Improving Efficiency in Data Collection for Urban Development Plans through Information & Communication Technology, Research paper presented in International Conference on Town Planning and Urban Management (ICTPUM2014)

1. Improving Efficiency in Data Collection for Urban
Development Plans through Information &
Communication Technology
Researchers
M.Q. Hussnain , G.A. Anjum , K. Wakil , Ranasinghe P.H.T.D
International Conference on Town Planning
and Urban Management (ICTPUM 2014)
University of Engineering and Technology, Lahore
September 2014
1

2. Contents
• Background
• Conventional Data Collection and Management
• Recent Trends in Data Collection and Management
• Research Design and Methodology
• Comparison on PAPI vs MDC
• Conclusions and Recommendations
2

3. Background
• Data collection has always been a key stage in
the preparation of development plans,
requiring considerable amount of time and
financial resources
• Primary and secondary data is generally
collected under different sectors including
– land uses, Housing, Transportation, socio-economic
conditions, Industry, Commerce,
Employment, community facilities and physical
infrastructure etc.
• Review of development plan preparation exercise in
past depicts that almost 60% of the total time goes in
data collection. For instance Rawalpindi Master Plan
3
Setting vision/ Scope
Preparation of Base Map
Baseline Data Collection
Situation Analysis
Results Presentation
Proposals Formulation
Public Participation
Decision Making
Plan Implementation
Monitoring and Feedback

4. Conventional Data Collection and Management
• Primary data collected for development plans mainly include
– Texts
– Numeric
– dates and time
– multiple choices
– Options
– pictures and locations
• Questions are structured on tools like performas, checklists and
questionnaires for using during observation surveys and interviews
• Traditionally researcher needs to different gadgets for capturing
different data types
• Paper and Pen Interviewing (PAPI)
4

5. Conventional Data Collection and Management
• The data so collected needs to be physically
stored and properly staked for entry into a
digital spreadsheet/ database for further
analysis
• Data entry often requires translation and
coding. After the data entry, process of data
cleaning and standardization of responses is
performed.
5

6. Recent Trends in Data Collection and Management
• Recent advancement in ICT has introduced the Mobile
Devices based Data Collection and management
system (MDC)
– an advanced form of Computer Assisted Personal
Interviewing (CAPI)
– The process of data collection and data entry is merged
• MDC is a structured process of systematic data
collection using mobile devices/ smart phones/
tablets. It commonly uses an online server to submit
the data.
6

7. Conceptual Stages in MDC
1. Form Design
2. Deploy Form on Server*
3. Deploy form on device
4. Collect Data
5. Send data to server*
6. Analyze data
7. Feedback to enumerators
*these are optional
7

8. Objectives of the Research
This research presents
• the utility of mobile based data collection for development
plans preparation
• and offers a comparative analysis of conventional PAPI and
ICT based MDC by
– quantifying the expected cost, time and quality improvements.
8

9. Research Methodology
• A randomized survey experiment among 100 households
– Using Socio-economic and Housing Survey.
Randomly selected group
of 50 HHs, Exposed to PAPI
Randomly selected group of
50 HHs, Exposed to MDC
• Graduate Enumerators of similar educational background, contextual
understanding, enumeration skills and experience were hired
• Out of selected six enumerators, three for each group, were randomly
assigned for these two household groups. Same survey tool was used in both
approaches.
9

10. Research Methodology
Composition of Survey Questionnaire
• The socio-economic and housing survey questionnaire used for this
survey contained 49 questions
– Open ended questions = 17
• Text answers = 6
• Numeric answers = 11
– Close ended questions = 32
• Yes/ No = 10
• Multiple Choice = 6
• Choose one = 16
10

11. Research Methodology
• Survey with PAPI Approach
– In addition to the collection of data, enumerators
noted down additional metadata information
including:
• Time taken to complete the survey
• Location of survey, GPS coordinates
• Survey with MDC
• Analysis
– Assess three key parameters, time, cost and data
quality.
– In case of data obtained from PAPI, each filled
questioner was checked against the database, to
identify data quality issues.
11

12. Comparison in terms of Time
44%
12
PAPI MDC
Field Enumeration Time (12.5h) Field Enumeration Time (9.2j)
Data Entry Time (3.8h)
Data Spot Checks time (0.2h)
Total Time (16.3h) Total Time (9.2h)

13. Comparison in terms of Cost
36%
13
PAPI (Rs.) MDC (Rs.)
Printing Cost 150
Enumeration Cost 1562 Enumeration Cost 1145
Data Entry Cost 500
Cost of Computers 500 Cost of Android devices 600
Total cost 2712 Total cost 1745

14. Comparison in terms of Quality
• In case of PAPI, rechecking of entered data against survey
questionnaires identified 21 typo errors, out of total 2450 entries.
• This turns out to be 0.85% errors during data entry.
• On the other hand, mobile based data collection involved no data entry
errors since data is coming straight from field to analysis table without
an additional tier of data entry.
14

15. Other Qualitative Benefits in MDC
• Harder to skip questions
• Range checking
• Eliminates branching errors
• Eliminates transcription errors
• Portable and handy
• Immediate access to data from server- rapid data aggregation
and analyses
• Get GPS location of respondent easily
• Cleaner data- standardization of data
• Real time monitoring of enumeration is possible
• Security of data
• Updating questionnaire is hassle free
15
Sorry, this response is
required!
Enumerator picking households too close

16. Summary of Findings
• With very high mobile penetration rate and fast growing cell phone coverage, Pakistan
presents a fertile ground for adopting ICT based solutions in data collection for development
plans.
• Experimental research on data collection tools demonstrate that there is high potential of
using Android based mobile devices in primary data collection. It can save more than 40% of
time and more than 30% of cost as compared to paper based surveys. This efficiency will
further increase in case of large scale surveys.
• Mobile devices based data collection eliminates the ‘data entry’ stage resulting in 0% errors
injection between collection and analysis stages.
• Mobile based data collection is a proven and tested technology being used around the globe as
well as in Pakistan for primary data collection in urban and rural areas. While Data collection is
a major stage requiring time and financial resources during the preparation of development
plan. Use of MDC in development plans preparation can substantially reduce the overall time
and costs of development plans preparation process.
16

17. Recommendations
• This research recommends the use of mobile based data collection tools in place of
paper based conventional methods.
• Open source platforms like Open Data Kit (ODK) which are freely available with
strong community support should be considered while designing mobile based data
collection and management system. It can be used for field data collection for socio-economic,
commerce, industry, housing, open spaces and other similar respondent
based surveys of urban development planning.
• Capacity building of planning agencies is crucial for the adaptation of ICT based
processes in routine data collection operations. An incremental approach should be
adopted while shifting from existing working methodologies to ICT based processes.
Department of City and Regional Planning can play a vital role in required capacity
building because of its good will among planners and competent faculty.
• We are living in information rich environment, now focus must be shifted from data
collection to analysis and proposals formulation. Adaptation of mobile based
mechanism will ensure more time and resources available for other processes.
17

18. Questions?
Comments?
18