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SMT201 - Geographic Information Systems for Urban Planning (G1) Project Report Specially Prepared for: Professor Kam Tin Seong Prepared by: Group 4 Kang Hui Yun [S9642400J] Lee Jung Jae [G1508022T] Wang Youjin [G1107574L] Date of Submission: 24th November 2019 Academic Year 2018/19 – Term 1 Table of Contents Abstract ...........................................................................................................................................2 1. Introduction ...............................................................................................................................2 2. Data Collection and Preprocessing..........................................................................................2 3. Methodology ..............................................................................................................................3 4. Change Detection ......................................................................................................................3 5. Master Plan Land Changes ......................................................................................................3 6. Population Changes and Forecast ...........................................................................................5 7. Analysis and Recommendations ..............................................................................................6 7.1 Data Preparation ....................................................................................................................7 7.2 Suitability Analysis for Schools ............................................................................................8 7.3 Suitability Analysis for Eldercare Centres ............................................................................9 7.4 Suitability Analysis for Shopping Mall ...............................................................................11 8. Limitations ...............................................................................................................................15 8.1 Scope of Project ..................................................................................................................15 8.2 Data Availability .................................................................................................................15 8.3 Time Constraint ...................................................................................................................15 References .....................................................................................................................................16 1 Abstract—The motivation of this project is to population and density respectively, out of the 55 harness the growing power and efficacy of planning areas in Singapore. There is generally an Geographic Information Systems (GIS) tools to increasing trend for population in Yishun but is derive useful insights in urban planning. especially high for senior citizens aged 65 and Specifically, GIS allows us to gain a higher above. With population trends and changes among understanding of our study area, and subsequently different age groups considered, it is essential to use take it a step further to ensure the safety and overall Geographic Information Systems tools to highlight welfare of the residents in Yishun. By making use and recommend for future development plans of of various data extracted from government sources Yishun area. such as SingStat and Data.gov, we aim to provide the uncovered insights and multiple analyses of the Yishun planning area in terms of the following: 1) Changes in the landscapes of Yishun and their purposes, 2) Population demographics and percentage changes, and lastly, 3) Analysis and recommendations in the interest of future planning across all age groups identified in Yishun. 1. INTRODUCTION FIGURE 1. Yishun planning area (with subzones) extracted using selection by expression 2. DATA COLLECTION AND PREPROCESSING With the growing availability of data, we have gathered our preliminary and secondary data from multiple sources, ranging from governmental sites to web articles and Wikipedia for usage in both QGIS and also to use the non-spatial information to FIGURE 1. better our initial understanding of Yishun. Yishun planning area in Singapore We have acquired the base layers (i.e. shapefiles of Situated at the north of Singapore, Yishun’s Planning Area/Subzone boundary, Master Yishun was previously known as Nee Plan 2008 & 2014 Land Use, and so forth) from Soon—the Mandarin romanisation of Data.gov2, which provides mainly spatial data that 林义顺 (Lim Nee Soon). He was a can be imported and visualised in many GIS successful industrialist, being a software. For aspatial (non-spatial) data such as rubber and pineapple magnate in the CSV (comma-separated values) files that serves to enhance the quality of the former, we have obtained region1. Overtime, several roads and ultimately them from SingStat3. Most of the time, these CSV the planning area we know today are named after files are manipulated both internally (in QGIS) and him. With a total of 9 different subzones existing externally to be joined with the relevant layers for within Yishun, the land area estimates around 21.24 further analyses. km². It ranks 8th, 7th, and 20th in terms of size, 2 In our acknowledgement, we have also used data The Master Plan proposed by the Singapore available in the course, which are obtained from government is paramount in guiding the country’s Land Transport DataMall4, OpenStreetMap5 and development of land and property with a long-term BBBike@Singapore6. outlook. Needless to say, it is one of the key factors in deciding where the country stands in the future, 3. METHODOLOGY for it holds a major stake in the welfare of our residents through the many ways that it can affect To achieve the final product in QGIS, various tools the different aspects of life. For example, proper and features are used and leveraged on. In fact, land management can change how people live— many of the layers went through data manipulation whether they stay in a HDB flat or condominium, if and several iterations of spatial preprocessing their children can enrol in a public school, and so before the desired output is produced for human on. Undoubtedly, it will also affect Singapore’s analysis. As the number of tools and functions used tourism industry and naturally the economy as a are almost boundless, we will name a few to whole. showcase the ability of QGIS: For vector layers: 1. Clip 2. Buffer 3. Difference 4. Intersection 5. Multipart to Singleparts 6. Geocoding For raster layers: 1. Rasterize 2. Raster Calculator 3. Proximity (Raster Distance) 4. Clip Raster with Polygon 5. Iso-Area as Contours (from Layer) Others: 1. Virtual layers FIGURE 2. 2. Structured Query Language (SQL) Depiction of boundary changes in Yishun between 3. Joins (with other layers) 2008 and 2014 Figure 2 illustrates the change in Yishun’s 4. CHANGE DETECTION boundary between Master Plan 2008 and 2014. We One of our first objectives in the Master Plan study observe a reduction of land from the north-east is to identify the changes over the years that has direction where majority of the area diminishes took place in Yishun. The change detection will from the east side. Subzones affected are namely cover the fundamental differential observation of Northland, Yishun East and Lower Seletar. the land (including land use), as well as other Another crucial aspect of land changes lies in the aspects such as population and demographics. We allocation of land use. As mentioned before, and will dive into the individual analysis in each section with the introduction of Master Plan, Singapore below. It is also important to note that the various aims to construct itself as a sustainable city-state by analyses provided in the subsequent sections balancing economic, social and environmental pertains to the changes between 2008 and 2014, factors7. This is made possible with long-term unless otherwise stated. monitoring of the country’s growth which allows 5. MASTER PLAN LAND CHANGES the government to carry out careful assignment of land use, making sure its limited land is optimised 3 for the right purposes. the availability of data manipulation through the popular Structured Query Language (SQL) used in databases today. In order to derive accurate figures of land use, we have made use of such SQL in the creation of a virtual layer: SELECT "LU_DESC", COUNT(*), SUM(SHAPE_Area) FROM LandUse_08_Yishun GROUP BY "LU_DESC" The block of SQL above is used to aggregate the land use information of Yishun in Master Plan 2008. “LU_DESC” specifies the category of land use—e.g. Business 1, while COUNT(*) tells us the number of occurrences and SUM(SHAPE_Area) adds up the area under each category, which is enabled by the GROUP BY statement. Upon doing the same for Master Plan 2014, we exported the data into an Excel spreadsheet for further analysis using its native features such as filtering, sorting and generation of charts. Land Use Change in Area (m2) Reserve Site 477,850 Park 150,698 Road 81,174 TABLE 1. Top 3 categories with highest increase in size Land Use Change in Area (m2) Sports & Recreation -1,087,953 Waterbody -287,940 Residential -276,628 FIGURE 3. TABLE 2. Land use of Yishun in Master Plan 2008 Top 3 categories with highest decrease in size As it is rather difficult to tell the differences purely Table 1 and 2 highlight the top 3 categories of land based on a side-by-side map comparison, this paper use that saw the most change in area in terms of will highlight the major changes of land use increment and decrement respectively. between the two years in textual and tabular forms. There are also certain interesting observation from To at least give an overview of Yishun’s land use, the change in land use. Intuitively, one would Figure 3 shows the landscape of the planning area foresee an expansion in land area whenever the in the 2008 Master Plan with various purposes number of occurrences (i.e. count) for the land use attributed to identified plots of land. Altogether, increases and vice versa. there are a total of 21 categories for land use. One of the basic yet powerful functions of QGIS is Land Use Change in Change in 4 Number Area (m2) Residential 81 -276,628 Waterbody 5 -287,940 Business 1 2 -66,123 Transport Facilities 0 -14,287 Special Use 0 -4,129 Place of Worship 0 -2,628 Reserve Site -2 477,850 TABLE 3. Land use with contrasting figures FIGURE 4. However, from Table 3 we can deduce otherwise. Percentage change of the Young (0 - 24) Although the number of residential lands increased population between 2008 and 2014 by a whopping count of 81, its total land area As shown from the figure above, there is a decrease decreased by 276,628m2. On the other hand, the in the young population in 5 out of 9 subzones of number of reserve sites had decreased by 2 while its Yishun which are adjacent to each other. The area increased relatively drastically, measuring at biggest decrease in terms of numbers took place in 477,850m2. On a side note (not included in the Yishun West with a drop of approximately 3,010 table), Residential with Commercial at 1st Storey residents. All in all, the young group has seen a saw an increase of 5m2 albeit there was no change decrease by approximately 5,000 residents in the in its number of land. Nonetheless, the overall period of 2008 to 2014. pattern remains intuitive—an increase land area follows the increase in number of land use. 13 out of the 21 categories account for such cases. 6. POPULATION CHANGES AND FORECAST After change detection of land use between 2008 and 2014, the next step was to import Yishun population CSV files (both 2008 & 2014) into QGIS and join them to Yishun shapefile maps. For our analysis, we have classified the population data into three different groups: Young (0 - 24), Economic Active (25 - 64), and Aged (65 and above). To calculate the population percentage change between the two years, the following Figure 5. formula was used: Percentage change of the Economic Active (25 - 64) population between 2008 and 2014 (population 2014 - population 2008) / population 2008 Despite an overall population increase throughout the subzones for the Economic Active group, it is relatively minimal except for Yishun East. There was an approximately 46% (~8,550 residents) increase for the subzone. Although there does not exist a clear-cut justification for such phenomenon, one important factor that such happening can be attributed to is that almost all business areas are heavily concentrated in Northland, making Yishun 5 West a fairly compatible place for the Economic Percentage change of the Aged (65 & above) Active group population to reside. population between 2008 and 2014 The Aged group showed the largest percentage change among the three population groups in Yishun. This means that the darker marked subzones saw a sudden jump of headcounts, of up to about 100% (which means double the original count) compared to the other two groups, with a maximum percentage change capped at 50% (increase of half the population). Figure 6. Employment in Yishun by industry (2010) Figure 9. Yishun’s population trend and forecast (2001 - 2024) Figure 7. Employment in Yishun by industry (2015) To summarise the population trend and to predict the future growth for the three age groups of To gain further insights on the Economic Active Yishun, we have extracted the population census group of Yishun, we have extracted the data of Singapore from SingStat and used the employment census data for 2010 and 2015 from appropriate trendlines in Excel to derive our SingStat and created two pie charts for each year. forecasts. From the illustration (Figure 9), it can be From both years, we can see that the top 4 industries deduced that the population growth for the are: Wholesale & Retail Trade, Manufacturing, Economic Active and Aged groups will continue to Public Administration & Education, and increase, whereas the Young group will gradually Transportation & Storage. decline over time. Overall, the total population of Yishun shall increase steadily, hitting a foreseeable figure of 226,009 residents by 2024. 7. ANALYSIS AND RECOMMENDATIONS Based on the data collected and the forecasted demand of the future population, we built a descriptive analysis model to better understand the accessibility of the three age groups to different facilities—mainly to provide the key social needs, namely, schools for the Young, shopping malls for the Economic Active and eldercare centres for the Figure 8. Aged. The output of the model is used as referential 6 layer, and is further considered with other factors to do not state their types, we have to resort to derive the final suitability land lot map for each a close alternative—clip the building layer case. to the residential area indicated in the land use dataset, which we treat as the actual Regardless of age groups, accessibility is an places of residence. important factor and is a top priority in many cases, especially for a fast-paced country like Singapore. For instance, eldercare centres and schools should be easily accessible through the use of public transportation, which are the various bus stops located in the planning area. We also take into consideration the other important social factors in building the new public facilities. However, the importance of each of them is Figure 10. subjective hence, differs from person to person. Buildings (filtered vs. unfiltered) Lastly, the creation of the model is based on the 2) Roads: the road data is used for the purpose understanding and knowledge we obtained from of avoiding heavy traffic. This is various public datasets, hence it can be particularly true in the case of the site oversimplified and may contain the team’s selections of eldercare centres, as we think assumptions whenever there is a lack of that safety is one of the priorities of the information. Nevertheless, these models should Aged. Hence, we extracted the road types: serve to provide a good insight of the happenings in primary, primary link, secondary, the Yishun planning area. secondary link, motorway and motorway link. These are the roads we deem to be 7.1 DATA PREPARATION dangerous with high risk of accidents due to the nature of the roads. Some SHP and KML layers from Data.gov are not up to date, including information on bus stops, food courts and markets. Hence, in the data preparation stage, we cross-reference the data obtained from Data.gov with Google Map8 and OpenStreetMap to achieve the highest possible accuracy in our analyses. In addition, manual geocoding is involved in the preparation. We also tidy up the raw datasets in the way that suit the analysis we carry out in each given scenario: 1) Buildings: in the land suitability analysis, Figure 11. we are only concerned about the Roads identified with heavy traffic residential/business properties whereas buildings used for other purposes are not 3) Residential area: As mentioned before, we taken into consideration. This is because wish for better accessibility for the Young we aim to conduct proximity analysis to and Aged group so that they could reach the ensure high accessibility to the different target locations safely and in a short age groups. In other words, we want amount of time. The selected areas are that schools and eldercare centres to be built of residential, commercial & residential near the vicinity of the homes, and the mall and residential with commercial at 1st to be close to the workplaces. However, the storey (from land use data). challenge being many building data points 7 Distance to bus stops and residential buildings are used as the base of the binary model. a. It should be near to the residential areas b. It should be close to the bus stops 7.2.1 BUS STOPS We first rasterized the bus stop vector layer to serve as an input for the next step. The proximity map is then created based on these raster locations of bus stops, taking them as nodes. Students mainly travel by bus as a means of daily Figure 12. commute. Hence, a walking-friendly distance from Land areas used for residential purposes school to the bus stops is strongly desired. 4) Point-of-interest (POI): A layer we call Category Proximity Range (m) social POIs is used in the suitability analysis for shopping mall. Data is Accessible Less than or equal to 250 obtained from OpenStreetMap. However, Poor Accessibility Greater than 250 in order to get the accurate POIs in the context of Economic Active group, we TABLE 4. manually filtered out the POIs that have Accessibility table of schools and bus stops stronger relevance to the population’s lifestyle including food centres, cinemas and hawker centres. Figure 13. Social POIs (filtered vs. unfiltered) Figure 14. Bus stops (reclassified) 7.2 SUITABILITY ANALYSIS FOR SCHOOLS 7.2.2 RESIDENTIAL AREA From our population prediction of Yishun, the Young group is forecasted to have a declining As parents mostly prefer their children to enrol in trend, overall. However, there are 2 subzones that neighbourhood schools and that Singapore is a defy this general trend based on the observation of small country, students tend to return to their abode our population analysis. After analysing the at the end of the day. This is unlike situations where locations and number of educational institutions in bigger countries have poorer and/or complicated Yishun, we proposed to have 2 more schools built accessibility. In this context, it is not uncommon for in such subzones. We also recommend building the students in such countries to move out of their schools within an accessible range to the public house to stay in a nearby dormitory during the library. period of schooling. For this, we propose to have schools built near residential areas which also 8 makes it easier for parents to walk their young Network analysis of public library children to school before heading to work. Finally, the location for the new schools were Category Proximity Range (m) selected in the subzone that is forecasted to have a significant increase in the young population with Accessible Less or equal to 250 years to come. Poor Accessibility Greater than 250 TABLE 5. Accessibility table of schools and residential areas Figure 17. Suitability map showing 2 ideal locations for building schools 7.3 SUITABILITY ANALYSIS FOR Figure 15. ELDERCARE CENTRES Residential buildings (reclassified) Previously, we have identified an overall increase Besides the factors considered in the binary map, in the Aged population. In addition, there is also a we also refer to other significant benchmarks that lack of eldercare centres in the west and south make up an ideal school location, such as the region of Yishun. We believe that the ageing availability and proximity to a public library. We population will be an ongoing event based on carried out the service area analysis using Iso-Area history and the current situation in Singapore. Map from QNEAT3, an external plugin is used for Therefore, it is safe to say that the demand for such advanced network analysis. a facility will soar. Given this, we have proposed to build another 3 eldercare centres in the planning area. However, due to the lack of information, we have made an assumption that the number of eldercare centres suffice for the Aged population in the latest year, and do our extrapolation forecasting to derive the final number of 3. 7.3.1 BUS STOPS Based on statistics, a majority of the elderly travel around short distance trips by bus9. Hence, the proximity to bus stops in the planning area is key to determine the most suitable location for new eldercare centres. Category Proximity Range (m) Figure 16. 9 Accessible Less than or equal to 250 Poor Accessibility Greater than 250 TABLE 6. Accessibility table of eldercare centres and bus stops Figure 19. Natural features (reclassified) 7.3.3 SLOPE From the initial slope analysis of the whole planning area, we found out many roads within residential area in Yishun have steep slopes, which Figure 18. is not desired as the elderly should avoid being put Bus stop (reclassified) in danger. With that said, the new eldercare centres 7.3.2 NATURE proposed should be further away from the steep areas/roads. The current 3 eldercare centres in Yishun are densely located in the east region and are far away Category Slope Steepness (degree) from the rich natural locations of Yishun. We believe it is crucial for the eldercare centres to be Elderly-friendly Less than or equal to 15 close to nature as various studies demonstrated the Not friendly Greater than 15 positive impact that nature can bring to human being’s happiness index. Hence, as an improvement TABLE 8. to the current eldercare infrastructure in Yishun, we Classification of slope proposed to have the new facilities built within a good distance to the natural features. We also believe that the previous generation favours such places as it suits their daily interests and lifestyle. Category Proximity Range (m) Accessible Less than or equal to 250 Poor Accessibility Greater than 250 TABLE 7. Accessibility table of eldercare centres and natural features Figure 20. Slope analysis (reclassified) 10 Putting the reclassified output of all three decision the new construction project. As shown below, factors together, we derived the below binary there exists a subzone on the upper west area that suitability map for eldercare centres. has a high count of elderly members but have not had any eldercare facilities present. Figure 21. Binary suitability map for eldercare centres Figure 23. Population count of the Aged group As illustrated above, the white areas represent the suitable land lots. On top of that, we also want to Combining the binary suitability analysis output make sure that the new centre is within a ‘safe’ area map together with other important factors, we coverage to the public hospital in the event of an derived the final map. Our proposal to build 3 emergency. eldercare centres is shown in the figure below. Hence, we did a network analysis on the public hospital in Yishun before recommending the final locations of the eldercare centres. Each circle represents 500 meters travel via the roads indicated based on road section line. Figure 24. Suitability map showing 3 ideal locations for building eldercare centres 7.4 SUITABILITY ANALYSIS FOR SHOPPING MALL Figure 22. Given the future development for Yishun as stated Service area analysis for hospital in the LTA development paper for Yishun in 2018, more business opportunities will be created in the In addition, we applied population study by upcoming years. On top of this, we also identified subzone to find out the area that is most suitable for that there is a significant increase of the Economic 11 Active age group in the north and east region of the Yishun. Hence, we proposed to build a new shopping mall since there is currently only one prominent shopping mall serving the entire local residence. Three decision factors were used in the derivation of the suitability map. Naturally, these factors have a differing importance when considering where to build the mall. Hence, unlike the previous analyses performed for schools and eldercare centres, we used a multi-factor analysis method with the help of Analytic Hierarchy Process (AHP). The common practice or rather methodology we Figure 26. applied in our suitability analysis is the rasterization Proximity to business area (workplaces) of the initial vector layer, after which we performed proximity calculation to show the closeness of all features in Yishun to the identified features of interests. As shown in the map, the darker parts depict higher proximity to the features of interest (social POIs, public transport, and business areas). Figure 27. Proximity to social POIs Upon completion of the proximity analyses done on the 3 selected features of interest, we next quantified the suitability by giving them a score that we can comprehend. Hence, applying the Figure 25. standardisation technique frequently used in Proximity to MRT statistics, we have employed the Max-Min method to standardise the above analysis results: Standardised Results = [Proximity to Features - Min(Proximity to Features)] / [Max(Proximity to Features) - Min(Proximity to Features)] The calculation is done using Raster Calculator in the processing tool. The x value will be the attribute available in the selection. We then manually extracted the max/min obtained from the proximity step. The final standardised results ranged from 0-1 and 12 we categorized them into 10 classes to give a clearer understanding of the score distribution performance. Figure 28. Proximity to social POIs (standardised) Figure 31. Social POIs map (standardised) 7.4.2 PUBLIC TRANSPORT Given the money and time that are needed to put in the shopping mall construction, it’s crucial to have the new shopping mall built near to the MRT station, ensuring easy access for public - especially to people living outside of Yishun. Given the current MRT stations in Yishun, we carry out the proximity analysis and standardise the value using Figure 29. raster calculator. Proximity to MRTs (standardised) Figure 30. Proximity to business area/buildings (standardised) Figure 32. 7.4.1 SOCIAL POIs MRT accessibility map (standardised) One of the key factors we need to consider in the 7.4.3 BUSINESS AREA analysis was the existing social point of interests for this age group (restaurants, food courts, cinema, As the population in discussion is the Economic etc). The shopping mall should be built near these Active group, our focus will be on the business area places to shorten the movement distance required of Yishun where the offices are designated. We for this age group. 13 propose to make it accessible for people working in the area to travel to the new shopping mall. Figure 35. AHP weightage and consistency check Figure 33. Business area accessibility map (standardised) Putting three decision factors together, we concluded that social factor is of utmost importance to the new shopping mall site selection, followed by accessibility factor 1 which is the distance to public transport, namely the MRT station, and then the distance to business area(their workplace). Hence, in the AHP table, we assign the below scores to each decision factor to derive their weightage. Figure 36. Composite suitability analysis using raster calculator On top of the three main decision factors, similar to the suitability analysis for schools, we also applied the population distribution by subzone and the changes. The yellow dotted subzone as shown below is identified as the area as the most Figure 34. appropriate to host the new construction projection AHP (relative importance of the factors) given the growth of the economic active population being 42%. 14 planning areas too to balance out the facility distribution. For example, there are very few eldercare centres located in Sembawang despite its large ageing population. Hence, we might be able to build one in Yishun that can also serve the population living in Sembawang as well. 8.2 DATA AVAILABILITY From the census data, we have identified the general trend for each age group. However, due to the lack of up-to-date and certain demographic data specific to different categories, we were not able to achieve an even better analysis and projection. If a Figure 37. more specific and updated dataset can be provided, Population count of the Economic Active group we can potentially factor them into our analysis and provide better suggestions and recommendations Putting all the factors together, we then derived the that will best represent the status quo of the future. final suitability analysis map as below. We decided to build one shopping mall due to the complexity 8.3 TIME CONSTRAINT and cost involved in building a facility like that of There are more insightful analyses that can be done a shopping mall. to the land suitability for different age groups. Due to time constraints however, we only focus on a limited number of key factors. Figure 38. Suitability map showing the ideal location for building a shopping mall 8. LIMITATIONS 8.1 SCOPE OF PROJECT In the suitability analysis, we only focused on the Yishun planning area alone without taking its surrounding neighbours into consideration. This is due to the scope of our study which is narrowed down to only one planning area—Yishun. However, in order for the new construction project to be successful and beneficial to the people living in and out of Yishun, we need to look at adjacent 15 REFERENCES 1. Wikipedia, “Yishun”, Accessed November 24, 2019. https://en.wikipedia.org/wiki/Yishun 2. Data.gov.sg, (Datasets), Accessed November 2019. https://data.gov.sg/ 3. Singapore Department of Statistics, (Datasets), Accessed November 2019. https://www.singstat.gov.sg/ 4. Land Transport DataMall, (Datasets), Accessed November 2019. https://www.mytransport.sg/content/mytranspo rt/home/dataMall.html 5. OpenStreetMap, (Datasets), Accessed November 2019. https://www.openstreetmap.org/#map=12/1.36 17/103.8558&layers=T 6. 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