The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

This graph shows how the impact factor of ^ is computed. The left axis depicts the number of papers published in years X-1 and X-2, and the right axis displays their citations in year X.

Geospatial Analytics in Construction Market Outlook

As per our latest research, the global geospatial analytics in construction market size reached USD 8.92 billion in 2024, demonstrating robust expansion driven by the rapid digital transformation of the construction sector. The market is expected to grow at a CAGR of 13.2% from 2025 to 2033, projecting a substantial increase to USD 27.14 billion by 2033. This growth is fueled by the rising adoption of advanced geospatial technologies, the increasing need for optimized resource allocation, and the demand for real-time project monitoring and risk mitigation strategies across construction projects worldwide.

The primary growth factor for the geospatial analytics in construction market is the escalating integration of digital technologies within construction workflows. The construction sector, traditionally reliant on manual processes, is now embracing advanced geospatial solutions such as Geographic Information Systems (GIS), remote sensing, and Building Information Modeling (BIM). These technologies enable precise mapping, spatial analysis, and visualization, which are crucial for efficient site selection, asset management, and project planning. The ability to overlay geospatial data with construction plans significantly enhances decision-making, reduces operational risks, and streamlines resource allocation, thereby driving market growth. Furthermore, the growing emphasis on smart city initiatives and sustainable infrastructure is accelerating the adoption of geospatial analytics, as stakeholders seek data-driven insights to optimize urban development and minimize environmental impacts.

Another key driver propelling the geospatial analytics in construction market is the increasing complexity and scale of construction projects globally. Urbanization, population growth, and the surge in infrastructure investments have led to larger, more intricate projects that require advanced analytics for effective execution. Geospatial analytics provides construction firms with the tools to monitor progress in real time, assess site conditions, and identify potential risks before they escalate. This proactive approach not only improves project timelines and cost efficiency but also ensures compliance with regulatory standards. The proliferation of drones, IoT sensors, and cloud-based platforms further enhances the accessibility and utility of geospatial data, enabling seamless collaboration among project stakeholders and driving higher adoption rates across the industry.

The evolution of regulatory frameworks and the growing focus on environmental sustainability are also shaping the trajectory of the geospatial analytics in construction market. Governments and regulatory bodies worldwide are mandating the use of advanced geospatial technologies to ensure compliance with environmental standards and to assess the impact of construction activities on ecosystems. This has led to increased investments in geospatial analytics for environmental impact analysis, risk assessment, and disaster management. Additionally, the integration of artificial intelligence and machine learning with geospatial analytics is unlocking new possibilities for predictive modeling, automated anomaly detection, and optimized resource utilization, further boosting market growth.

The integration of Geospatial BIM Integration Service is becoming increasingly vital in the construction industry. By combining geospatial data with Building Information Modeling (BIM), construction firms can achieve a more comprehensive understanding of project sites and infrastructure. This integration allows for enhanced visualization and analysis, enabling stakeholders to make informed decisions based on accurate spatial data. The synergy between geospatial analytics and BIM facilitates improved collaboration among project teams, as it provides a unified platform for sharing and analyzing data. As construction projects grow in complexity, the demand for integrated solutions that streamline workflows and enhance project outcomes is on the rise. The Geospatial BIM Integration Service is poised to play a crucial role in addressing these needs, driving innovation and efficiency in the construction sector.

From a regional perspective, North America currently dominates the geospatial analytics in construction market, accounting for

Results for Bayesian binary probit and Bayesian spatial binary probit models in Clark County.

This research study analysed the crime rate spatially and it examined the relationship between crime and spatial factors in Saudi Arabia. It reviewed the related literature that has utilised crime mapping techniques, such as Geographic Information Systems (GIS) and remote sensing (RS); these techniques are a basic part of effectively helping security and authority agencies by providing them with a clear perception of crime patterns and a surveillance direction to track and tackle crime. This study analysed the spatial relationships between crime and place, immigration, changes in urban areas, weather and transportation networks. The research study was divided into six parts to investigate the correlation between crime and these factors. The first part of the research study examined the relationship between crime and place across the 13 provinces of Saudi Arabia using GIS techniques based on population density in order to identify and visualise the spatial distributions of national and regional crime rates for drug crimes, thefts, murders, assaults, and alcohol-related and ‘outrageous crimes’ (offences against Islam) over a 10-year period from 2003 to 2012. Social disorganisation theory was employed to guide the study and explain the diversity in crime patterns across the country. The highest rates of overall crimes were identified in the Northern Borders Province and Jizan, which are located in the northern and southern regions of the country, respectively; the eastern area of the country was found to have the lowest crime rate. Most drug offences occurred in the Northern Borders Province and Jizan; high rates of theft were recorded in the Northern Borders Province, Jouf Province and Makkah Province, while the highest rates of homicide occurred in Asir Province. The second part of the research study aimed to determine the trends of overall crime in relation to six crime categories: drug-related activity, theft, murder, assault, alcohol-related crimes and outrageous or sex-related crimes, in Saudi Arabia’s 13 provinces over a 10-year period from 2003 to 2012. The study analysed the spatial and temporal changes of criminal cases. Spatial changes were used to determine the differences over the time period of 2003–2012 to show the provincial rates of change for each crime category. Temporal changes were used to compute the trends of the overall crime rate and crimes in the six categories per 1,000 people per year. The results showed that the overall crime rate increased steadily until 2008; thereafter it decreased in all areas except for the Northern Borders Province and Jizan, which recorded the highest crime rates throughout the study period. We have explained that decrease in terms of changes in wages, support for the unemployed and service improvements, which were factors that previous studies also emphasised as being the primary cause for the decrease. This study includes a detailed discussion to contribute to the understanding of the changes in the crime rates in these categories throughout this period in the 13 provinces of Saudi Arabia. The third part of the research study aimed to explain the effects of immigration on the overall crime rate in the six most significant categories of crime in Saudi Arabia, which are drug-related activity, theft, murder, assault, alcohol-related crimes and outrageous crimes, during a 10-year period from 2003 to 2012, in all 13 administrative provinces. It also sought to identify the provinces most affected by the criminal activities of immigrants during this period. No positive association between immigrants and criminal cases was found. It was clearly visible that the highest rate of overall criminal activities was in the south, north and Makkah areas, where there is a high probability of illegal immigrants. This finding supports the basic criminological theory that areas with high levels of immigrants also experience high rates of crime. The study’s results provide recommendations to the Saudi government, policy-makers, decision-makers and immigration authorities, which could assist in reducing crimes perpetrated by immigrants. In the fourth part of the research study, urban areas were examined in relation to crime rates. Urban area expansion is one of the most critical types of worldwide change, and most urban areas are experiencing increased population growth and infrastructure development. Urban change leads to many changes in the daily activities of people living within an affected area. Many studies have suggested that urbanisation and crime are related. However, those studies focused on land uses, types of land use and urban forms, such as the physical features of neighbourhoods, roads, shopping centres and bus stations. It is very important for criminologists and urban planning decision-makers to understand the correlation between urban area expansion and crime. In this research, satellite images were used to measure urban expansion over a 10-year period; the study tested the correlations between these expansions and the number of criminal activities within these specific areas. The results show that there is a measurable relationship between urban expansion and criminal activities. The findings support the crime opportunity theory as one possibility, which suggests that population density and crime are conceptually related. Moreover, the results show that the correlations are stronger in areas that have undergone greater urban growth. This study did not evaluate many other factors that might affect the crime rate, such as information on the spatial details of the population, city planning, economic considerations, the distance from the city centre, the quality of neighbourhoods, and the number of police officers. However, this research will be of particular interest to those who aim to use remote sensing to study crime patterns. The fifth part of the research study investigated the impacts of weather on crime rates in two different cities: Riyadh and Makkah. While a number of studies have examined climate influences on crime and human behaviour by investigating the correlation between climate and weather elements, such as temperature, humidity and precipitation, and crime rates, few studies have focused on haze as a weather element and its correlation with crime. This research examined haze as a weather variable to investigate its effects on criminal activity and compare its effects with those of temperature and humidity. Monthly crime data and monthly weather records were used to build a regression model to predict crime cases based on three weather factors using temperature, humidity and haze values. This model was applied to two provinces in Saudi Arabia with different types of climates: Riyadh and Makkah. Riyadh Province is a desert area in which haze occurs approximately 17 days per month on average. Makkah Province is a coastal area where it is hazy an average of 4 days per month. A measurable relationship was found between each of these three variables and criminal activity. However, haze had a greater effect on theft, drug-related crimes and assault in Riyadh Province than temperature and humidity. Temperature and humidity were the efficacious variables in Makkah Province, while haze had no significant influence in that region. Finally, the sixth part of the research study examined the influence of the quality and extent of road networks on crime rates in both urban and rural areas in Jizan Province, Saudi Arabia. We performed both Ordinary Least Squares regression (OLS) and Geographically Weighted Regression (GWR) where crime rate was the dependent variable and paved (sealed) roads, non-paved (unsealed/gravel) roads and population density were the explanatory variables. Population density was a control variable. The findings reveal that, across all 14 districts in that province, the districts with better quality paved road networks had lower rates of crime than the districts with unpaved roads. Furthermore, the more extensive the road networks, the lower the crime rate whether or not the roads were paved. These findings concur with those reported in studies conducted in other countries, which revealed that rural areas are not always the safe, crime-free places they are often believed to be. This research contributes knowledge about the geographical information of criminal movement, and it offers some conceivable reasons for crime rates and patterns in relation to the spatial factors and the socio-cultural perspectives of Saudi Arabian life. More geographical research is still needed in terms of criminology, which will provide a better understanding of crime patterns, particularly in Saudi Arabia, and across the globe, where the spatial distribution of criminal cases is an essential base in crime research. Furthermore, additional studies are needed to investigate the complex interventions of the effect of different spatial variables on crime and the uncertainties correlation with the impact of environmental factors. This can help predict the impact of socioeconomic and environmental factors. The greater part of such an investigation will enhance the understanding of crime patterns, which is imperative for advancing a framework that can be used to address crime reduction and crime prevention.

This study tried to focus on the older drivers’ group and explore the impact factors of injury severity involving older drivers from geo-spatial analysis. To reach the goal, a spatial analysis was proposed employing geographic information systems (GIS) with a case study application to two counties in Nevada. First, crash clusters were explored using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) approach to investigate the spatial crash pattern for older drivers, and determine high risk locations of injury severity. Next, Bayesian spatial binary probit model was presented in order to determine the significant impact factors of injury severity involving older drivers. It was found that at-fault driver condition and vehicle condition, not-at-fault vehicle action and road factors were significant factors for injury severity of older drivers. Results revealed that DBSCAN provides a solid option for hotspot identification of injury severity and Bayesian spatial binary probit model addresses the factor determinants spatially. The GIS-based spatial analysis can benefit more reliable older driver-concentrated evaluation and injury severity analysis.

This service provides information on noise and vibration investigated based on the environmental impact assessment project area and survey point of the National Institute of Environmental Research of the Ministry of Environment, through noise and vibration attribute inquiry, data with spatial information (survey point WFS, survey point WMS), and attributes (survey point, survey number, address, vibration (dB (V)), survey number, survey start date, survey end date, noise (dB (A)), Y coordinate, X coordinate, survey point name) and feature information of noise and vibration survey points based on spatial coordinates (spatial coordinate data of features, index values constituting spatial data, environmental impact assessment project code, project district division, project point name, method of constructing the corresponding spatial data, validity of the corresponding spatial data value, length for the corresponding space, area for the corresponding space), and provides information through point image inquiry of the noise and vibration survey point based on the corresponding spatial coordinates, such as the tag system, return image type, presence or absence of a return background image, return image width/height, and size.

Spatial Analytics Platform Market Outlook

According to our latest research, the global Spatial Analytics Platform market size reached USD 4.9 billion in 2024, with a robust year-on-year growth and a projected compound annual growth rate (CAGR) of 13.7% from 2025 to 2033. By the end of 2033, the market is forecasted to reach approximately USD 15.1 billion, driven primarily by the increasing adoption of location-based analytics across various sectors and the rising integration of spatial data with business intelligence platforms. The growth momentum is further fueled by technological advancements in geospatial data collection and analysis, as well as the growing need for real-time decision-making capabilities in urban planning, transportation, and environmental monitoring.

A significant growth factor in the Spatial Analytics Platform market is the exponential increase in geospatial data generation from IoT devices, mobile applications, and satellite imagery. Organizations across sectors are leveraging spatial analytics to derive actionable insights from this data, enhancing operational efficiency, customer engagement, and risk management. The proliferation of smart cities and urban infrastructure projects globally has further accelerated the adoption of spatial analytics platforms, enabling governments and enterprises to optimize resource allocation, monitor environmental impact, and improve citizen services. The integration of spatial analytics with AI and machine learning has also unlocked new possibilities for predictive analysis, automation, and scenario modeling, making these platforms indispensable for data-driven decision-making.

Another critical driver for the spatial analytics platform market is the growing demand for real-time location intelligence in transportation, logistics, and supply chain management. Companies are increasingly utilizing spatial analytics to optimize routes, track assets, and forecast demand, leading to cost savings and improved service levels. In the retail and BFSI sectors, spatial analytics platforms are being deployed to enhance market segmentation, site selection, and personalized marketing strategies, thereby improving customer experiences and driving revenue growth. The healthcare industry is also embracing spatial analytics to track disease outbreaks, optimize facility locations, and improve patient outcomes, highlighting the versatile applications of these platforms across diverse verticals.

Furthermore, the market is witnessing a surge in investments and partnerships among technology providers, data aggregators, and end-users to develop advanced spatial analytics solutions. The emergence of cloud-based spatial analytics platforms has democratized access to sophisticated geospatial tools, enabling small and medium enterprises (SMEs) to leverage spatial intelligence without significant upfront investments. The continuous evolution of data visualization techniques, coupled with the increasing availability of open-source geospatial datasets, is expected to further propel market growth. However, data privacy concerns and the complexity of integrating spatial analytics with legacy systems remain challenges that industry players must address to ensure sustained adoption.

From a regional perspective, North America currently holds the largest share of the global spatial analytics platform market, followed closely by Europe and the Asia Pacific. The dominance of these regions can be attributed to the presence of leading technology vendors, high digital maturity, and significant investments in smart infrastructure and urban development projects. The Asia Pacific region is anticipated to exhibit the fastest growth during the forecast period, driven by rapid urbanization, government initiatives for digital transformation, and the increasing adoption of spatial analytics in emerging economies such as China and India. Latin America and the Middle East & Africa are also expected to witness steady growth, supported by infrastructure modernization and the rising need for efficient resource management.

This graph shows how the impact factor of ^ is computed. The left axis depicts the number of papers published in years X-1 and X-2, and the right axis displays their citations in year X.

The purpose of the study was to better understand the factors associated with police decisions to make an arrest or not in cases of heterosexual partner violence and how these decisions vary across jurisdictions. The study utilized data from three large national datasets: the National Incident-Based Reporting System (NIBRS) for the year 2003, the Law Enforcement Management and Administrative Statistics (LEMAS) for the years 2000 and 2003, and the United States Department of Health and Human Services Area Resource File (ARF) for the year 2003. Researchers also developed a database of domestic violence state arrest laws including arrest type (mandatory, discretionary, or preferred) and primary aggressor statutes. Next, the research team merged these four databases into one, with incident being the unit of analysis. As a further step, the research team conducted spatial analysis to examine the impact of spatial autocorrelation in arrest decisions by police organizations on the results of statistical analyses. The dependent variable for this study was arrest outcome, defined as no arrest, single male arrest, single female arrest, and dual arrest for an act of violence against an intimate partner. The primary independent variables were divided into three categories: incident factors, police organizational factors, and community factors.

Animal mortality caused by vehicle collisions is one of the main ecological impacts of roads. Amphibians are the most affected group and road fatalities have a significant impact on population dynamics and viability. Several studies on Iberian amphibians have shown the importance of country roads on amphibian road mortality, but still, little is known about the situation in northern Portugal. By being more permeable to amphibian passage, country roads represent a greater source of mortality than highways, which act as barriers. Thus, mitigation measures should be applied, but due to the extensive road network, the identification of precise locations (hotspots) and variables related to animal-vehicle collision is needed to plan these measures successfully. The aim of the study was to analyse the spatial occurrence and related factors linked to amphibian mortality on a number of country roads in northern Portugal, using spatial statistics implemented in GIS and applying a binary logistical regression. We surveyed 631 km of road corresponding to seven transects, and observed 404 individual amphibians: 74 (18.3%) alive and 330 (81.7%) road-killed. Bufo bufo represented 80% of the mortality records. Three transects showed clustered distribution of road-kills, and broadleaved forests and road ditches were the most important factors associated with hotspots of road-kill. Logistic regression models showed that habitat quality, Bufo bufo’s habitat preferences, and road ditches are positively associated with amphibians’ road mortality in northern Portugal, whereas average altitude and length of walls were negatively associated. This study is a useful tool to understand spatial occurrence of amphibian road-kills in the face of applying mitigation measures on country roads from northern Portugal. This study also considers the necessity of assessing the condition of amphibian local populations to understand their road-kills spatial patterns and the urgency to apply mitigation measures on country roads. Palabras clave: Amphibian, Mortality

Spatial Mapping Software Market Outlook

As per our latest research, the global spatial mapping software market size in 2024 stands at USD 7.2 billion, with a robust compound annual growth rate (CAGR) of 13.7% projected through 2033. By the end of 2033, the market is forecasted to reach a valuation of USD 22.1 billion. This impressive growth trajectory is primarily driven by the increasing adoption of location-based services, the proliferation of smart city initiatives, and the rising demand for geospatial analytics across various industries. The market is experiencing significant momentum as organizations seek advanced solutions for spatial data visualization, real-time mapping, and efficient resource management, thereby fueling the expansion of spatial mapping software globally.

The rapid digital transformation across industries is a major growth factor for the spatial mapping software market. As businesses and governments increasingly rely on data-driven decision-making, the ability to visualize, analyze, and interpret spatial data has become essential. Urbanization and the expansion of smart cities are creating a surge in demand for mapping solutions that enable planners and administrators to optimize infrastructure, manage assets, and monitor environmental impact. Furthermore, the integration of spatial mapping software with emerging technologies such as artificial intelligence, Internet of Things (IoT), and 5G networks is enhancing the precision and real-time capabilities of these platforms. This convergence is paving the way for innovative applications in areas such as autonomous vehicles, disaster response, and precision agriculture, further propelling market growth.

Another significant driver for the spatial mapping software market is the growing need for efficient asset management and risk mitigation. Organizations across sectors such as utilities, transportation, and defense are leveraging spatial mapping software to monitor and manage critical assets, detect anomalies, and ensure operational continuity. The ability to overlay real-time data on geographic maps provides unparalleled situational awareness, enabling quick and informed decision-making. Additionally, advancements in cloud computing have democratized access to sophisticated mapping tools, allowing even small and medium enterprises to benefit from spatial analytics without substantial infrastructure investments. The trend towards remote work and distributed operations post-pandemic has also accelerated the adoption of cloud-based mapping solutions, making spatial mapping an integral part of modern enterprise workflows.

Environmental monitoring and disaster management represent pivotal growth avenues for the spatial mapping software market. Climate change, urban sprawl, and natural disasters necessitate advanced solutions for tracking environmental changes, predicting hazards, and coordinating emergency responses. Spatial mapping software is being utilized to model flood zones, monitor deforestation, and track pollution, providing governments and organizations with actionable insights for sustainable development and disaster resilience. The increasing frequency and intensity of natural disasters globally have heightened the importance of real-time geospatial intelligence, driving investments in mapping technologies. As environmental regulations become stricter and public awareness grows, the demand for spatial mapping solutions in environmental monitoring is expected to remain strong throughout the forecast period.

The integration of Spatial Mapping Processor technology is revolutionizing the capabilities of spatial mapping software. This advanced processor enhances the speed and accuracy of data processing, allowing for more detailed and real-time analysis of spatial data. By leveraging the power of spatial mapping processors, organizations can achieve higher precision in mapping applications, which is crucial for sectors such as autonomous vehicles and smart city planning. The processor's ability to handle complex algorithms efficiently is enabling new levels of innovation in geospatial analytics, providing users with deeper insights and improved decision-making capabilities. As the demand for high-performance mapping solutions grows, the role of spatial mapping processors in driving technological advancements cannot be overstated.

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Geospatial Imagery Analytics Market Outlook

According to our latest research, the global geospatial imagery analytics market size reached USD 12.1 billion in 2024, reflecting robust expansion driven by the integration of advanced analytics and imaging technologies across various sectors. The market is projected to grow at a CAGR of 22.8% from 2025 to 2033, with the forecasted market size expected to reach USD 65.2 billion by 2033. This growth is attributed to the increasing demand for real-time geospatial data, advancements in satellite and drone imaging, and the rising adoption of cloud-based analytics platforms. As per our latest research, the proliferation of artificial intelligence and machine learning in image processing is acting as a key catalyst, enabling organizations to derive actionable insights from vast volumes of geospatial data.

A significant growth factor for the geospatial imagery analytics market is the rapid evolution of imaging technologies, particularly in satellite and aerial platforms. The deployment of high-resolution satellites, drones, and unmanned aerial vehicles (UAVs) has revolutionized the way organizations capture and analyze spatial data. These technologies enable the collection of detailed images and videos, which can be analyzed to monitor environmental changes, assess infrastructure, and manage natural resources effectively. The integration of AI and deep learning algorithms further enhances the accuracy and predictive power of geospatial analytics, allowing for real-time decision-making in critical sectors such as defense, disaster management, and urban planning. As organizations increasingly recognize the value of geospatial insights for operational efficiency and strategic planning, the demand for advanced imagery analytics solutions continues to surge globally.

Another key driver is the expanding application of geospatial imagery analytics in sectors such as agriculture, energy, and environmental monitoring. In agriculture, geospatial analytics help optimize crop yield, monitor soil health, and manage irrigation systems through precise mapping and predictive modeling. Energy and utilities companies leverage these technologies to monitor pipelines, assess vegetation encroachment, and plan infrastructure development, thereby minimizing risks and operational costs. Environmental agencies are utilizing geospatial imagery analytics to track deforestation, monitor wildlife habitats, and assess the impact of climate change. The ability to process and analyze large volumes of spatial data in near real-time is transforming how these industries operate, leading to improved resource management and sustainability outcomes. The growing emphasis on data-driven decision-making across industries is expected to further propel the adoption of geospatial imagery analytics.

The surge in cloud computing and the availability of scalable analytics platforms have also played a pivotal role in the market’s expansion. Cloud-based geospatial imagery analytics solutions offer unparalleled flexibility, enabling organizations to process and store vast datasets without significant upfront investments in infrastructure. This has democratized access to advanced analytics tools, allowing small and medium enterprises (SMEs) to harness the power of geospatial data alongside large corporations. The shift towards cloud deployment is also fostering greater collaboration and data sharing among stakeholders, facilitating more comprehensive and integrated analyses. As regulatory frameworks around data privacy and security become more robust, the adoption of cloud-based geospatial analytics is expected to accelerate, further fueling market growth in the coming years.

Regionally, North America continues to dominate the geospatial imagery analytics market, owing to substantial investments in satellite infrastructure, technological innovation, and widespread adoption across defense, government, and commercial sectors. Europe follows closely, benefiting from strong regulatory support and research initiatives in geospatial technologies. The Asia Pacific region is emerging as a high-growth market, driven by rapid urbanization, infrastructure development, and increasing governmental focus on smart city initiatives. Countries such as China, India, and Japan are making significant strides in integrating geospatial analytics into national development projects. Latin America and the Middle East & Africa are also witnessing growing adoption, particularly in environmental monitoring

The aim of this study is to discuss and analyze key factors that affect the desertification in Pa Deng Sub-district, Thailand, in order to assess the desertification risk of the sites. The MEDALUS Model was used to conduct the desertification risk assessment. The spatial analysis study was done with Geographic Information System (GIS) and Remote Sensing (RS) programs. The key factors that had an impact on the desertification in Pa Deng area are climatic factor, soil factors (soil texture, fertility and erosion) and human activity factor (land use). The results revealed that the majority of the plain area in Pa Deng was at moderate desertification risk. The critical factor that increased the risk of decertification was soil erosion.

As China’s political and economic centre, the Beijing–Tianjin–Hebei (BTH) urban agglomeration experiences serious environmental challenges on particulate matter (PM) concentration, which results in fundamental or irreparable damages in various socioeconomic aspects. This study investigates the seasonal and spatial distribution characteristics of PM2.5 concentration in the BTH urban agglomeration and their critical impact factors. Spatial interpolation are used to analyse the real-time monitoring of PM2.5 data in BTH from December 2013 to May 2017, and partial least squares regression is applied to investigate the latest data of potential polluting variables in 2015. Several important findings are obtained: (1) Notable differences exist amongst PM2.5 concentrations in different seasons; January (133.10 mg/m3) and December (120.19 mg/m3) are the most polluted months, whereas July (38.76 mg/m3) and August (41.31 mg/m3) are the least polluted months. PM2.5 concentration shows a periodic U-shaped variation pattern with high pollution levels in autumn and winter and low levels in spring and summer. (2) In terms of spatial distribution characteristics, the most highly polluted areas are located south and east of the BTH urban agglomeration, and PM2.5 concentration is significantly low in the north. (3) Empirical results demonstrate that the deterioration of PM2.5 concentration in 2015 is closely related to a set of critical impact factors, including population density, urbanisation rate, road freight volume, secondary industry gross domestic product, overall energy consumption and industrial pollutants, such as steel production and volume of sulphur dioxide emission, which are ranked in terms of their contributing powers. The findings provide a basis for the causes and conditions of PM2.5 pollution in the BTH regions. Viable policy recommendations are provided for effective air pollution treatment.

As the world becomes more and more urbanized, it is increasingly important to understand the impacts of urban landscapes on biodiversity. Urbanization can change local habitat factors and decrease connectivity among local habitats, with major impacts on the structure of natural food webs. However, most studies have focused on single species, or compared rural to urban habitats, which do not inform us on how to design and manage cities to optimize biodiversity. To understand the local and spatial drivers of ecological communities within urban landscapes, we assessed the relative impact of local habitat factors (sunlight exposure and leaf litter) and spatial connectivity on an oak-associated herbivore community within an urban landscape. From the local habitat factors, leaf litter but not sunlight exposure was related to herbivore species richness, with leaf litter contributing to the maintenance of high species richness on isolated trees. Guilds and species differed strongly in their res...

Modelling changes in biodiversity has become a necessary component of smart urban planning practices. However, concepts such as biodiversity are often evaluated using area-based composite indices, the results of which are heavily reliant on specific parameters chosen. This paper explores the design and implementation of a butterfly biodiversity index by comparing two widely accepted modelling techniques: principal component analysis and spatial multi-criteria decision analysis (MCDA). A high degree of scale dependency has been demonstrated in previous studies exploring the use of area-based composite measures. To evaluate the impact of scale, each model was assessed at two different spatial resolutions. The outcomes were analyzed, mapped and compared using ordinary least squares, geographically weighted regression and global Moran’s I to evaluate relative biodiversity patterns across the City of Toronto, Canada.

The Urban Butterfly Index - City of Toronto includes a geodatabase that consists of shapefiles of various geographic information used to model urban biodiversity and the butterfly observation points provided by the Ontario Butterfly Atlas. The Ontario Butterfly Atlas (OBA) is a program created and administered by the Toronto Entomologists’ Association (TEA), a non-profit organization that aims to educate and inform the public about local insect populations. Volunteers submit species observations throughout the year including important attribute data such as location (i.e., GPS coordinates or detailed location description to be verified), species name, observation date, adult and immature counts (TEA 2019). The OBA dataset was used as the benchmark variable in each regression analysis performed (i.e., species abundance or richness per area). Each geographic dataset was aggregated and summed by areal unit (i.e., Census Tracts or Dissemination Areas) and combined into a set of composite index scores using either principal component analysis (PCA) or spatial multi-criteria decision analysis (i.e., Weighted Linear Combination).

Tabular files used in the Ordinary Least Squares (OLS) and Geographic Weighted Regression (GWR) are also provided and include the benchmark variable of butterfly observations per area (i.e. SA07_Ha and SR07_Ha), as well as final PCA and WLC scores. Summary text files of the regression results have also been provided for reference.

Findings indicate that the impact of spatial scale was significant, whereby the coarser resolution models (i.e., Census Tracts) were found to be more highly correlated with biodiversity, compared to the finer resolution models (i.e., Dissemination Areas). The results of this study contribute to a growing body of literature that explores key conceptual questions regarding the robustness of GIS-based MCDA, the impact of scale in urban ecology studies, and the use of composite indices to manage spatial ecological data.

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The spatial light modulator market share should rise by USD 239.06 million from 2022 to 2026 at a CAGR of 8.41%.

This spatial light modulator market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers market segmentations by technology (equal or more than 1024 X 768 pixels resolution and less than 1024 X 768 pixels resolution) and geography (APAC, North America, Europe, MEA, and South America). The spatial light modulator market report also offers information on several market vendors, including Hamamatsu Photonics KK, HOLOEYE Photonics AG, Jenoptik AG, Kopin Corp., Laser 2000 UK Ltd., Meadowlark Optics Inc., PerkinElmer Inc., Santec Corp., Texas Instruments Inc., and Thorlabs Inc. among others.

What will the Spatial Light Modulator Market Size be in 2022?

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Spatial Light Modulator Market: Key Drivers and Trends

The rapid utilization of holograms and projectors in the retail sector is notably driving the spatial light modulator market growth, although factors such as lack of awareness regarding advantages of spatial light modulators (slms) may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the spatial light modulator market. The holistic analysis of the drivers will help in predicting end goals and refining marketing strategies to gain a competitive edge.

This spatial light modulator market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2022-2026.

Who are the Major Spatial Light Modulator Market Vendors?

The report analyzes the market’s competitive landscape and offers information on several market vendors, including:

Hamamatsu Photonics KK
HOLOEYE Photonics AG
Jenoptik AG
Kopin Corp.
Laser 2000 UK Ltd.
Meadowlark Optics Inc.
PerkinElmer Inc.
Santec Corp.
Texas Instruments Inc.
Thorlabs Inc.

The vendor landscape of the spatial light modulator market entails successful business strategies deployed by the vendors. The spatial light modulator market is fragmented and the vendors are deploying various organic and inorganic growth strategies to compete in the market.

To make the most of the opportunities and recover from post COVID-19 impact, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

Download a free sample of the spatial light modulator market forecast report for insights on complete key vendor profiles. The profiles include information on the production, sustainability, and prospects of the leading companies.

Which are the Key Regions for Spatial Light Modulator Market?

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41% of the market’s growth will originate from APAC during the forecast period. Japan, China, and South Korea (Republic of Korea) are the key markets for spatial light modulator market in APAC.

The report offers an up-to-date analysis of the geographical composition of the market. APAC has been recording significant growth rate and is expected to offer several growth opportunities to market vendors during the forecast period. Rising demand for high-resolution displays in consumer electronics, gaming devices, and advertising will facilitate the spatial light modulator market growth in APAC over the forecast period. The report offers an up-to-date analysis of the geographical composition of the market, competitive intelligence, and regional opportunities in store for vendors.

What are the Revenue-generating Technology Segments in the Spatial Light Modulator Market?

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The spatial light modulator market share growth by the equal or more than 1024 X 768 pixels resolution segment has been significant. This report provides insights on the impact of the unprecedented outbreak of COVID-19 on market segments. Through these insights, you can safely deduce transformation patterns in consumer behavior, which is crucial to gauge segment-wise revenue growth during 2022-2026 and embrace technologies to improve business efficiency.

This report provides an accurate prediction of the contribution of all the segments to the growth of the spatial light modulator market size. Furthermore, our analysts have indicated actionable market insights on post COVID-19 impact on each segment, which is crucial to predict cha

Results for Bayesian binary probit and Bayesian spatial binary probit models in Washoe.

Geographic Information System (GIS) Market Outlook

The Geographic Information System (GIS) market is witnessing robust growth with its global market size projected to reach USD 25.7 billion by 2032, up from USD 8.7 billion in 2023, at a compound annual growth rate (CAGR) of 12.4% during the forecast period. This growth is primarily driven by the increasing integration of GIS technology across various industries to improve spatial data visualization, enhance decision-making, and optimize operations. The benefits offered by GIS in terms of accuracy, efficiency, and cost-effectiveness are convincing more sectors to adopt these systems, thereby expanding the market size significantly.

A major growth factor contributing to the GIS market expansion is the escalating demand for location-based services. As businesses across different sectors recognize the importance of spatial data analytics in driving strategic decisions, the reliance on GIS applications is becoming increasingly pronounced. The rise in IoT devices, coupled with the enhanced capabilities of AI and machine learning, has further fueled the demand for GIS solutions. These technologies enable the processing and analysis of large volumes of spatial data, thereby providing valuable insights that businesses can leverage for competitive advantage. In addition, government initiatives promoting the adoption of digital infrastructure and smart city projects are playing a crucial role in the growth of the GIS market.

The advancement in satellite imaging and remote sensing technologies is another key driver of the GIS market growth. With enhanced satellite capabilities, the precision and quality of geospatial data have significantly improved, making GIS applications more reliable and effective. The availability of high-resolution satellite imagery has opened new avenues in various sectors including agriculture, urban planning, and disaster management. Moreover, the decreasing costs of satellite data acquisition and the proliferation of drone technology are making GIS more accessible to small and medium enterprises, further expanding the market potential.

The advent of 3D Geospatial Technologies is revolutionizing the way industries utilize GIS data. By providing a three-dimensional perspective, these technologies enhance spatial analysis and visualization, offering more detailed and accurate representations of geographical areas. This advancement is particularly beneficial in urban planning, where 3D models can simulate cityscapes and infrastructure, allowing planners to visualize potential developments and assess their impact on the environment. Moreover, 3D geospatial data is proving invaluable in sectors such as construction and real estate, where it aids in site analysis and project planning. As these technologies continue to evolve, they are expected to play a pivotal role in the future of GIS, expanding its applications and driving further market growth.

Furthermore, the increasing application of GIS in environmental monitoring and management is bolstering market growth. With growing concerns over climate change and environmental degradation, GIS is being extensively used for resource management, biodiversity conservation, and natural disaster risk management. This trend is expected to continue as more organizations and governments prioritize sustainability, thereby driving the demand for advanced GIS solutions. The integration of GIS with other technologies such as big data analytics, and cloud computing is also expected to enhance its capabilities, making it an indispensable tool for environmental management.

Regionally, North America is currently leading the GIS market, driven by the widespread adoption of advanced technologies and the presence of major GIS vendors. The regionÂ’s focus on infrastructure development and smart city projects is further propelling the market growth. Europe is also witnessing significant growth owing to the increasing adoption of GIS in various industries such as agriculture and transportation. The Asia Pacific region is anticipated to exhibit the highest CAGR during the forecast period, attributed to rapid urbanization, government initiatives for digital transformation, and increasing investments in infrastructure development. In contrast, the markets in Latin America and the Middle East & Africa are growing steadily as these regions continue to explore and adopt GIS technologies.

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