Part 4 : Conclusion

4.0 Conclusion

In summary, this study aims to identify the possible use of GIS in integrating the cycling path network with places of interests (schools, public facilities and nature parks) to identify the issue of accessibility for cyclists and potential users in Tampines Town.

By leveraging on GIS environment, we are able to obtain a full area coverage and dynamic model that allow us to assess the connectivity and potential for improvement of the cycling paths network in Tampines Town.

Part 3 : Result and Discussion

3.0 Result and Discussion

3.1 Outcome

Suitability Map of New Cycling Path

The Suitability Map (shown above) generated from ArcMap shows the spatial analysis for the best area to have new cycling path to improve the connectivity of Tampines. The best area is indicated with dark green and the less desirable area with red.

3.2 Recommendation

From the analysis, it can be seen from the Suitability Map that the most suitable area for having a new cycling path is at a neighbourhood park near Tampines Avenue 5. 

Recommended location for new cycling path

After incorporating the site condition, it is found that a new bicycle path (indicated in orange on Suitability Map) could be built along Tampines Avenue 5. This could benefit the cyclists living nearby and improve connectivity to the rest of the region.

In addition, another cycling path (indicated in red) could be established next to the industrial cluster at Tampines Street 92. This could facilitate the existing working adults that commute via cycling and also encourage more working adults to adopt cycling as a way to commute.

3.3 Limitations

First, it is expected that the results may not accurately depicts the actual condition due to inadequate data that leads to a certain degree of bias. Assumptions will have to be made. For instance, site specific attributes such as gradient, traffic condition, type of pavement and demographic data is likely to influence the demand for bicyclist needs and choice of cycling routes.

Second, due to time constraints and difficulty in data collection especially sensitive data such as topography and etc from local authorities, the research team had to compromise our selection of site- from initial choice in Bukit Timah that has uneven topography to Tampines New Town which has relatively uniform topography.

As such, we were unable to incorporate the relevant factors into the model, which could have enhanced our findings and analyses. As a result, we had to propose a simpler model to meet our grading assessment.

3.4 Implications

By leveraging on the spatial capability of GIS, this study is able to identify suitable routes to improve the connectivity for cyclists with minimal extension of path. However, the desired deliverable of this study is to establish a path suitability model so as to assist the road planners to determine the appropriate routes to construct or simply convert existing pavements to designated cycling paths. It is not meant to replace the traditional methodology e.g. site survey.

Despite the fact that GIS is capable of incorporating spatially disaggregated data into the map to demonstrate certain patterns, the usefulness of the spatial analysis is still highly influenced by the quality of data. 

Continue to Part 4 : Conclusion

Part 2 : Research Methodology

2.0 Research Methodology

Based in graph theory, suitability evaluation study are usually implemented by using vector format GIS analysis, however, as the proposed model involves continuous surface, a raster environment is created as well. By doing it, it allows us to employ other analysis, with the flaws of compromising geometrical information accuracy. There are a total of 4 phases of the study, which includes:

1. Data Collection and Filtering
2. Data Integration
3. Visualization and Presentation
4. Suitability Analysis

2.1 Data Collection and Filtering

Relevant Map layers and Data sources were obtained from the reliable database from the authority, Singapore Land Authority. The key information encompasses variables such as Building, Park Connector, Road and Nature Park. 

As the raw information was compiled according to classification that is not appropriate for the project needs, filtering process is conducted on the Building Layer to extract and reclassify the building types according to place of interests. The following classification is employed:

Classification	Description	Label
School	All schools in Tampines Region. Includes Junior College and Polytechnic.	1
Public Facility	All public infrastructure and facility. E.g. Swimming Complex, Community Club, Stadium, MRT Station, Bus Interchange, Regional Library.	2
Shopping	E.g. Tampines Mall, Tampines One, Century Square	3
Others	Other type of facility	0

Table : Classification of Layer

To facilitate data management, nominal scale is employed by replacing the names with numeric figures. In addition, the numbers were used to reduce the time required for manual data entry since there is no raw and relevant data available.

In view of the difficulty in collecting contour data (that is related to accessibility factor) and road element data such as traffic light (that is related to safety factor, in particular hazards), the selection of Tampines area is a deliberate move to leverage on relative flat surface and well maintained road condition of Tampines area, where accessibility and safety is less critical. 

Road Condition of Tampines Area (Source: Google Map)

However, as a caveat, where uneven terrain e.g. Bukit Timah Area is concerned, safety and accessibility shall be taken into consideration. In appropriate cases, Bicycle Compatibility Index (BCI) that computes comfort level based on roadway and traffic characteristics shall be established, in areas such as planning for designating bicycle routes as well as identifying bicycle facilities that may need improvement (D. Harkey et. al.,1998).

Location of bicycle path is the most important data for this analysis. As it was not readily available, a new ‘Bicycle Path’ Layer was created in ArcMap. This ‘Bicycle Path’ Layer was constructed based on a ‘Tampines Cycling Path Network’ guide published by the Land Transport Authority. In addition, all ‘future cycling paths’ indicated were also included in the new layer for analysis. 

2.2 Data Integration

The four Data Layers - Bicycle Path, School, Public Facility and Nature Park were identified as important inputs for the analysis.

Location of bicycle path is required to avoid having new bicycle path close to existing ones. School and Public Facility were chosen as these places are node of activities and residents in the region are likely to visit them frequently for education and leisure purpose. Nature Park is seen to be a better environment for cyclist as compared with roads and built-up areas.

The Flow Chart shown below was used to integrate the data layers for analysis. Firstly, Euclidean (straight-line) Distance tool was used in ArcMap ModelBuilder to calculate the distances away from each of the four inputs.

Next, the distances obtained in the form of raster datasets were reclassified into 10 equal intervals using the Reclassify Tool. New ordinal values (1 to 10) were assigned to each interval according to their suitability for analysis. The reclassifications are as follow:

Raster Dataset	Low Suitability	High Suitability
Distance to Bicycle Path	Nearest = 1	Furthest = 10
Distance to School	Furthest = 1	Nearest = 10
Distance to Public Facility	Furthest = 1	Nearest = 10
Distance to Nature Park	Furthest = 1	Nearest = 10

Reclassification of distance to ordinal values

In order to avoid having new cycling path from being too close to the existing ones, a value of 10 was given to the furthest distance/interval from the location of Bicycle Path for its high suitability. A value of 10 was assigned to the nearest distance/interval from School, Public Facility and Nature Park. The rationale is that cycling path should be near to these attributes for the convenience of cyclists.

2.3 Visualization and Presentation

Having determined the type of land use that is likely to have an effect, certain decision parameters e.g. the constraints need to make so as to derive the results. Hence, GIS, as a spatial data enabled platform, provides the convenience and ease in manipulating the data, in particularly the visualization and presentation of the outcome. 

However, prior to it, GIS operations need to be applied. In this case, the Weighted Overlay tool was used to combine the New Values of the four raster datasets. The percentage of influence given to each reclassified datasets is as follow:

Raster Datasets	% Influence
Reclassified distance to bicycle path	40
Reclassified distance to school	20
Reclassified distance to public facility	20
Reclassified distance to nature park	20

Reclassification of distance to influence level

A higher percentage of influence (40%) was given to the ‘Reclassified distance to bicycle path’ as it is deemed to reduce duplication efforts and minimize the complexity of the bicycle network, the provision of space allowance is necessary by distancing the suitable area for new cycling path from the existing ones. 

Next, we take into account that frequent users of public facility and nature park are likely to be health conscious and thus more likely to use bicycle as the transportation, if not already have. Lastly, one of the key objectives of the study is to facilitate and cultivate the young in adopting cycling and thus by locating bicycle route near to school is important as well. Hence for those considerations, 20% influences were evenly distributed.

2.4 Analysis

The derived result would be a distribution of values throughout the Tampines region. This values which can be represented with different colour would be used to understand and identify the suitability of area for the proposed new cycling path.

Continue to Part 3 : Result and Discussion

Part 1 : Introduction to Project

1.0 Introduction

Tampines is the first new town in Singapore to adopt a dedicated cycling path under the National Cycling Plan. The National Cycling Plan is a government initiative by the Land Transport Authority of Singapore to promote safe cycling by segregating pedestrians from cyclists.

Referring to the National Cycling Plan, there are six other towns which include Yishun, Sembawang, Taman Jurong, Pasir Ris, Changi-Simei and Bedok. Marina Bay will construct such cycling paths to facilitate travelling to reach main transport nodes and key local amenities. 

According to AsiaOne News, the Senior Parlimentary Secretary for Transport and Community Development, Youth and Sports, Mr Teo Ser Luck commented that the provision of cycling paths was aimed to encourage the residents to pick up cycling as a mode of transport as to curb rising oil prices and vehicle congestion issue.

Recently, Tampines Bicycle Town has been launched in anticipation of the growing population of cycling enthusiasts. This is also in tandem with the government’s stand for a green environment for people to live in. However, we find that the connectivity within some parts of Tampines is not well defined.

                  Tampines Cycling Path (Source: Land Transport Authority)

1.1 Objective

We aim to improve the connectivity of the bicycle path in Tampines, which can be the catalyst to encourage higher participation from the residents. Spatial analysis is used to create a suitability map in order to identify new suitable cycling paths in the future.

Next, we will be discussing about the benefits of bicycling, importance of connectivity within a region and economic benefits of cycling path.

1.2 Literature Review

Benefits of Bicycling and Walking

As petrol prices hover around $2 per litre, and COE prices reaching new heights, bicycling around the community has become an important transportation alternative. People who choose to ride rather than drive are typically replacing short drives, which ironically contributes high amounts of pollutant emissions. Moreover, bicycling move people effectively from one place to another without adverse impacts to the environment.

As the car population has increased significantly over the years, whereby contributing to the higher COE prices over the years, it has also resulted in heavily congested transportation networks. On the contrary, using bicycles can be seen as a good solution to ease traffic congestion and packed public transportation systems as bicycles require less infrastructure needs and space as compared to automobile uses. Improvements to the existing infrastructure can contribute to safer conditions for the cyclist as well as other stakeholders. It can also ease congestion around schools, where parents tend to pick up their children after school or send their children to school.

The use of bicycling also helps to foster a sense of neighbourhood togetherness and community. This is a result of the residents cycling within their neighbourhoods, which allows them to interact with their surrounding community. However, the utility level experienced by each individual may differ according to their expectation.

Importance of Connectivity

According to the Victoria Transport Policy Institute (VTPI), connectivity refers to the directness of links and the density of connections in path or road network. A path network is considered well-connected where it has many short links, intersections, and minimal dead-ends which enable the most efficient travelling from one point to the other. This reason being that when the level of connectivity increases, it shortens travel distances and provides more route options, thus enabling more direct travel between destinations, making the transportation system more accessible. (Online TDM Encyclopedia,2005)

Moreover, it can be a hassle to cycle if the connectivity around a region is poor. The lack of connectivity will discourage people from bicycling as it will increase the travelling distance from one point to another.

A well connected path network has many short linkages, abundant intersections and minimal cul-de-sacs. Research showed connectivity has a negative relationship with travel distances and a positive relationship with route options (Kulash, Anglin, Marks, 1990).

Based on our literature reviews, the main focus of this study is to develop a suitability model to provide the road planners with a quantitative tool, keeping in mind of the relationship between social facility with bicycle demand as a key variable for area development and planning. In essence, the model incorporates a weighting procedure that evaluates the appropriate paths in conjunction with land use pattern, which is in line with the National Cycling Plan for the Tampines New Town to be identified as the pilot town for the improved cycling path project.

Economic Benefits of Cycling Path

The construction of walking and cycling paths allows more residents to reduce automobile trips by adopting non-motorized trips such as walking and bicycling. According to the National bicycling and Walking Study (NBWS) final report, the provision of walking/ cycling paths allows the American public to save about 3 to 14 cents for every automobile kilometer displaced by walking and bicycling. The cost savings are derived from the reduction of carbon emission, oil costs, and costs of congestion.

Furthermore, although there is no conclusive evidence suggesting the provision of cycling path will induce more cyclists, it is expected to provide several benefits from an urban planning perspective. Other than economic consideration, it will increase community interaction and reduce neighborhood segregation through improved connectivity, in a social context. Also, establishing more open space and enhancing greeneries with reduced noise and air pollution with the increase in number of cyclists.

Continue to Part 2 : Research Methodology 

Use GIS meaningfully....

The beauty of the competition is that  all teams are given the freedom to choose the kind of project that they wish to undertake, however, it also meant bigger responsibility.

It is because if without establishing objectives, decide mission deliverables and setting constraint it potentially meant that we are able to do anythings with GIS.

Agriculture firms could use GIS to manage their plantations.
Logistics firm coulde use it to plan their transportation routes.
Retail firm coulde use it to decide on the strategic location for new branches.
etc...

Poor planning spells failure. It is the kind of situation that happened to us in the initial weeks when we are considering fanciful ideas without taking account what is our forte, interest and how we can leverage on GIS to achieve the goals. Not surprisingly, those proposals are deemed to be inappropriate by our supervisor.

Lesson learnt, we research on various applications of GIS and found the suitable use that also our course of interest-urban planning that we will attempt to share the outcome in our blog at later time.

The link between spatial data and GIS

Location-specific attributes e.g. infrastructure play an important role in real estate application like property value analysis and site selection. Geographic Information System (GIS), a platform that links the computerized mapping, spatial data and database management system, is thus extremely useful for real estate applications.

Why Spatial Data Quality

However, the most fundamental steps which is both time consuming and costly (to certain extent), for real estate practitioners that keen to initiate a GIS is creating a database. In order to provide reliable and relevant output, the precise and accurate integration of spatial data is crucial to real estate practitioners. 


As such, understanding and checking the spatial data quality (SDQ) is important for real estate projects uses GIS even prior to acquiring the data and after data development.

The consequences of poor handling in Spatial Data Quality

The common mistakes that one can make in implementing a GIS are data collection and compilation errors, data processing errors and data usage errors. The presence of errors would result in wrong analysis and misappropriate conclusions, are sometimes unavoidable. But it could be mitigated suitably if users are aware of the limitations by execute proper quality criteria e.g. accuracy versus completeness and leverage on available supports e.g. SG-SPACE to estimate the quality of data provided by a source with another source.      

Components of Spatial Data Quality (SDQ)

Next, for clarity, the author would attempt to illustrate SDQ in the real estate context. Generally, the components of SDQ include: (1) Lineage, (2) Positional accuracy, (3) Attribute accuracy, (4) Logical consistency & (5) Completeness.


Lineage

For (1), this is referred to the source and process used to derive the data e.g. surveying or cartography. In the example of surveying, considering the methods of derivation, the land surveyor might not be able to access to the area due to uncontrollable situations e.g. military area or the data might not be updated e.g. fails to capture new buildings. It could result in wrong assumptions made by real estate users in determining the highest and best use.


Positional accuracy & Attribute accuracy

For (2), this is the closeness of a feature to its true position in an appropriate coordinate system. It could be expressed as 95% of the land parcel corner points in a parcel layer are ± 3m of their true location. In this case, it is dependable of (1), as the scale and resolution of the source are the determinants of (2). In other words, if the source e.g. maps are less precise or lack of standardized scale, the GIS layer is almost always less accurate to pinpoint the actual location. From economic perspectives, it could result in huge bid price difference, said, between the distances to CBD e.g. 2km versus 20km in map dimension. 


While for (3), an attribute provide information includes the fact about the set of locations e.g. population or measurement e.g. elevation that are necessary for market/site studies. Typical errors occur include typo error, numerical error e.g. wrong values, categorical error e.g. replace land use type from “residential” to “industrial”. For the latter, the operator may be held negligent for developer’s loss.

Logical Consistency & Completeness

Next, (4) deals with the attribute rules for spatial data and logical rules of structure/framework, it also describes the compatibility of a datum with other data in a data set. There might be issues if there is inconsistency exists. For illustrations, if there is gaps or overlaps between census area, the population forecast will be invalid. 


To ensure the data quality, logical consistency checks such as metric incidence and incidence tests could be run. Lastly, (5) is a check to ascertain if there is any omission of relevant data. Taking retail study as an example, it is to check whether all shopping centers are captured in the database, including mixed development.

Work with Spatial Data Quality’s potentials and limitations

By understanding SDQ, users could sometimes prevent errors. Some methods include use default values (to prevent typo errors), required fields (to prevent omission) & manual check against sources. Importantly, operators should document the data quality in metadata that provide information such as the sources, last update, the purpose for the data. 


Such actions would often reveal the usefulness and flaws of the data, so as to allow future users to make informed decisions.  

Summary

In summary, for real estate users to successfully exploit and harness the GIS capability, would requires operators to balance between artificial intelligence e.g. auto computation and human judgment on GIS, an enabled platform.

Reference:

Spatial Information ClearingHouse (2004). Spatial Data Topics. Retrieved February 13, 2011, from http://maic.jmu.edu/sic/topics.htm

Barbara, P. (2007). Data Quality for GIS. Retrieved February 13, 2011, from http://ocw.tufts.edu/data/54/676129.pdf

Author: Lawrence Ler

What is GIS????

Like majority of the participants (perhaps not?), as a group of undergraduates that majoring in Real Estate in National University of Singapore, at first, we are not aware of th concept of GIS (even the meaning of GIS).

However, having took up the elective module-"RE2901- GIS in Real Estate", we were exposed to GIS, the use of GIS, the available mechanism and many more. Like other concepts or disciplines, there are many meanings attached to GIS as well.

GIS, the acronym stands for Geographic Information System. It is easy to obtain many explainations from the internet. In general, it is a system that make use of geographically reference data to integrates, share, analyze, edit and display geographic information. For instance, a map will provides many useful info about geographical condition for developers to look at, but a GIS will help to form market analysis report to aid in decision making

Then, as real estate students, we will think then why we need to learn some many other things such as ArcGIS, spatial data, non-spatial data and metadata  (Afterall, we are not computing or IT students that are well verse in data management and programming). Through the competition, the knowledges acquired along the way broaden our understanding, GIS is not only about geography, it also encompasses database management, cartography, statistics and logical thinking.

We will attempt to share what we learn, find and enjoy during the next few weeks..