Geographic Information System (GIS) modeling approach to determine the fastest delivery routes

Geographic information systems adoption model: A partial least square-structural equation modeling analysis approach

The ability of Geographic Information System (GIS) to organize, analyze, visualize and integrate spatial data has been at the top of its primary uses among professional industries. However, considering the extensive adoption of Information System (IS) throughout history for government organizations' or citizens' disaster response, the implementation of geographical elements is still minimal. Previous GIS models and framework studies, particularly in developing countries, were affected by pandemic pressure, competitiveness pressure, change management, and security factors. Thus, this study aims to develop a model for the successful adoption of GIS using the Technology Acceptance Model (TAM), and De Lone and Mc Lean Information Success Model and analyze the applicability of the existing factors to enhance the performance of Public Sector Organizations (PSOs). From the study, a new conceptual framework was proposed to examine the effects of factors on GIS adoption that impact performance among PSOs from the perspective of Saudi Arabia. Quantitative methods were used to collect data through a questionnaire distributed to 350 respondents from PSO, and only 272 were found to be valid. Partial Least Square Structural Equation Modeling (PLS-SEM) validated the GIS model. The finding revealed that system quality, service quality, change management, competitiveness pressure, perceived ease of use, perceived usefulness, and security factors significantly and positively affected GIS adoption. The study also showed that GIS adoption substantially affected PSO performance. The proposed model provides insight into how GIS adoption can eventually enhance performance among PSOs. In essence, the study contributes to the running of PSO and the decisions taken by policymakers.

An evaluation of primary schools and its accessibility using GIS techniques: a case study of Prayagraj district, India

The spatial analysis of primary schools is needed to ensure that they meet standards set by the Government of India. Therefore, mapping of Prayagraj district primary schools is carried out in this research work. The spatial analysis of schools is performed using various parameters that include: literacy rate, student gender ratio, teacher taught ratio, and students' toilets ratio. Several problems were revealed in the GIS based analysis of schools by highlighting their locations on the map. The results identify the location of all those schools where there is a need to take corrective measures. Further, the route analysis between home and school is handled in this research work. Sometimes, commutation is the sole criteria in the selection of a school. Therefore, optimal path analysis has been performed based on three parameters, i.e. population density, traveling distance and traveling time. A combined weight function is also given to find the optimal path. This work will help people to select the best possible path based upon their requirements. There is often a requirement to reach all the schools to distribute the goods or other physical monitoring activities. This work gave the optimal route to visit all the schools by using the Travelling Salesman Problem (TSP). School accessibility results obtained in this research through TSP will assist in choosing the best possible route. This will help the government identify locations where new roads need to be constructed to have road connectivity.

Statistical Study Based on the Kriging Method and Geographic Mapping in Rigid Pavement Defects in Southern Chile

ASTM D6433 is used to assess the need for maintenance of pavement sections. Although the Pavement Condition Index (PCI) factor calculation method provides reliable values, this method analyzes sections and defects individually and indicates current maintenance needs, but it cannot be used to predict the occurrence of new defects. Therefore, it is necessary to complement this method by considering variables that influence the occurrence of faults, among which are the geospatial distribution and the specific characteristics of the slabs. This research focuses on the identification of multiple types of disturbances that exist in Portland Cement Pavements (PCC), located in a high traffic area in the city of Valdivia (Chile). A spatial geostatistical relationship is established through visual inspection using geographical maps, as well as distribution, using the kriging method. This technique makes use of variograms that allow quantifying the parameters used in this study, thus expressing the spatial autocorrelation of the faults analyzed. From the results obtained by spatial geostatistics and kriging, it is possible to generate a data correlation for the distribution and characteristics of the streets considered. In addition, a co-kriging method is established instead of an ordinary kriging method. The relationship between observed and predicted values improved from 0.3327 to 0.5770. The width of the slabs, as well as some streets, is shown in our analysis to be unimportant. For better model accuracy, the number of covariates associated with the type of vehicle traffic, the age and shape of the slabs, and the construction techniques used for the pavement needs to increase.

Spatial-based model for oil palm plantation suitability rating and its smallholder farmers’ competitiveness: a case study at Kampar Regency, Riau Province, Indonesia

Purpose

This study aims to construct models to classify independent smallholder farmers’ (ISFs) plantation suitability and its competitiveness index. It proposes the models with the objective to accommodate ISFs as the main source of raw material for the palm oil industry. It was predicted that the supply of oil palm fresh fruit bunch would depend on ISFs’ plantations due to the government policy that restricts the expansion of the plantations.

Design/methodology/approach

The study was designed using a geographic information system approach and analytical hierarchy process for mapping the suitability of smallholder farmers’ oil palm plantation in the Kampar Regency. The competitiveness index was measured using a modified Diamond Porter framework and competitiveness index quantification. The model was conducted on 177 respondents from five districts in Kampar Regency.

Findings

The results indicated that it mapped 128,936.759 hectares area of ISFs’ oil palm plantation in the Kampar Regency. The results of plantation suitability showed that only 13.88% of plantations owned by ISFs were potential, 71.21% of them were in the developing category and 14.91% of plantations were non-potential. The competitiveness index showed there were only 7.91% of them at the developed competitive position, 73.45% at developing a competitive position and 18.64% at the least-developed position.

Practical implications

The paper includes implications for developing ISFs’ capacity building and best practice management for oil palm optimization, resulting in bargaining positions and social well-being.

Originality/value

The study had succeeded to visualize ISFs’ plantation area suitability and competitiveness at Kampar Regency, Riau Province. The model provides a brisk understanding and valuable information about ISFs’ conditions spatially. It offers specific outcomes and becomes important in optimize and develop the existing plantations at the right time and exact location.