Difference between revisions of "IS428 2016 17T1 Team Valor"
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Revision as of 01:51, 10 October 2016
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| Project Details | Project Poster | Project Application | Research Paper |
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The problem everyone faces in purchasing a new property is the multitude of factors that influence our decision. The most common means to compare property choices on the layman level would be to input the various property choices and their attributes onto a spreadsheet. Additionally, there are also several externalities that are subtly preferred e.g. preferred close proximity to supermarket or religious places of worship.
However, considering the time, money and effort invested in procuring a place they can call home, we believe that home buyers should be given the opportunity to carry out a more thorough analysis on their choices. Owing to the spatial nature of this decision-making process, our web application BOICHU, aims to provide users a better visualization of amenities surrounding their property choices. In order to help decision-makers make more informed decisions, our group has developed an application that allows them to do so with the reference to both spatial and non-spatial data. With regards to this unique integration, we have incorporated the usage of Analytic Hierarchy Process (AHP) where they can prioritize the importance of different criterions.
Purpose-Built Offices part of property investment portfolio
With increased interest in purpose-built offices in Malaysia, investors identify the need to leverage on geospatial tools to make better value judgments in investments. In the context of PBOs, properties situated within the Kuala Lumpur Golden Triangle (KLGT) have higher rental values. In the study conducted, five purpose-built offices (PBO) were selected for analysis and primary data was gathered via a questionnaire for the building occupants and secondary data from Geographic Information Systems (GIS) analysis. The first part of the study required survey respondents to prioritize the importance of individual locational characteristics that affect their choice of a PBO. After which, network analysis (to determine distance) is applied to the dataset in order to assign scores for each locational characteristic for each PBO. With the weightages derived from part one multiplied to the scores from part two, a final result was returned for each PBO, with the highest score (termed 'Locational Quality Index') indicating the most suitable PBO for investments.
Tsunami-prone Area Categorization
Owing to the fact that Japan in located near the Pacific of Ring of Fire, this endevaour to conduct pre-planning response and post-disaster recovery procedures in the Ofunato City of Japan focuses on the usage of GIS and AHP. The spatial analysis included the study of factors (also known as criterions in AHP) affecting a grid’s vulnerability and they are land elevation, slope, coastline distance, vegetation density. With the gathered data, the investigators then proceed to apply AHP to prioritize the influence that each of these criterions have in determining the overall vulnerability of a grid, which returns a weightage for each criterion. The derived weighted overlay is then applied to the spatial data (of the five criterions) to generate an overall score which indicates the vulnerability of that respective grid, therein serving as a basis for the formulation of recovery procedures specific to that grid.
GIS-based Land Suitability Analysis Using AHP for Public Parks Planning in Larkana City
The planning of public parks in Larkana City of Pakistan was carried out by integrating a Geographic Information System (GIS) alongside the use of the Analytic Hierarchy Process framework to evaluate multiple criteria. Specifically, the decision support system software was that of ExpertChoice, which was used to determine the weights based on three alternative scenarios — land availability, land value and population density. These three scenarios were performed in raster format and analyzed in ArcGIS, a popular GIS software. The three scenarios were then combined to determine the potential land space that best fit the criteria involved in the decision-making process. This particular case in point highlights the tedium involved in integrating AHP and GIS into the decision making process — they exist as separate applications and the information from one has to be ported to the other by manual means, therein underscoring the prevalent silo-ed nature of such applications.
- Data was crawled from the Singapore Sports Council site prior to its revamp, and the SingHealth site to obtain the addresses of the stadiums and GP clinics respectively using scrapy.
- Subsequently, the geocoding of these landmarks were done using by means of a node.js geocoder.
- The geocoded locations were then loaded into QGIS and thereafter saved as GeoJSON for use with the leaflet libraries.
There are only a handful of applications which incorporate the analysis of spatial and non-spatial data. Geospatial Analytics has been a very common and handy tool when it comes to evaluating a selected location, but is limited to only spatial data. However, when it comes to assessing locational characteristics, we firmly believe that non-spatial characteristics such as land parcel valuation or transaction prices should also be taken into consideration. Hence, in order to allow for better geographical analysis, our group is convinced that the incorporation of Geospatial Analytics and AHP will prove to be insightful.
Devised some 30 years ago, Analytic Hierarchy Process (AHP) was introduced as a multi-criterion decision making tool. It has since been implemented in various contexts, from deciding which primary school to send children to, to procurement and strategic sourcing in organisations. AHP captures its user's consideration for multiple criterions in decision making, and returns a priority index (also known as priority weights) ranking the importance of each criterion to the user. Based on the generated weights for each criterion, the user will proceed to compare his or her choices against one another in terms of each criterion. Similarly, a priority index is generated but this time round, it shows the ranking of choices in terms of which criterions the user preferred best.
Taking it to the next level, AHP can also be used with multiple inputs from different users. There are various instances where decisions involve more than just one stakeholder. For example, choosing a location to set up a new school; stakeholders involved are such as SLA (who plans exactly the purpose for each state-owned land parcel), the potential school board, NEA (preservation of nature) etc. Also, in the context of our application, buying a new home is very commonly a decision made between two or more parties.
Users can toggle through the different thematic maps we have available on BOICHU to get a better sense of the distribution of amenities and of the property market.
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