Spatial-temporal analysis using two-stage clustering and GIS-based MCDM to identify potential market regions

Promotion is essential in a competitive environment. Promotion to the right areas increases success and saves resources. However, due to Indonesia's vast territory and numerous regions of origin school, universities with student markets from all over the country must select target areas for promotion to meet their objectives and save resources. Unlike for-profit businesses, besides quantity factors, educational institutions need to consider student quality factors in selecting promotional locations. This study aims to conduct a data-driven spatio-temporal analysis to identify potential regions for university promotions targets. This study uses enrollment and academic data from one private university in Indonesia for the empirical study. In Geographic Information System (GIS) environment, the origin schools' locations were geocoded, and various thematic maps were analyzed. This study integrates two-stage clustering and GIS-based multi-criteria decision-making (MCDM) to identify potential market regions. A potential region is one that consistently sends many qualified students. First, time-series clustering is conducted to groups regencies/cities based on the enrolled students' patterns over time in the university. Subsequently, the origin schools' regencies/cities were clustered using the k-prototypes algorithm based on their time-series pattern category, the consistency in sending students, average cumulative grade point average (CGPA), and dropout (DO) rate. The clusters are scored using the sum weighting method. The highest valued cluster that consists of eight regencies and 18 cities that consistently contributed high quantity and quality students were selected as the priority regions. The proposed approach's results were compared to the Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods for evaluation. The proposed method can assist the university management in determining potential regions for promotion purposes

An intelligent mobile disaster alert system

Malaysia has experienced various disasters either natural or manmade disaster. One of the critical phases in Disaster Management System life cycle is response phase. In this phase, connectivity analysis such as a navigation service to help emergency rescue (ER) units reach at disaster area on time is necessary. Nowadays, commercial navigation system seems not appropriate to be used by ER units as they have different preferences. In addition, location information that is vital was not fully utilized in disaster management, especially in doing multi-task analysis. Thus, the real potential of GIS technology in managing spatial data including real-time (moving objects) data of ER units may influence the quality of the service. However, the services should be supported by a good data model. In order to eliminate inappropriate information, incomplete data, and overloaded information from Database Management System (DBMS) sent to the user, this paper will present the framework of integrated routing application for emergency response units embedded with context-aware

Open Spatiotemporal Data Warehouse For Agriculture Production Analytics

Business Intelligence (BI) technology with Extract, Transform, and Loading process, Data Warehouse, and OLAP have demonstrated the ability of information and knowledge generation for supporting decision making. In the last decade, the advancement of the Web 2.0 technology is improving the accessibility of web of data across the cloud. Linked Open Data, Linked Open Statistical Data, and Open Government Data is increasing massively, creating a more significant computer-recognizable data available for sharing. In agricultural production analytics, data resources with high availability and accessibility is a primary requirement. However, today’s data accessibility for production analytics is limited in the 2 or 3-stars open data format and rarely has attributes for spatiotemporal analytics. The new data warehouse concept has a new approach to combine the openness of data resources with mobility or spatiotemporal data in nature. This new approach could help the decision-makers to use external data to make a crucial decision more intuitive and flexible. This paper proposed the development of a spatiotemporal data warehouse with an integration process using service-oriented architecture and open data sources. The data sources are originating from the Village and Rural Area Information System (SIDeKa) that capture the agricultural production transaction in a daily manner. This paper also describes the way to spatiotemporal analytics for agricultural production using a new spatiotemporal data warehouse approach. The experiment results, by executing six relevant spatiotemporal query samples on DW with fact table contains 324096 tuples with temporal integer/float for each tuple, 4495 tuples of field dimension with geographic data as polygons, 80 tuples of village dimension, dozens of tuples of the district, subdistrict, province dimensions. The DW time dimension contains 3653 tuples representing a date for ten years, proved that this new approach has a convenient, simple model, and expressive performance for supporting executive to make decisions on agriculture production analytics based on spatiotemporal data. This research also underlines the prospects for scaling and nurturing the spatiotemporal data warehouse initiative

Space Subdivision For Indoor Navigation: A Systematic Literature Review

Along with the increasing demand for indoor navigation, many attempts were made to improve indoor navigation performance. Information about the room becomes important, because one of the characteristics of indoor navigation is the dynamic indoor conditions. Space subdivision is an effort made to make indoor navigation even more accurate. The purpose of this study is to create a systematic literature review (SLR) regarding the topic of space subdivision for indoor navigation which is based on a SLR method, previously defined research question. This study examines several previous works specifically in the field of space subdivision for indoor navigation with the SLR. This research is expected to be the basis for further research to improve the quality of indoor navigation based on space subdivision

Agriculture Spatiotemporal Business Intelligence using Open Data Integration

Business Intelligence is a technology for collecting, transforming, and presenting data for analysis as a tool for supporting decision making. Business Intelligence using Data Warehouse, Multidimensional data, and Online Analytical Processing (OLAP) has proven to be useful for obtaining information and knowledge relevant to the business. Nowadays the development of the internet with Web 2.0 model is increasing the availability of data over the internet. Linked Open Data (LOD), Open Data, and Open Government Data is constantly growing, producing a large amount of valuable data in the form of semi-structured data, flexible and machine-readable. Data sharing on agricultural production is one of the requirements for the best of analysis of agricultural production, but most of the data is still in the format of 2/3-stars open data and does not yet have spatial data that facilitates analysis based on spatial dimensions. The emerging open data concept makes the data warehouse more dynamic and can accommodate external data. Spatiotemporal support in open data also enables a more sophisticated analysis of data with spatial queries. This research develops tools to integrate agricultural data originating from the Village and Rural Area Information Systems (SIDeKa) that has open distributed data, a service-oriented approach, and spatiotemporal data. This paper also describes the design of business intelligence and multidimensional data for analysis and decision-making tools that enable spatiotemporal and non-spatial based analysis. This paper also highlights the opportunities for scaling and sustaining the initiative