Integrating Spatial Analysis and Regional Science to Guide Urban Planning (Editorial for Special Issue Reprint)

1. Introduction

1.1. Advances in Spatial Analysis

Spatial analysis is a research paradigm that employs specialized techniques and models to analyze and model spatial data, focusing on the variation over space and helping to reveal hidden patterns and model relationships [1]. Since the 1950s, spatial analysis has been established as a core discipline in quantitative geography [2], with numerous applications in the scientific fields in which the location of activities/events must be explicitly considered. Areas of application include, among others, urban analysis, environmental planning, transportation analysis, health planning, etc. In the 1990s, with the wide availability of microcomputers, spatial data, and commercial/open software, spatial analysis was integrated into the geographic information systems (GISs) that offered a powerful computing environment for managing and analyzing spatial data. During the past two decades a new paradigm has been gradually emerging, related to the massive use of big data in the analysis of space at several spatial scales [3], from the most local to the regional or even the global one.

Spatial analysis emerged historically from simple models that estimated the interaction/relationship of two objects/events as a function of the distance that separated them. The simple gravity function, proposed in the 1950s, was gradually transformed to an elaborate set of equations and mathematical constructs that described the impact of the geographic location on the environment and our lives. In city planning, cities were initially recognized as static systems governed by regular patterns. Classical approaches, such as Christaller’s central place theory or the rank–size law, described settlement hierarchies in abstract isotropic space. Early urban economics introduced density gradients and bid–rent models that explained how population densities and land values decline with distance from a center, followed by large-scale interaction models, such as simulating the flows of people from residences to employment using spatial interaction functions.

Since the 1980s spatial analysis has been transformed, and three major axes can be mentioned:

First, GPS and remote sensing have resulted in a revolution in the availability of spatial data. The GPS and the mobile devices permitted the on-demand precise identification of the location of any event. Volunteered geographic information (VGI), geotagged social media, and sensor networks introduced unprecedented spatial detail and update frequency. With remote sensing it was possible to observe the Earth’s surface at high resolution and at regular time intervals, thus providing consistent data for urbanization, land-use change, natural resources monitoring, climate change, etc. Landsat and other sensors provided a continuously updated open repository of satellite images. In the EU, the Copernicus program and the Sentinel constellation resulted in a wealth of free satellite images at detailed resolutions, as well as processed data for European countries ready to be used for spatial analysis. The CORINE Land Cover program—alongside other land use/land cover geospatial databases of the Copernicus Land Monitoring Service and the European Environmental Agency—is another pioneering case study in urban and territorial transformation, allowing comparative studies at a pan-European level. This “datafication” of geography has shifted spatial analysis from coarse zones to detailed, dynamic observations of space and society.

Second, advanced modeling techniques such as agent-based models, spatial statistics, raster analysis, and others were introduced, and nowadays they are enhanced by machine learning and artificial intelligence (AI). Since the 1990s, cellular automata and agent-based models have been widely used to reproduce urban growth, sprawl, or segregation as emergent processes [4]. providing a bridge between micro-behavior and system dynamics [5]. These methodologies can be used to analyze spatial-temporal time series of environmental data, to simulate traffic patterns and urban growth, etc.

Third, advances in computing facilities permitted the introduction of platforms that can process and analyze data using available software [6,7]. The Google Earth Engine provides an integrated environment for analyzing satellite images, producing vast georeferenced datasets that require explicit spatial-statistical handling [8]. The R software includes ready-to-use spatial statistics algorithms, whereas for users who want to develop their own code, Python libraries for spatial data are widely available. Complete modeling environments are readily and freely available, for example, models for simulating urban growth like UrbanSim [9] and SLEUTH [10], models of land surface temperature like SURFEX [11], models for flood modeling like HEC-RAS [12], and models for microclimate and environmental simulations (ENVI-met-type models) at the urban canopy level. Such models can be used for environmental simulations that link human activities, housing and employment location, topology, and existing infrastructure to climate and ecosystem performance [13] or to test various scenarios for flood or other hazard mitigation.

1.2. Spatial Analysis in Planning

Planning at the local/metropolitan/regional scale is revolutionized by advances in spatial analysis and the availability of extremely large datasets (big data) and methods for processing, managing, and viewing those data, either in the form of remote sensing analysis [14], advanced software analysis [15,16], or generally, in the form of complex algorithms, including machine-learning methods and, more recently, artificial intelligence (AI).

The integration of large-scale data and advanced analytical methods is reshaping current decision-making processes at multiple spatial scales. The rapid expansion of data sources provides planners with better ways of understanding urban dynamics. For instance, combining satellite imagery, mobile phone records, and geotagged social media with machine learning techniques enables predictive modeling of urban growth, mobility patterns, and environmental risks [17].

Remote sensing and GIS-based spatial analyses remain central to these developments, offering the mapping and visualization tools needed to interpret complex datasets. Those systems enable detailed assessments of land use, vegetation cover, and infrastructural conditions, supporting both strategic and operational planning; while when paired with machine learning, such datasets can reveal spatio-temporal patterns and scenario testing, offering planners the ability to evaluate multiple interventions before their implementation.

Nevertheless, researchers and practitioners increasingly recognize that purely technical approaches cannot fully capture the lived realities of urban spaces and pinpoint the need to integrate traditional planning knowledge, local expertise, and participatory methods into data-driven processes. Therefore, participatory GIS, citizen sensing, and co-creation workshops are increasingly employed to complement computational methods, ensuring grounding analyses in local expertise and lived experience [18]. This combination of advanced analytics with participatory approaches represents a broader trend toward “hybrid planning,” where technology augments, rather than replaces, human judgment.

With the integration of real-time data streams, cities can now implement dynamic policies—in the sense of the “real-time city”, as described by Kitchin [19]—highlighting a shift from static, masterplan approaches toward flexible, evidence-informed decision-making. Overall, today’s trends reveal a planning paradigm in which technological innovation, big data, and AI coexist with traditional spatial methods and participatory engagement, producing more responsive, evidence-based, and socially attuned planning processes.

This Special Issue provides a repository of novel approaches in spatial analysis that follow the trends discussed above and that could be potentially integrated into planning methodology at different scales, from the urban to the regional. The significance of the value of the research discussed in these papers cannot be underestimated since only planning policy and practice can address the existing challenges of quality of life and the new and evolving challenges of climate change and its impacts on cities and rural areas (urban heat, floods, drought, degradation of ecosystems, increasing urbanization, environmental degradation, etc.).

Issues like urban sprawl and inclusive sustainable growth underline the need for innovative approaches on how to handle the environmental consequences of urban growth at a metropolitan or even regional scale [20] Climate change introduces additional challenges for urban and regional systems, including increased frequency of extreme weather events, rising temperatures, flooding, and pressures on water and energy systems. These dynamics necessitate planning strategies that not only accommodate growth but also enhance the resilience of urban and peri-urban areas [21].

As shown by the selection of papers included in this Special Issue, spatial analysis facilitates the integration of diverse datasets, helping planners understand the interdependencies among different urban processes. For example, spatial analysis and modeling can help quantify urban sprawl (List of Contributions, 1,2) identify climate-related high-risk areas like urban heat island hotspots (List of Contributions, 3) or other environmental issues in the urban context like noise perception (List of Contributions, 4) optimize the placement of green infrastructure (List of Contributions, 5), and evaluate the cumulative impact of new developments on regional ecosystems (List of Contributions, 6) Such methods usually enable scenario-based planning, allowing decision-makers to foresee the potential outcomes of different urban growth strategies and assess trade-offs between economic development, social equity, and environmental protection. When combined with traditional spatial analysis methods, new spatial planning methods and technologies allow a more holistic approach to planning, where policies are not only reactive but anticipatory, promoting resilience and sustainability across regional systems.

2. Research Papers and Thematic Areas

The aim of this Special Issue is to contribute to the challenges discussed earlier and to explore novel approaches connecting spatial analysis and spatial planning elements. The Special Issue includes papers that provide methodological advances and/or have practical planning implications. The Special Issue includes eight research papers, covering two basic thematic areas: (a) Spatial analysis using census and secondary geospatial data at the urban or regional level. (b) Spatio-environmental analysis using remote-sensing data and simulations at the urban level.

Additionally, a research contribution (List of Contributions, 7) that discusses participatory mapping is included. In this perspective, integrated planning brings together spatial science with participatory governance, with this paper being a complement to the more data-driven contributions in the SI. Although not exhaustive in their selection of topics and methods, the eight papers provide an insight into the current research interests of international academics, including scholars from Europe, USA, and North Africa (

Table 1. Case study areas and research issues.

Case Study Area	Topic
Southern Europe	Equity in health planning (Rome); SDG urban metrics (Thessaloniki); ecosystem services and land capitals (inland Italy); impervious land expansion (Greece); gender-inclusive planning (Serbia)
North Africa	Urban heat and microclimate impacts (Morocco, Cairo)
USA	Urban morphology and noise modeling (NYC)

).

Table 2. List of published works, key methods used, and relevance to planning.

List of Contributions	Author (Year)	Study Region	Methods	Application in Planning
1	Lagarias (2023)	Greece, coastal zone	Spatial analysis based on land use detection, spatial indicators	Urban density/sprawl
2	Yiannakou & Zografos (2025)	Thessaloniki, Greece	Spatial indicators	Urban density/sprawl
3	Lachkham et al. (2025)	Morocco	Biophysically based land surface model	Land surface temperature estimation in urban areas
4	Chen et al. (2025)	New York City, USA	Multi-scale spatial analysis: spatial autocorrelation and spatiotemporal heterogeneity, geographically weighted regression (GWR) modeling	Noise perception in urban environments
5	Abdelmejeed et al. (2023)	Cairo, Egypt	ENVI-met analysis (urban geometry and tree scenarios)	Microclimate in urban environments
6	Marino et al. (2024)	Italy, inland areas	LULC changes, demand and supply methods	Spatial dynamics and ecosystem services
7	Čolić Marković & Danilović Hristić (2025)	Belgrade, Serbia	Participatory planning and other participatory methods	Inclusion and public spaces
8	Mazzalai, et al. (2025)	Rome, Italy	Socio-demographic indicators	Definition of health districts in urban areas

tabulates the papers in terms of the study region, the methodology used, and its application and relevance in planning. A variety of models are noted, including spatial indicators estimation, land use/land cover (LULC) changes, simulation models, spatial autocorrelation and spatiotemporal heterogeneity, geographically weighted regression (GWR) modeling, demand and supply methods, land surface model (LSM), etc.

A brief description of the research objectives, the methodology used, and the results/major findings obtained in each paper is presented below:

Lagarias (List of Contributions, 1) applies geospatial methodologies on high-resolution imperviousness raster data to identify urban sprawl patterns in Greece’s coastal zones between 2006 and 2018. The analysis shows that although during the economic crisis there was a significant slowdown in construction, some areas continued to be developed, even within areas of ecosystemic priority (Natura 2000) and climate-change sensitivity (flood-prone areas). Methodologically, the study highlights imperviousness as a more effective indicator than building density or urban land cover for monitoring and managing land-use change and calls for stronger spatial planning instruments in Greece and across the Mediterranean to explicitly regulate impervious expansion and safeguard climate resilience.

Yiannakou & Zografos (List of Contributions, 2) address peri-urban sprawl in Thessaloniki and estimate the ratio of land consumption to population growth, using GIS-based land-use analysis at different time periods. The methodological innovation lies in estimating the global indicator of Sustainable Development Goals’ metrics at a fine-grained local scale, which allows evaluation of its precision and relevance. The results reveal that land consumption has outpaced population growth, indicating inefficient sprawl. The study’s significance rests on its ability to bridge global sustainability frameworks with site-specific spatial analysis, reinforcing the operational value of SDGs in urban planning practice.

Lachkham et al. (List of Contributions, 3) explore the link between urbanization and land surface temperature (LST) in multiple cities in Morocco, applying remote sensing integrated with spatial statistical techniques. The methodological strength is the comparative, multi-city analysis that identifies common and divergent patterns across diverse urban morphologies. Results confirm a strong relationship between urban expansion and surface heat and reveal intra-urban variations affected by the population density and urban form. This work underscores the need for climate-sensitive planning in rapidly urbanizing contexts of the Global South.

Chen et al. (List of Contributions, 4) explore the relationship between urban morphology and noise perception, employing a multi-scale spatiotemporal modeling approach that integrates morphological metrics with acoustic data and perception surveys. Methodologically, it links physical urban form to subjective sensory experiences, combining quantitative spatial data with qualitative perceptions. Results show scale-dependent relationships: while dense environments intensify noise annoyance at certain scales, they also mitigate perception in others by diffusing sound or supporting adaptive behavior.

To optimize microclimate conditions Abdelmejeed et al. (List of Contributions, 5) apply the ENVI-Met simulation model in Cairo, Egypt, to test different urban morphology and vegetation configurations. Methodologically, it relies on design-oriented scenario modeling, allowing urban form and greenery placement to be virtually explored. Results indicate that depending on the morphology of the urban streets (aspect ratio) and the presence of urban canyons, different strategies regarding placement and density of trees should be applied to mitigate heat stress. The results confirm ENVI-Met’s value as a useful practical planning tool, enabling forward-looking design choices and showing how simulation modeling can be used for implementing adaptation strategies for climate-sensitive design in urban environments.

Marino et al. (List of Contributions, 6) analyze territorial change in Italian inland territories through an ecosystem services framework that integrates ecological, social, and economic indicators. Methodologically, it is noteworthy because it uses an assessment model that considers how land transformations affect natural resources, economic viability, and social well-being. Results show that degradation of natural capital reduces ecosystem services and undermines the economic potential and the social resilience of these regions.

Čolić Marković and Danilović Hristić (List of Contributions, 7) address the inclusion of gender perspectives in urban planning, using qualitative, participatory, and policy analysis methods rather than quantitative modeling. The study uncovers persistent gender gaps in planning participation and decision-making, showing how planning cultures reproduce inequalities. Its major finding is that procedural inclusivity is as crucial as spatial equity, since technical solutions alone cannot address systemic exclusion. The significance lies in highlighting that integrated planning must combine spatial science with participatory governance.

Finally, Mazzalai et al. (List of Contributions, 8) propose a method to redefine urban boundaries to improve equity in healthcare access in Rome. They apply a socio-demographic spatial model, combining census data, socio-economic variables, and geospatial mapping of health services. This methodological approach incorporates equity lenses, highlighting disparities between administrative divisions and populated urban geographies. Results show that official boundaries do not align with actual demographic and health needs, producing systematic inequities in service provision.

3. Summary and Perspectives

Spatial analysis has moved from stylized, equilibrium-based theories to data-intensive, dynamic approaches. It now integrates satellite monitoring, big-data analytics, complex simulation procedures, machine learning, and AI-driven tools, providing a much richer basis for understanding and guiding urban and regional change and related spatial policies. Integrating spatial analysis methods into planning processes has become particularly critical in managing the spatial complexity of urban development, as spatial analysis tools allow planners to visualize and simultaneously analyze land-use patterns and environmental constraints at multiple scales.

Furthermore, contemporary planning increasingly emphasizes the multi-dimensional nature of urban systems. Social, economic, and environmental factors interact in complex ways. Spatial analysis facilitates the integration of diverse datasets—from demographic and economic statistics to mobility patterns and environmental indicators—helping planners understand the interdependencies among different urban processes.

This Special Issue demonstrates how spatial analysis methods are instrumental in dealing with urban-environmental challenges. Cross-regional case studies illustrate how context-specific problems (e.g., urban heat islands, ecosystem services) can be addressed through a spatial–regional science lens. The focus on tools such as ENVI-Met (a leader in holistic 3D modeling software) and Sustainable Development Goals (SDG) indicators underscores a commitment to actionable research and informed policymaking. Overall, this Special Issue brings a convergence of theoretical advancement and practical application.