Location Based Navigation Service (LBNS) is a mobile system which helps its user to make decision based on position and other supplementary information tagged to it, to implement a task at hand [1]. It constitutes a user who consumes data or information from LBNS for decision making, Internet service which conveys data through the world wide web to mobile devices, telecommunication services that are capabilities to transfer specific set of user-information to a group of users through the air waves broadcast, mobile device which is any small electronic device that uses wireless communications and has general computing capability and can be carried around much easily, geospatial data stored as coordinates and topology where cartography captivates user cognition [2] and GNSS/GPS systems for positioning as illustrated in Figure 1 [3] [4].

Huang et al. (2018) categorize challenges in LBS into technical and non-technical [1], these in turn fall in “Positioning, Modelling, Communication, Applications, Analysis of LBS-generated data, with social and behavioural implications” as non-Technical of which all are LBNS constituent components. With great advancement in LBS, challenges still exist [1] despite earlier prediction of high growth from previous studies [3]. This is revealed in a quantitative survey carried out in Nairobi CBD Kenya, which indicated that LBS technology had not been accepted and adopted yet [5]. From the study, LBS awareness was at 84% while actual usage was at 16%, clearly showing huge disparity between awareness by mobile phone user, on existence of navigation applications on their devices. Low acceptability points at underlying issues on this technology, leading this research to investigate on Geographic Information Retrieval (GIR), for user decision making [6]. Pegged on information interpretation and recognition by LBS user, featured as among the many challenges that keep on existing [7], leading to the research question on whether it is among other challenges hindering adaptation and acceptance of this technology within this region. The study manipulates GIR, using multiple database sources for distribution, of correct/right/appropriate cartographic visualization content, of geographic environment to a user based on dynamic circumstances that exist [8].

This paper is structured into six parts, the first part considers background knowledge on LBS and challenges that exist, it also highlights on a quantitative study carried out to test on this technology acceptance and adoptions in Nairobi CBD. The second part is literature review on user cognition process, GIR towards user of LBNS implementing user-specific features in cartographic systems. This is mainly with the aim of knowing the extent of user cognition for a given cartographic visualization. This part points out at challenges faced by LBNS in this area and states on solution from this study. The third part provides an optimization system design, outlining how it operates based on geographic Information retrieval (GIR) and how it impacts on user decision making and eventual reaction or response to information delivered. This section also provides conceptual architecture of the optimization system. Part four discusses on methods and material used in the process of formulating a mathematical algorithm for optimization process. The process of determining dependent and independent variables is discussed and ranking of various variables is also elaborated. Cartographic content variables are linked to user status and fickle variables of mobile device, to provide for an optimizing solution in LBNS application through a prototype LBNS development, which was coded to implement the optimization solution. Part five is on prototype LBNS constituents, whereas part six is conclusion and recommendation on the study.

To have a clear perspective of GIR, user cognitive characteristics need to be understood. Geographic Information Retrieval (GIR) is closely associated with recognition of information conveyed to a user through user cognitive task process [8]. Cognitive task requires a set of cognitive activity from a user, such as decision-making which should define a course of action, purposely preferred from a set of alternatives to achieve a definite goal [9]. This requires problem-solving, the need of working through details of a difficult issues, to reach a solution either through mathematical or systematic operations [10]. Memory information through the ability by which the mind program, hoard, hold and subsequently recollect information and past experiences in a person’s brain are essential. Finally, attention and judgement where attention is a state of being aroused, a behavioral and cognitive process of decisively concentrating on distinct aspect of information, whether considered biased or not in total disregard of any other perceivable information takes over. On the other hand, judgement is mental acts in which affirmation or denial of concepts from another forming an opinion or conclusion [11]. Cognitive task performance will largely depend on differences between novice and expert users [12], whereas cognitive task process requires one to go through goal setting, action content, action approach and consequence.

Goal setting is the object of a person’s ambition or effort, aimed at desired result where it will involve defining measurable goals and setting them, for accomplishing within a set time-frame as illustrated in Figure 2. Action content involves planning process, which helps to focus on ideas and to decide what steps to take to achieve particular goals that may have been set. Action approach involves reasoned-action approach or attitudes towards determined intentions. Outcome/Consequence is the way thing turns out or a result or effect [13].

Any action or response to information/data will largely be a result of decision making, which is a culmination of how information has been gathered, how alternative solutions have been identified, how the solutions have been weighted, how the choice among the alternatives have been arrived at, hence the quality of the outcome based on cartographic product and precision of a software, as well as hardware characteristics [14].

Decisions can have so many different effects on situations, processing time will depend on the number of choices available as they may require different actions. Delays in responses can make the difference between winning and losing, life or death, gain or loss of opportunity. Figure 3 below illustrates GIR, feeding information to user that will assist in decision making within a time frame thereby affecting the outcome.

Current LBS technology is marred by inefficiency and delivery of irrelevant content, consumed information is dependent on manual navigating web pages for relevant information through hyperlinks, hypertext and hypermedia as indicated in Figure 4. This can be painstaking step by step process and time consuming that it may cause unnecessary delay in information delivery, resulting to misinterpretation and delayed response or un-appropriate decision leading to wrong reaction and bad consequence, which may be loss of resources, time and even life as illustrated in Figure 5. This is supported by Lobben (2009), who chart out challenges in this technology and quantifies the cost of getting lost as momentous, due to not receiving relevant cartographic information for decision making [15].

With difference in LBNS user needs and aspirations, together with the dynamic nature of mobile device variables, a multivariate contextual optimization algorithm for cartographic content in LBNS, to address user satisfaction in information retrieval and delivery was developed. Cartographic content variables were determined and assigned values based on assumed preference status. Fickle

mobile device characteristics were also decided upon based on assumed measurable parameters. Both cartographic content and fickle mobile device variables were later to be calibrated through a quantitative research survey to a knowledgeable group of respondents in the field of Geomatic and Geospatial Information Systems in Jomo Kenyatta University of Agriculture and Technology (JKUAT).

GIR was implemented through a “lift up” process, as opposed to a “staircase” step by step method of web navigation as illustrated in Figure 6. This was to improve on decision making and reaction time, hence improved outcome.

The lift up approach for GIR eliminates Manual Navigation of Geographic Resource (MANAGER), and introduces a function that optimizes variables that prevail in LBNS, improving on response as depicted in Figure 7. Optimized

solution for GIR is to provide relevant information, that is recognizable to a user within suitable time, guarantying a timely, effortless, action-oriented response or reaction (TEAR).

An optimization algorithm was developed between user status, fickle mobile phone features as independent variables and cartographic content as dependent variables. A mathematical relationship between user and fickle features together with ranked cartographic visualization content, were developed through the process outlined in the methodology flowchart in Figure 10.

A prototype LBNS was developed using Android Studio 2.2.2 as the Integrated Development Environment (IDE), Android Software Development Kit (SDK) version 24.4.1 which is loaded with Google’s material design concept, Google maps for android, Volley library version 1.0.19 and Picasso library version 2.5.2. Incorporated with the optimization algorithm as an added module, it is able to provide a user with a personalized cartographic visualization content, to enable user’s orientation, recognition of geographic space, providing swift decision making and response, hence improved outcome.

GIS Helper application is a LBNS with optimization module for cartographic content, aimed at improving on relevance and effectiveness of cartographic visualization content delivery to mobile device user, to improve on decision making leading to appropriate reaction or response hence outcome. This is a simple to interact application that has a landing page or welcome page, leading to a user interface where a user is to choose on their status as indicated in Figure 24. This includes travelling mode and familiarity to destination, further leading to the application fishing out status of mobile device fickle variables as registered in the system. Optimization is done in the application where suitable cartographic content is delivered to a user through a user interface.

v LBNS technology can be developed based on existing technical challenges, to suit user needs, aspirations and satisfaction, considered as a social being which is non-technical challenge, to warrant acceptance and adoption.

v Personalized LBNS can be achieved through optimization of cartographic content, in relationship to user status and mobile device variables. Optimized solution in contextual cartographic content in geographic Information retrieval, can be achieved through multiple data sourcing, if applied to the application.

v Optimization in LBNS provides room for improvement on cartographic content presentation variables, for user satisfaction and consumption resulting to appropriate and relevant content that steers decision making for user’s response, on situation as per prevailing circumstances of mobile device.

v For any users at same location, same destination, it is not necessarily true that cartographic content delivery is exactly the same, as it is dependent on individual familiarity to destination and fickle variables of their individual mobile devices, which may not necessarily have similar variable at any given instance, thereby personalizing individual user needs.

v In optimization of cartographic content in LBNS, suitable cartographic variable need to be developed for the application to offer satisfaction to user, in which recognition and orientation of user should be achieved within a limited time. More investigations can be performed to determine cartographic content preference, based on varied user need as they form the base to optimization solution.

v LBNS is a user decision-making tool, in that it should be able to immediately change on what information to retrieve based on instantaneous changing circumstances of user and mobile device. The content should also provide general orientation of user within some geographic environment.

v An automated system can be developed to link to the optimization structure which can automatically alter how cartographic content can be presented to a user, through continued monitoring, testing and evaluation of the application. This will improve on efficiency in tailor making of application to conform to needs of specific users.

v Apart from user status given priority in the system, more variant either technical or non-technical need to be tested as well to determine their viability in the effort of improving LBNS and its service performance.

I wish to extend my special thanks to my supervisors Prof. E. H. Waithaka, Prof. M. K. Gachari and Prof. D. N. Kuria for their guidance, support and patience. I equally give a lot of gratitude to my beloved Wife Everlyne Nangila Nyongesa for her encouragement, moral and material support. I also express my sincere appreciation to Jomo Kenyatta University of Agriculture and Technology for sponsoring my research work.

The authors declare no conflicts of interest regarding the publication of this paper.

Wasomi, C.B., Waithaka, E.H., Gachari, M.K. and Kuria, D.N. (2022) Location Based Navigation Service Technology: Development of an Optimization Algorithm for Multivariate Contextual Cartographic Content in Location Based Navigation Service. Journal of Computer and Communications, 10, 57-90. https://doi.org/10.4236/jcc.2022.101004

For optimal cartographic content, the resultant function is as below:

f ( x ) = ∑ i = 1 100     W 1 a x 1 + W 2 b x 2 + W 3 c x 3 + W 4 d x 4 + W 5 e x 5 + W 6 f x 6     + W 1 K 1 + W 2 K 2 + W 3 K 3 + W 4 K 4 + W 5 K 5 + W 6 K 6 (A.1)

where,

a, b, c, d, e, f are coefficients;

x₁, x₂, x₃, x₄, x₅, x₆ are variables within the hardware and user status;

C (K₁, K₂, K₃, K₄, K₅, K₆) is a constant for individual variables.

For the function, x can be obtained from A and y through a regression analysis in Equation (A.1), using matrix algebra as indicated Figure 25:

A x = y (A.2)

The elements of A and y shown below, populate the A and y matrices resulting in matrices shown in Figure 26.

A 1 i , y 1 i : ( 0 , 6 ) , ( 10 , 6 ) , ( 20 , 6 ) , ( 30 , 6 ) , ( 40 , 5 ) , ( 50 , 4 ) ,                         ( 60 , 4 ) , ( 70 , 4 ) , ( 80 , 3 ) , ( 90 , 2 ) , ( 100 , 1 )

A 2 i , y 2 i : ( 0 , 0 ) , ( 10 , 2 ) , ( 20 , 2 ) , ( 30 , 3 ) , ( 40 , 4 ) , ( 50 , 5 ) ,                         ( 60 , 7 ) , ( 70 , 7 ) , ( 80 , 9 ) , ( 90 , 10 ) , ( 100 , 10 )

A 3 i , y 3 i : ( 0 , 10 ) , ( 10 , 10 ) , ( 20 , 10 ) , ( 30 , 10 ) , ( 40 , 9 ) , ( 50 , 8 ) ,                       ( 60 , 7 ) , ( 70 , 6 ) , ( 80 , 5 ) , ( 90 , 2 ) , ( 100 , 0 )

A 4 i , y 4 i : ( 0 , 1 ) , ( 10 , 2 ) , ( 20 , 3 ) , ( 30 , 5 ) , ( 40 , 5 ) , ( 50 , 7 ) ,                         ( 60 , 7 ) , ( 70 , 10 ) , ( 80 , 10 ) , ( 90 , 10 ) , ( 100 , 10 )

A 5 i , y 5 i : ( 0 , 10 ) , ( 10 , 10 ) , ( 20 , 10 ) , ( 30 , 10 ) , ( 40 , 7 ) , ( 50 , 7 ) ,                       ( 60 , 5 ) , ( 70 , 5 ) , ( 80 , 3 ) , ( 90 , 2 ) , ( 100 , 1 )

A 6 i , y 6 i : ( 0 , 6 ) , ( 10 , 6 ) , ( 20 , 6 ) , ( 30 , 5 ) , ( 40 , 5 ) , ( 50 , 4 ) ,                       ( 60 , 4 ) , ( 70 , 4 ) , ( 80 , 3 ) , ( 90 , 2 ) , ( 100 , 2 )

Therefore:

Substituting for the coefficients in the function (A.1),

hence,

f ( x ) = ∑ i = 1 100 − 0.05 x i 1 + 0.1036 x i 2 − 0.0973 x i 3 + 0.1 x i 4 − 0.1 x i 5 − 0.0436 x i 6   + 6.7727 + 0.1818 + 11.8636 + 1.3636 + 11.3636 + 6.4545 (A.3)

In order to choose an optimal cartographic visualization content, based on user status and fickle variables of mobile device, a weighted mean score was obtained. Using a Likert scale ranking criteria, from most important, important, average, less important, not important, varying from 5, 4, 3, 2, 1 respectively as in Table 12. An aggregated score of the weighted cartographic content at any given instance of request, is obtained and a weighted mean score computed as in expression A.4 to find the optimum content for presentation.

x ¯ = ∑ ​     w i x i ∑ ​     w i (A.4)

With an assumption that the most important variable be user status, followed by battery power and distance to destination as important, time limit error being taken as average, while signal strength and data volume were considered as less important and not important respectively as indicated in Table 13.

Thus a (12 × 1) weight matrix multiplied to the function obtains a resultant cartographic content. This is demonstrated in equation below.

x = ( I ⋅ W ) T ⋅ ( A T A ) − 1 A T y (A.5)

where

I is identity matrix;

W is weight matrix.

Hence through matrix multiplication of identity matrix shown in Figure 27, with assigned weights to produce a weight matrix indicated in Figure 28, resulting to determinant for the optimization function which is cartographic content option to be presented.

x = ( I ⋅ W ) T ⋅ ( A T A ) − 1 A T y (A.6)

where

I is identity matrix;

W is weight matrix.

The resultant function is

f ( x ) = ∑ i = 1 100     W 1 a x 1 + W 2 b x 2 + W 3 c x 3 + W 4 d x 4 + W 5 e x 5 + W 6 f x 6   + W 1 K 1 + W 2 K 2 + W 3 K 3 + W 4 K 4 + W 5 K 5 + W 6 K 6 (A.7)

where W is the weight matrix.

Hence final optimization algorithm,

f ( x ) = ∑ i = 1 100 5 19 ( − 0.05 ) x i 1 + 4 19 ( 0.1036 ) x i 2 + 1 19 ( − 0.0973 ) x i 3 + 2 19 ( 0.1 ) x i 4   + 3 19 ( − 0.1 ) x i 5 + 4 19 ( − 0.0436 ) x i 6 + 5 19 ( 6.7727 ) + 4 19 ( 0.1818 )   + 1 19 ( 11.8636 ) + 2 19 ( 1.3636 ) + 3 19 ( 11.3636 ) + 4 19 ( 6.4545 ) (A.8)

For varying values of x that are independent variables, the solution is given by the above mathematical equation, which represents the cartographic content to be delivered.