The first model used remotely sensed (digital) environmental data; the second complemented the remotely sensed data with fine-scale habitat and topographic data collected on the ground. Key results Digital environmental factors and habitat features proved to be key predictors of relative possum abundance. In both GIS models, height above valley floor, presence of forest cover and mean annual temperature were the strongest predictors. Conclusions Predictive maps (projections) of relative possum abundance produced from these models can provide useful decision-support tools for pest-control managers, by enabling possum control to be targeted spatially. Implications Spatially targeted pest control could allow effective control activities for invasive species or disease vectors to be applied at a lower cost for the same benefit.

Spatial modeling is an integral component of most geographic information systems (GISs). However, conventional GIS modeling techniques can require substantial processing time and storage space and have limited statistical and machine learning functionality. To address these limitations, many have parallelized spatial models using multiple coding libraries and have applied those models in a multiprocessor environment. Few, however, have recognized the inefficiencies associated with the underlying spatial modeling framework used to implement such analyses. In this paper, we identify a common inefficiency in processing spatial models and demonstrate a novel approach to address it using lazy evaluation techniques. Furthermore, we introduce a new coding library that integrates Accord.NET and ALGLIB numeric libraries and uses lazy evaluation to facilitate a wide range of spatial, statistical, and machine learning procedures within a new GIS modeling framework called function modeling. Results from simulations show a 64.3% reduction in processing time and an 84.4% reduction in storage space attributable to function modeling. In an applied case study, this translated to a reduction in processing time from 2247 h to 488 h and a reduction is storage space from 152 terabytes to 913 gigabytes.

BackgroundAssessment of the spatial accessibility of hospital accident and emergency departments as perceived by local residents has not previously been investigated. Perceived accessibility may affect where, when, and whether potential patients attend for treatment. Using data on 11,853 respondents to a population survey in Caerphilly county borough, Wales, UK, we present an analysis comparing the accessibility of accident and emergency departments as reported by local residents and drive-time to the nearest accident and emergency department modelled using a geographical information system (GIS).ResultsMedian drive-times were significantly shorter in the lowest perceived access category and longer in the best perceived access category (p < 0.001). The perceived access and GIS modelled drive-time variables were positively correlated (Spearman's rank correlation coefficient, r = 0.38, p < 0.01). The strongest correlation was found for respondents living in areas in which nearly all households had a car or van (r = 0.47, p < 0.01). Correlations were stronger among respondents reporting good access to public transport and among those reporting a recent accident and emergency attendance for injury treatment compared to other respondents. Correlation coefficients did not vary substantially by levels of household income. Drive-time, road distance and straight-line distance were highly inter-correlated and substituting road distance or straight-line distance as the GIS modelled spatial accessibility measure only marginally decreased the magnitude of the correlations between perceived and GIS modelled access.ConclusionThis study provides evidence that the accessibility of hospital-based health care services as perceived by local residents is related to measures of spatial accessibility modelled using GIS. For studies that aim to model geographical separation in a way that correlates well with the perception of local residents, there may be minimal advantage in using sophisticated measures. Straight-line distance, which can be calculated without GIS, may be as good as GIS-modelled drive-time or distance for this purpose. These findings will be of importance to health policy makers and local planners who seek to obtain local information on access to services through focussed assessments of residents' concerns over accessibility and GIS modelling.

Assessment of constructing canopy urban heat island temperatures from thermal images: An integrated multi-scale approach

Eligibility of out-of-hospital cardiac arrest patients for extracorporeal cardiopulmonary resuscitation in the United States: A geographic information system model

Energy monitoring using statistical process control (SPC) methods makes it more straightforward to identify patterns and trends to decrease energy consumption more effectively. The literature review of energy consumption monitoring with SPC techniques generally focuses on the temporal aspect of variation. However, due to the spatial nature of energy data, enhancing these methods to incorporate temporal and spatial aspects would improve the accuracy of the diagnostic information, underscoring simultaneous detection of the time and location of changes. Thus, the main novelty of this work is the spatial modeling and spatiotemporal monitoring of electricity consumption. For this purpose, the study used actual electricity consumption data from eight western cities of Mazandaran province in the north of Iran for spatial modeling using spatial regression models and a geographically weighted regression (GWR) model. The prediction performance evaluation of spatial models showed GWR as an appropriate model, whose coefficients were monitored through a generalized likelihood ratio (GLR) chart in phase II. The GLR chart detected two changes in consumption, and its performance was confirmed based on the statements from electricity experts relying on meteorological information and floating population data. Furthermore, the performance of the GLR chart was evaluated using out-of-control average run length (ARL1) across three different scenarios. The findings indicate that the GLR chart can effectively detect any sizes of shifts (δ), ranging from 5% to 100% of the model's parameter value. Additionally, with larger values of δ, the ARL1 decreases, resulting in faster detection of changes in the model.

This research aimed to modeling a geographic information system (GIS) using graph clustering. GIS discuss about spatial data that consists of several objects including points, lines, regions, rectangles, surfaces, volumes, and the all data representation of an object on earth. Graph-based clustering techniques are extremely useful because many real word problem domains have a natural graph representation. This research focused on case GIS for family empowerment post (POSDAYA) which is one of the programs for improving the quality of human beings, especially in achieving the Millennium Development Goals (MDGs) in Indonesia that has a priority on family-based poverty reduction. The results are the GIS framework model that use three components such as graph clustering, layering, and the view of data properties. GIS provided the spatial data and its properties, and graph clustering as a method or algorithm used to make the layers properties and related to each points based on similarities.