Urban visual pollution is increasingly affecting the built-up areas of the rapidly urbanizing planet. Outdoor advertisements are the key visual pollution objects affecting the visual pollution index and revenue generation potential of a place. Current practices of uninformed and uncontrolled outdoor advertising (especially billboards) impairs effective control of visual pollution in developing countries. Improving this can result in over 20% reduction of visual pollution. This article presents a spatial decision support system (SDSS) to facilitate all the stakeholders (development control authorities, advertisers, billboard owners, and the public) in balancing the optimal positioning of billboards under the governing regulations. In terms of its technical implementation, SDSS is based on well-known geospatial open source technologies and uses an analytical hierarchy process AHP-inspired approach in spatial decision-making. It can help users through its category-specific user interface to identify potential sites to position new billboards and the selection of boards from existing sites based on a wide variety of characteristics. The observations of all stakeholders have been recorded through panel feedback to assess the system’s initial effectiveness. The proposed system has been found functional in identifying hot spots for the focused management and exploration of the best suitable sites for new billboards. So, it helps the advertising agencies, urban authorities, and city councils in better planning and management of existing billboard locations to optimize revenue and improve urban aesthetics. The system can be replicated in other countries irrespective of spatial boundaries by incorporating jurisdictional rules and regulations.

Spatial data infrastructures (SDIs) have been implemented for the last four decades in most countries. One of the key objectives of SDIs is to ensure the quick availability and accessibility of spatial data. The success of SDI depends on the underlying spatial datasets. Many developing countries such as Pakistan are facing problems in implementing SDI because of the unavailability of spatial data. Volunteered Geographic Information (VGI) is an alternate source for obtaining spatial data. Therefore, the question is what factors hamper the adoption of VGI for making it part of SDI in Pakistan. The intention behind this paper is to explore such factors as the key research question. To do so, we make use of the Technology–Organization–Environment (TOE) framework along with the partial least square structural equation model (PLS-SEM) to empirically analyze the factors impeding VGI from becoming part of SDI in the country. The study concludes that many technical, organizational, and environmental factors affect the adoption of VGI to be part of SDI in Pakistan.

Trackers installed in vehicles gives insights into many useful information and predict future mobility patterns and other aspects related to vehicles movement which can be used for smart and sustainable cities planning. A novel approach is used with the COPERT model to estimate fuel consumption on a huge dataset collected over a period of one year. Since the data size is enormous, Apache Spark, a big data analytical framework is used for performance gains while estimating vehicle fuel consumption with the lowest latency possible. The research presents peak and off-peak hours fuel consumption’s in three major cities, i.e., Karachi, Lahore and Islamabad. The results can assist smart city professionals to plan alternative trip routes, avoid traffic congestion in order to save fuel and time, and protect against urban pollution for effective smart city planning. The research will be a step towards Industry 5.0 by combining sustainable disruptive technologies.

In 2015, global real estate was worth $217 trillion, which is approximately 2.7 times the global GDP; it also accounts for roughly 60% of all conventional global resources, making it one of the key factors behind any country’s economic growth and stability. The accessibility of spatial big data will help real estate investors make better judgement calls and earn additional profit. Since location is deemed necessary for real estate and consequent decision-making, digital maps have become a prime resource for real estate purchases, planning and development. Personalisation can assist in making judgments by identifying user desires and inclinations, which can then be recorded or captured as a user performs some interactions with a digital map. A personalised real estate portal can use this information to suggest properties, assist homeowners and provide valuable real estate analytics. This article presents a novel framework for recommending real estate to users. By monitoring user interactions through an online real estate portal, the framework can make personalised recommendations of real estate based on content, collaboration and location. The effectiveness of the recommendations was tested by the user feedback mechanism through a method of mean absolute precision, and the results show that 79% precise suggestions were generated, i.e., out of 5 recommendations produced, users were interested in at least 3. Along with that, a separate house price prediction model based on neural networks and classical regression techniques was also implemented to assist users in making an informed decision regarding prospects of real estate purchase.

Unmanaged placement, size, location, structure and contents of outdoor advertisement boards have resulted in severe urban visual pollution and deterioration of the socio-physical living environment in urban centres of Pakistan. As per the regulatory instruments, the approval decision for a new advertisement installation is supposed to be based on the locational density of existing boards and their proximity or remoteness to certain land- uses. In cities, where regulatory tools for the control of advertisement boards exist, responsible authorities are handicapped in effective implementation due to the absence of geospatial analysis capacity. This study presents the development of a spatial decision support system (SDSS) for regularization of advertisement boards in terms of their location and placement. The knowledge module of the proposed SDSS is based on provisions and restrictions prescribed in regulatory documents. While the user interface allows visualization and scenario evaluation to understand if the new board will affect existing linear density on a particular road and if it violates any buffer restrictions around a particular land use. Technically the structure of the proposed SDSS is a web-based solution which includes open geospatial tools such as OpenGeo Suite, GeoExt, PostgreSQL, and PHP. It uses three key data sets including road network, locations of existing billboards and building parcels with land use information to perform the analysis. Locational suitability has been calculated using pairwise comparison through analytical hierarchy process (AHP) and weighted linear combination (WLC). Our results indicate that open geospatial tools can be helpful in developing an SDSS which can assist solving space related iterative decision challenges on outdoor advertisements. Employing such a system will result in effective implementation of regulations resulting in visual harmony and aesthetic improvement in urban communities.