GIS diagnostics map a no-greenfield path to regenerate aging Gulf districts

Two aging districts in the United Arab Emirates, Al-Butina in Sharjah and Al-Bustan in Ajman, have become the test beds for a geographic information system framework that turns satellite imagery, road networks and land-use layers into a concrete regeneration blueprint. The study, published in City, Territory and Architecture, combines vector GIS modeling with AI-assisted architectural and urban design diagnostics to quantify what is wrong with these car-oriented neighborhoods and to test what targeted interventions could achieve. The result is a replicable, no-greenfield pathway that reuses existing rights-of-way, activates underutilized land and reallocates street space to people rather than parked cars.

The methodology rests on spatial data built from recent satellite imagery and validated against Google Earth and OpenStreetMap. In Al-Butina, a 200.4-hectare site envelope, the analysis identified 68.2 hectares of underutilized white land, roughly 34 percent of the district, as a structured land bank for adaptive reuse and small-lot infill. The proposed plan stitches a 6.1-kilometer walking and cycling lattice through existing rights-of-way, converting a fabric dominated by vehicular movement into a permeable network that supports safe access and short trips. Consolidated parking across 0.83 hectares on eleven sites unlocks curb reallocation to shade trees, wider sidewalks and bicycle bays, while ten pocket-green sites totaling 1.68 hectares form shade chains and a direct connector links the neighborhood westward to the Sharjah Corniche Walk.

Al-Bustan, a 68-hectare district near Ajman Port and the Free Zone Industrial Area, receives a parallel treatment. The proposal reuses existing corridors for a 6.40-kilometer active grid intended to reduce embodied and operational emissions through mode shift and shorter trips. A 63.81-hectare land bank is positioned for repair and reuse hubs, maker spaces, micro-logistics depots and mixed-use infill. Parking consolidation across 3.35 hectares on thirteen sites frees curb space from ad-hoc occupation, and six green sites totaling 5.47 hectares rebalance open-space equity while extending shade chains within a two-to-three-minute walk for more residents. The plan anchors eastward to the Ajman Port waterfront and Free Zone, creating a direct human-scale interface that could seed proximity economies where residents sell home-grown or cooked goods to nearby industrial workers.

The diagnostic power of the framework lies in its ability to expose spatial inequality that deters investment. GIS mapping pinpointed vacant and misused plots, connectivity breaks, walkability deficiencies and service gaps that conventional planning documents had not systematically captured. In Al-Bustan, maps revealed a tight grid interrupted by curvilinear arterials producing uneven permeability, uneven distribution of schools and health facilities, commercial activity concentrated on major corridors, and a marked deficit of green space consisting of small isolated patches. Low-density G+3 stock indicated horizontal expansion and inefficient land take, prompting proposals for selective up-zoning to mid-rise mixed-use forms around corridors and nodes.

Al-Butina’s maps told a similar story of fragmented potential. A largely orthogonal street grid punctuated by roundabouts yielded good vehicular permeability but weak priority for pedestrians and cyclists, with no continuous sidewalks, crossings or cycle facilities. Commercial frontage concentrated on primary roads created visible business spines alongside localized congestion and spillover parking. Green infrastructure was scarce and fragmented, intensifying heat exposure, while low-rise bands near high-access corridors depressed land efficiency. The reading produced a clear agenda for retrofit: an active-mobility layer, selective traffic calming, curb reallocation, small embedded facilities positioned from GIS-derived demand heatmaps, and distributed pocket parks at network junctions stitched into a shade chain aligned with the future walking and cycling network.

Pareto analysis quantified the extreme concentration of land in a few categories. In Al-Bustan’s 87.94-hectare footprint, unutilized land dominated at roughly 63.08 hectares, about 71.7 percent, and together with roads at roughly 11.64 hectares accounted for approximately 85 percent of the site. Green space at roughly 0.86 hectares and amenities at roughly 0.34 hectares were marginal, explaining weak walkability, heat exposure and limited public activity. Al-Butina showed an even starker pattern: unutilized land at 68.2 hectares and road right-of-way at 30.7 hectares together accounted for nearly 49.3 percent of the 200.4-hectare envelope, with every other category collectively adding only a few percentage points. The cumulative curve flattening after the second bar illustrated a classic 80/20 pattern in which a few drivers explain nearly all of the outcome.

Radar KPI benchmarking normalized heterogeneous indicators such as hectares per ten hectares and kilometers per square kilometer so that mobility, green space, logistics and parking could be assessed simultaneously against a target of 1.0. In Al-Bustan, logistics scored the highest, connector and active-grid measures were strong, and green space sat close to target, while parking hovered around the mid-range, flagging the need for careful operations through pricing, time limits and shared use rather than additional land take. In Al-Butina, the waterfront connector was the strongest performer at roughly 0.9, the active walking-cycling grid and structured parking each registered around 0.8, and pocket greens reached roughly 0.7. The critical shortfall was underutilized land at approximately 0.3 against a target of no more than 10 percent versus the current 34 percent, confirming that land-bank activation remains the decisive lever.

The framework’s predictive layer integrated machine learning within the GIS environment, processing spatial layers and field data through Random Forest and Neural Network algorithms to forecast regeneration outcomes such as improved walkability, service access and social cohesion. Pilot AI-generated building models tested the approach on specific parcels. In one Al-Bustan pilot, an underutilized barren lot that fragmented the urban fabric was reorganized into a modest communal space with pedestrian paths, shade and planting. A second pilot restructured a neglected open space into a well-integrated urban area with organized green landscapes and legible pedestrian routes. In Al-Butina, a vacant lot that had devolved into open-air parking was reimagined as a compact mixed-use program with commercial and office frontages, high-performance glazing to reduce operational energy, and a green infrastructure layer of street trees and planted areas improving microclimate and air quality.

The study’s authors are explicit that these proposals are evidence-based planning and design models rather than definitive redevelopment solutions. The research focused on strategic spatial analysis and AI-supported model generation rather than full implementation and post-occupancy validation, meaning the long-term performance of the mixed-use, green-space and community-oriented interventions has not been tested in real urban settings. Effectiveness requires further validation through phased implementation, stakeholder engagement and longitudinal assessment. Semi-structured interviews and focus groups with residents, alongside participatory mapping, informed the spatial analysis, and predictive maps were presented to local stakeholders for feedback on technical feasibility and contextual suitability.

What makes the work significant beyond the two districts is its methodological template. By pairing GIS-derived layers with Pareto prioritization and radar KPI benchmarking, the framework identifies the few categories driving most outcomes, ranks the highest-leverage assets, and sequences interventions so that active mobility comes first, pocket greens follow on priority links, and commercial and amenity infill completes the package. The approach aligns with emirate-level strategies such as the Dubai 2040 Urban Master Plan and Sharjah’s sustainable development initiatives, and it demonstrates how data-driven diagnostics can transform neglected urban areas into vibrant, resilient communities without expanding onto greenfield land. For rapidly urbanizing regions across the Gulf and beyond, the study offers a transferable model for evaluating aging districts through land utilization, connectivity improvement and public realm enhancement.

Subject of Research: GIS-based diagnostics and scenario testing for sustainable regeneration of aging urban districts in Sharjah and Ajman, UAE

Article Title: Sustainable urban regeneration of aging districts in Gulf region through GIS based diagnostics and scenario testing

Article References: Chohan, A. H., Mangi, M. Y., Noori, F., & Awad, J. (2026). Sustainable urban regeneration of aging districts in Gulf region through GIS based diagnostics and scenario testing. City, Territory and Architecture, 13(1), Article 22. https://doi.org/10.1186/s40410-026-00313-y

Image Credits: AI Generated

DOI: 10.1186/s40410-026-00313-y

Keywords: urban regeneration, GIS, Sharjah, Ajman, walkability, land bank, parking consolidation, pocket parks, Pareto analysis, radar KPI, sustainable urbanism, UAE