Spatial Analysis Approach Research Articles

Modeling Airport Catchment Areas: Using Spatial Analysis Approach

Th e airport catchment area is the geographic area from which an airport can reasonably expect to draw commercial air service passengers. Th e purpose of this interdisciplinary research is to estimate airport catchment areas using a spatial analysis method for informed airport management. In order to ensure the comprehensiveness and reliability of the research, we chose to analyze the catchment areas for five airports of different sizes and in different geographic locations in the United States. The Huff model, which is usually used in marketing, economics, and retail research, was adopted in this study. We applied this model in airport catchment analysis for the selected airports, with consideration of ground transportation distance and airport attractiveness. We identified the best distance factors and parameters for modeling purposes. Th e results provided reliable information for the selected five airports to estimate their catchment areas. In addition, the model can provide a good reference for other airport catchment analysis studies about the effectiveness of the Huff model in prediction.

Molecular, morphometric, and spatial data analyses provide new insights into the evolutionary history of the Peromyscus boylii species complex (Rodentia: Cricetidae) in the mountains of Mexico

Peromyscus is the most speciose genus in America, which occupies almost every terrestrial habitat type. However, its taxonomy and evolutionary relationships remain unsolved in several species, one species group with these issues is the P. boylii complex. This complex consists of 11 species (P. beatae, P. boylii, P. carletoni, P. ensinki, P. greenbaumi, P. kilpatricki, P. levipes, P. madrensis, P. schmidlyi, P. simulus, and P. stephani) largely distributed in Mexico and has been taxonomically studied during the last 40 years, resulting in the description of new species, several of them from the mountains of central Mexico. In this study, we aimed to integrate molecular, morphometric, and spatial data analysis approaches to inquire into the phylogenetic relationships. To clarify the taxonomic status of the P. boylii complex, particularly for the species distributed in the central Mexican highlands where the taxonomic uncertainty is high. We performed phylogenetic and divergence time analyses based on four molecular markers for all species within the complex, we explored cranium shape and size variation in 146 individuals of continental species (P. madrensis and P. stephani excluded) using geometric morphometrics. Also, we evaluated environmental differentiation and ecological niche modeling analyses of three species endemic to the central Mexican highlands to retrieve evidence on their evolutionary history. Our results recovered one well-supported clade including all mountainous species, in which P. schmidlyi was sister to the clade composed of P. greenbaumi and P. ensinki, which diverged during the late Pleistocene. The novel morphological data allowed a better differentiation of all continental species. The discovery of new specimens for P. greenbaumi and P. ensinki improved their taxonomic knowledge, as well as delimiting their habitat and distribution, showing substantial environmental divergence with low overlapped geographical distributions; P. schmidlyi restricted to the Sierra Madre Occidental, P. ensinki to the central-west Trans-Mexican Volcanic Belt, and P. greenbaumi to the center of the Sierra Madre del Sur. We discuss the biogeographic history of these three lineages and the role of the late Pleistocene climatic oscillations on their diversification.

Altered dynamic functional network connectivity in levodopa-induced dyskinesia of Parkinson's disease.

The aim of this study was to clarify the dynamic neural activity of levodopa-induced dyskinesia (LID) in Parkinson's disease (PD). Using dynamic functional network connectivity (dFNC) analysis, we evaluated 41 PD patients with LID (LID group) and 34 PD patients without LID (No-LID group). Group spatial independent component analysis and sliding-window approach were employed. Moreover, we applied a k-means clustering algorithm on windowed functional connectivity (FC) matrices to identify reoccurring FC patterns (i.e., states). The optimal number of states was determined to be five, the so-called State 1, 2, 3, 4, and 5. In ON phase, compared with No-LID group, LID group occurred more frequently and dwelled longer in strongly connected State 1, characterized by strong positive connections between visual network (VIS) and sensorimotor network (SMN). When switching from OFF to ON phase, LID group occurred less frequently in State 3 and State 4. Meanwhile, LID group dwelled longer in State 2 and shorter in State 3. No-LID group occurred more frequently in State 5 and less frequently in State 3. Additionally, correlation analysis demonstrated that dyskinesia's severity was associated with frequency of occurrence and dwell time in State 2, dominated by inferior frontal cortex in cognitive executive network (CEN). Using dFNC analysis, we found that dyskinesia may be related to the dysfunctional inhibition of CEN on motor loops and excessive excitation of VIS and SMN, which provided evidence of the changes in brain dynamics associated with the occurrence of dyskinesia.

Development of Land Capability-Based Settlement Areas in Banyuasin District

Lack study of land capability aspects in development of residential area affect the carrying capacity of environment. Therefore, this study aimed to analyze the land capability in Banyuasin District and formulate the land use directions according to its capability class. The research method used descriptive quantitative method using a spatial analysis approach with Geographic Information System and scoring analysis. The study results found out that the LCU of morphology was classified into low morphological LCU of 96.77% and less morphological LCU of 3.23%, the LCU of ease of work was high 48.11% and low 24.83%, the LCU of slope stability with the classification of high slope stability of 48.11% nd low slope stability of 24.83%, the LCU of foundation stability with high classification of 48.11% and low classification of 24.83%, the LCU of water availability with high classification of 48.11 and very low classification of 24.83%, the LCU of drainage with high classification of 56.62% and less classification of 46.11%, the LCU of erosion with high classification of 31.80% and very low classification of 1.85%, the LCU of waste disposal with sufficient classification of 46.11% and less classification of 56.62%, the LCU of disaster prone with high classification of 48.11% and low classification of 24.83%. In conclusion, the research area was divided into 3 classes of very high, moderately high, and low land development capability.

Research on the spatial correlation and drivers of industrial agglomeration and pollution discharge in the Yellow River Basin

The spatial auto-correlation analysis approach is used to examine the agglomeration features and geographical correlation of industrial agglomeration and pollutant discharge using 91 prefecture-level cities in the Yellow River Basin from 2005 to 2020 as the research target. The Yellow River Basin and the upper, middle, and lower reaches were investigated for the effects of industrial agglomeration on industrial pollution emissions. The Dubin model of the spatial panel is used to investigate the drivers of industrial pollution from both indirect effects and direct effects. The findings indicated that ① the spatial pattern of industrial pollution agglomeration and industrial agglomeration in the Yellow River Basin has spatial convergence; ② the global clustering characteristics of industrial pollution in the Yellow River Basin are apparent. The global correlations of the three pollutants are industrial wastewater > industrial smoke and dust > industrial SO2; there is a spatial association between industrial agglomeration and pollution agglomeration, and there are differences in the spatial interconnection between industrial agglomeration and the accumulation of different pollutants; ③ from the overall point of view of the Yellow River Basin, industrial agglomeration will reduce the pollution levels of industrial wastewater, industrial SO2, and industrial smoke and dust. From the perspective of different regions, industrial agglomeration in the upper, middle, and lower reaches is conducive to the decline of industrial pollution, and the role is in the middle, downstream, and upstream in order from large to small; and ④ the Yellow River Basin’s industrial pollution is primarily caused by population agglomeration, industrial structure, environmental regulation, opening to the outside world, and economic development. Their effects on industrial wastewater, industrial SO2, industrial smoke, and dust pollution vary.

Spatial Predictive Modeling of the Burning of Sugarcane Plots in Northeast Thailand with Selection of Factor Sets Using a GWR Model and Machine Learning Based on an ANN-CA

The main purpose of the study is to apply symmetry principles to general mathematical modelling based on multi-criteria decision making (MCDM) approach for use in development in conjunction with geographic weighted regression (GWR) model and optimize the artificial neural network-cellular automaton (ANN-CA) model for forecasting the sugarcane plot burning area of Northeast Thailand. First, to calculate the service area boundaries of sugarcane transport that caused the burning of sugarcane with a fire radiative power (FRP) values using spatial correlation analysis approach. Second, the analysis of the spatial factors influencing sugarcane burning. The study uses the approach of symmetry in the design of algorithm for finding the optimal service boundary distance (called as cut-off) in the analysis of hot-spot clustering and uses calculations with the geographic information system (GIS) approach, and the final stage is the use of screened independent variable factors to predict the plots of burned sugarcane in 2031. The results showed that the positively related factors for the percentage of cane plot sintering in the sub-area units of each sugar plant’s service were the distance to transport sugarcane plots index and percentage of sugarcane plantations in service areas, while the negative coefficients were FRP differences and density of sugarcane yield factors, according to the analysis with a total of seven spatial variables. The best GWR models display local R2 values at levels of 0.902 to 0.961 in the service zones of Khonburi and Saikaw. An influential set of independent variables can increase the accuracy of the ANN-CA model in forecasting with kappa statistical estimates in the range of 0.81 to 0.85 The results of the study can be applied to other regions of Thailand, including countries with similar sugarcane harvesting industries, to formulate policies to reduce the exposure of sugarcane harvested by burning methods and to support the transportation of sugarcane within the appropriate scope of service so that particulate matter less than 2.5 microns (PM2.5) can be reduced.

Temporal and Spatial Variation of Geographical Population Distribution in Jordan for the Period (1994 – 2015) Using GIS

The study aimed at analysing temporal and spatial patterns of population distribution in Jordan at governorate level. It also aimed to define the influencing geographical factors that affected the population distribution and caused the changes over the last decades based on census data of 1994, 2004, and 2015 years. Several approaches and statistical methods were used in this study to achieve study objectives, including analytical and descriptive, evolutionary, and spatial analysis approaches along with some quantitative methods. The choropleth maps and population density maps were created for mapping the population distribution in Jordan. In addition, the researchers has defined and the future population projections at governorate level in Jordan. The study revealed that there is a population concentration during the study period in three main Jordanian governorates: Amman, Irbid and Zarqa. Population numbers are also predicted to increase in some governorates in the future. The study concluded that both natural and human factors have influenced population distribution in Jordan, and the impact of the latter factors are more significant. Accordingly, the study showed that there are three main factors affecting the geographical distribution of population in Jordan: population growth, political and economic conditions in neighbouring Arab countries, and the reclassifying bases of urban areas in Jordan. The study recommends the need for population strategies and policies to be applied to face the changes that occur to population distribution in Jordan. Population densities among governorates and its influencing factors and consequences should be taken into consideration when planning for regional development.

Spatial spectral contextual image analysis approach to map water quality of inland waterbody using AVIRIS-NG datasets

Remote sensing has already proven its capability in water quality studies. The technology enables the mapping of water quality parameters that affect its optical properties. Generally, multi-spectral remote sensing data has been utilized for such analysis. Mostly, the band rationing or simple regression approaches have been adapted to map the water quality, however, they result in qualitative estimates rather than quantitative. On the other hand, sensor technology has made steep advancements from multi-spectral to hyperspectral. The hyperspectral data provides contiguous spectral information over several hundred narrow spectral bands. This emerging technique provides a more in-depth means of investigating spatial, spectral, and temporal variations, leading to more quantifiable/accurate estimates of the process under consideration. At present, very few satellite-based hyperspectral data are available. Recently, the Indian Space Research Organisation has initiated an airborne hyperspectral remote sensing campaign to cover the important earth features in India using Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) sensor in collaboration with the National Aeronautics and Space Administration (NASA). As the AVIRIS-NG hyperspectral data measures the spectral response in the wavelength range from 380 to 2510 nm at very narrow spectral bandwidth of 5 nm. These advanagtes of AVIRIS-NG datasets enable researchers to analyse the spectra of each and every pixel of the image spatially to match its spectral characteristics with the spectra collected in the field. In the present study, this spectral similarity approach has been expoilted within the context of turbidity, the optical property of water for Chilika Lake, Odisha, India; using the AVIRIS-NG data and field reference spectra. The lake was surveyed synchronous to AVIRIS-NG flights over it during the month of December 2015. A field-based spectral library with respect to various concentrations of turbidity in the lake has been generated using sophisticated instruments like field spectro-radiometer, handheld Global Navigation Satellite System (GNSS) receiver, and turbidimeter. The spectral angle mapper (SAM) spatial-spectral contextual image analysis has been carried out to map the turbidity of the lake. It was observed that almost at each and every pixel the image spectra were matching with the field-collected spectra. Further, the match between two spectra (field and image) measured in terms of SAM score was as high as 0.9 for most of the instances. Moreover, the estimated turbidity was in the agreement (R2 = 0.96) with the turbidity measured on the field. It was realized that the spectral similarity approach using hyperspectral remote sensing data provides more quantitative estimates as compared to multi-spectral data analysis.

A Spatial Analysis Approach for Urban Flood Occurrence and Flood Impact Based on Geomorphological, Meteorological, and Hydrological Factors

Urban flooding can cause significant infrastructure and property damage to cities, loss of human life, disruption of human activities, and other problems and negative consequences on people and the local government administration. The objective of this research work is to investigate the relation between urban flood occurrence and potentially flood-triggering factors. The analysis is performed in the western part of Athens Basin (Attica, Greece), where over the past decades several flood events caused human losses and damages to properties and infrastructure. Flood impact is measured by the number of citizen calls for help to the emergency line of the fire service, while potentially influencing factors are several geomorphological characteristics of the area and hydrometeorological indices regarding storms, which were determined with the aid of GIS techniques. The analysis is based on the investigation on binary logistic regression and generalized linear regression models that are used to build relationships between the potentially flood-influencing factors and the flood occurrence/impact for three events that were selected for reasons of comparison. The entire analysis highlights the variations attributed to the consideration of different factors, events, as well as to the different cell size of the grid used in the analysis. Results indicate that, the binary logistic regression model performed for flood occurrence achieves higher predictability, compared to the ability of the model used to describe flood impact.

Investigating Spatial Patterns of Pulmonary Tuberculosis and Main Related Factors in Bandar Lampung, Indonesia Using Geographically Weighted Poisson Regression.

Tuberculosis (TB) is a highly infectious disease, representing one of the major causes of death worldwide. Sustainable Development Goal 3.3 implies a serious decrease in the incidence of TB cases. Hence, this study applied a spatial analysis approach to investigate patterns of pulmonary TB cases and its drivers in Bandar Lampung (Indonesia). Our study examined seven variables: the growth rate of pulmonary TB, population, distance to the city center, industrial area, green open space, built area, and slum area using geographically weighted Poisson regression (GWPR). The GWPR model demonstrated excellent results with an R2 and adjusted R2 of 0.96 and 0.94, respectively. In this case, the growth rate of pulmonary TB and population were statistically significant variables. Spatial pattern analysis of sub-districts revealed that those of Panjang and Kedaton were driven by high pulmonary TB growth rate and population, whereas that of Sukabumi was driven by the accumulation of high levels of industrial area, built area, and slums. For these reasons, we suggest that local policymakers implement a variety of infectious disease prevention and control strategies based on the spatial variation of pulmonary TB rate and its influencing factors in each sub-district.

Spatial characterization of marine socio-ecological systems: A Portuguese case study

There is a lack of available methods to understand the associations between land and sea in social and ecological systems. This work adapts a methodology to explore how coastal-marine areas in the Portuguese Mainland Subdivision spatially differ in socioeconomic and marine environmental characteristics. The methodology used a combined spatial analytical approach of ecological, social, economic, and land-cover variables. The outputs of this work were Marine Socio-Ecological Categories (MSEC) mapped along the coast. Results show contrasting ecological and societal conditions across the coastal municipalities. The northern region and some parts of the Algarve coast are experiencing higher socioeconomic development but are also facing higher resource demands and placing more pressure on marine and coastal ecosystems. MSEC categories are valuable to understand how different conditions may be dealt with at regional and national contexts in future management and planning policies. Mapping socio-ecological systems aid sustainable economic development, but further research is needed to improve the system analysis. This study shows that there is no land-sea divide and the interactions between both systems should be reflected in the existent policy framework. • Method to study the spatial associations in coastal-marine socio-ecological systems. • Adapts a methodology to the Portuguese Mainland Subdivision. • Main outputs are the Marine Socio-Ecological Categories mapped along the coast. • Existent policy framework does not reflect the close connection between land-sea.

Spatial model of forest area designation and function based on multi-criteria in dry land and mangrove forest ecosystems, Central Sulawesi, Indonesia

Abstract. Akhbar, Naharuddin, Arianingsih I, Misrah, Akhbar RK. 2022. Spatial model of forest area designation and function based on multi-criteria in dry land and mangrove forest ecosystems, Central Sulawesi, Indonesia. Biodiversitas 23: 3620-3630. Along with the dynamics of rapid social change and the need for land for economic development, land and climate parameters on changes in the designation and function of forest areas, it is time to integrate with socio-economic parameters and government policies while still paying attention to the condition of existing forest vegetation cover. This study aimed to develop a spatial analysis model of changes in the designation and function of forest areas based on multi-criteria in dry land and mangrove forest ecosystems. The research used a descriptive-spatial method with a multi-criteria-based decision-making technique for changes in the designation and function of forest areas. The data analysis utilizes Geographic Information System. The motivation for the proposed forest area change is the target to be achieved in the multi-criteria decision analysis. This research resulted in a spatial analysis approach that integrates scoring techniques and multi-criteria-based decision-making techniques. In Locus BL_010, a change in the function of forest area can be generated, while in Locus TL_003 a decision is made to change the allocation and function of forest area. The achievement of multi-criteria-based spatial analysis on changes in the designation and function of forest areas is quite flexible in decision making because each proposed motivational target will be achieved if it is in accordance with the established criteria and field facts.

Using Spatial Data Science in Energy-Related Modeling of Terraforming the Martian Atmosphere

This paper proposes a methodology for numerical modeling of terraforming Mars’ atmosphere using high-energy asteroid impact and greenhouse gas production processes. The developed simulation model uses a spatial data science approach to analyze the Global Climate Model of Mars and cellular automata to model the changes in Mars’ atmospheric parameters. The developed model allows estimating the energy required to raise the planet’s temperature by sixty degrees using different variations of the terraforming process. Using a data science approach for spatial big data analysis has enabled successful numerical simulations of global and local atmospheric changes on Mars and an analysis of the energy potential required for this process.

Spatial analysis approach for sustainability of Campus Radio in Malaysia: Case study of UMS KKFM Campus Radio

The campus radio in Malaysia usually established for educational purpose which is important for the student’s development in soft skills. Unfortunately, some campus radio is regarded as not popular and irrelevant due to the emergence of the Internet. Thus, this leads to low utilization of campus radio among students and the public. Although, there are listeners to the campus radio but it is difficult to identify the distribution pattern especially the location of the listeners. Therefore, this study was conducted using spatial analysis to determine the distribution pattern of radio listeners specifically towards campus radio in Universiti Malaysia Sabah (UMS) KKFM. The study was based on the perceptions of the respondents reside around the area of UMS with distance within 15km. Techniques such as interpolation, hotspot and geographic distribution measurement were applied in this study using ArcGIS software. Based on the result, the spatial pattern shows that the public and those reside far from UMS are among the regular listeners of the campus radio. Finally, the outcome of this study indicates that the campus radio is still relevant specifically for certain programmes. This study is important for service improvement and to achieve sustainability of the campus radio.

Proposed flood evacuation routes for heritage areas based on spatial configuration analysis: a case study of Penang, Malaysia

PurposeBecause of the global increase of climate change effects, floods are becoming more frequent and severer, especially in urban areas of coastal cities and islands where floodplains have turned into buildings because of rapid urbanisation, leading to a higher risk of damages. Urban heritage areas should be highly considered in the time of evacuation because of the vulnerability of streets and buildings and limitations on taking counteractions. Given these limitations, this study aims to propose a network of potential evacuation routes based on spatial configuration analysis of the heritage areas.Design/methodology/approachPenang Island's heritage site, namely, George Town, located on the northwest coast of Malaysia, is chosen as the case study. By using an approach of spatial configuration analysis using space syntax techniques in addition to considering the potential starting points for evacuation and flood risk map of the area, this study analysed the area's street network values for evacuation function during flood crisis time.FindingsPotential evacuation routes were identified for flood disasters in the George Town heritage area. Furthermore, the proposed evacuation routes were evaluated in terms of time for evacuation by metric step-depth analysis of space syntax.Originality/valueA few studies have focused on practical guidelines for evacuation routes based on spatial configuration analysis, an important yet neglected approach in this regard, especially concerning urban island areas. This study can contribute to providing strategies to reduce vulnerability and casualties in urban heritage areas.

SPF: A spatial and functional data analytic approach to cell imaging data.

The tumor microenvironment (TME), which characterizes the tumor and its surroundings, plays a critical role in understanding cancer development and progression. Recent advances in imaging techniques enable researchers to study spatial structure of the TME at a single-cell level. Investigating spatial patterns and interactions of cell subtypes within the TME provides useful insights into how cells with different biological purposes behave, which may consequentially impact a subject's clinical outcomes. We utilize a class of well-known spatial summary statistics, the K-function and its variants, to explore inter-cell dependence as a function of distances between cells. Using techniques from functional data analysis, we introduce an approach to model the association between these summary spatial functions and subject-level outcomes, while controlling for other clinical scalar predictors such as age and disease stage. In particular, we leverage the additive functional Cox regression model (AFCM) to study the nonlinear impact of spatial interaction between tumor and stromal cells on overall survival in patients with non-small cell lung cancer, using multiplex immunohistochemistry (mIHC) data. The applicability of our approach is further validated using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset.

KLFDAPC: a supervised machine learning approach for spatial genetic structure analysis.

Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect and describe them is principal component analysis (PCA) or the supervised linear discriminant analysis of principal components (DAPC). However, genetic features produced from both approaches could fail to correctly characterize population structure for complex scenarios involving admixture. In this study, we introduce Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC), a supervised non-linear approach for inferring individual geographic genetic structure that could rectify the limitations of these approaches by preserving the multimodal space of samples. We tested the power of KLFDAPC to infer population structure and to predict individual geographic origin using neural networks. Simulation results showed that KLFDAPC has higher discriminatory power than PCA and DAPC. The application of our method to empirical European and East Asian genome-wide genetic datasets indicated that the first two reduced features of KLFDAPC correctly recapitulated the geography of individuals and significantly improved the accuracy of predicting individual geographic origin when compared to PCA and DAPC. Therefore, KLFDAPC can be useful for geographic ancestry inference, design of genome scans and correction for spatial stratification in GWAS that link genes to adaptation or disease susceptibility.

Do underserved and socially vulnerable communities observe more crashes? A spatial examination of social vulnerability and crash risks in Texas

Understanding the relationship between social vulnerability and traffic crashes is a cornerstone for promoting social justice in transportation planning and policymaking. However, few studies have examined the disparities in traffic crashes by systemically considering the influence of social vulnerability via spatial analysis approaches. This study puts forward a new approach to assess the inequity in transportation safety by spatially examining the relationships between crash risks and the social vulnerability index (SVI) established by the Centers for Disease Control and Prevention (CDC). We performed spatial autocorrelation analyses to identify the clusters of high-risk and high-vulnerable census tracts in Texas. Meanwhile, we innovatively applied the Multiscale Geographically Weighted Regression model (MGWR) to assess the impacts of CDC SVI on crash risks spatially and statistically. The results demonstrate that the crash rate and the social vulnerability are significantly correlated in the highly urbanized regions as well as the southern border along the Rio Grande in Texas. The MGWR results indicate the minority status of census tracts is strongly correlated with overall crashes in north-central and northeastern Texas, and the socioeconomic status is tightly correlated with fatal crashes across Texas. The outcomes from this study have significant implications for transportation planning and policymaking.

An Effective Trend Surface Fitting Framework for Spatial Analysis of Extreme Events

AbstractThe estimation of exceedance probabilities for extreme climatic events is critical for infrastructure design and risk assessment. Climatic events occur over a greater space than they are measured with point‐scale in situ gauges. In extreme value theory, the block maxima approach for spatial analysis of extremes depends on properly modeling the spatially varying Generalized Extreme Value marginal parameters (i.e., trend surfaces). Fitting these trend surfaces can be challenging since there are numerous spatial and temporal covariates that are potentially relevant for any given event type and region. Traditionally, covariate selection is based on assumptions regarding the topmost relevant drivers of the event. This work demonstrates the benefit of utilizing elastic‐net regression to support automatic selection from a relatively large set of physically relevant covariates during trend surface estimation. The trend surfaces presented are based on 24‐hr annual maximum precipitation for northeastern Colorado and the Texas‐Louisiana Gulf Coast.

Detailed spatial analysis of renewables’ potential and heat: A study of Groningen Province in the northern Netherlands

Spatially sensitive regional renewables’ potentials are greatly influenced by existing land-use claims and related spatial and environmental policies. Similarly, heat particularly related to low-temperature demand applications in the built environment (BE) is highly spatially explicit. This study developed an analytical approach for a detailed spatial analysis of future solar PV, onshore wind, biomass, and geothermal and industrial waste heat potentials at a regional level and applied in the Dutch Province of Groningen. We included spatial policies, various spatial claims, and other land-use constraints in developing renewable scenarios for 2030 and 2050. We simultaneously considered major spatial claims and multiple renewable energy sources. Claims considered are the BE, agriculture, forest, nature, and network and energy infrastructure, with each connected to social, ecological, environmental, technical, economic, and policy-related constraints. Heat demand was further analyzed by creating highly granular demand density maps, comparing them with regional heat supply potential, and identifying the economic feasibility of heat networks. We analyzed the possibilities of combining multiple renewables on the same land. The 2050 renewable scenarios results ranged 2–66 PJ for solar PV and 0–48 PJ for onshore wind and biomass ranged 3.5–25 PJ for both 2030 and 2050. These large ranges of potentials show the significant impact of spatial constraints and underline the need for understanding how they shape future energy policies. The heat demand density map shows that future heat networks are feasible in large population centers. Our approach is pragmatic and replicable in other regions, subject to data availability.