Indonesia, the most densely archipelagic state, has a population of 282 million, or 3.47% of the world's population. In 2022, Indonesia, in general, and Java, in particular, already experienced a biocapacity deficit based on a study by the Global Footprint Network. This study aims to optimize land cover/land use (LCLU) location selection. This ecological footprint (EF) is utilized as an LCLU allocation assessment based on primary demands. The EF analysis shows that paddy rice croplands and estate crop plantations are deficient. This study also conducted an LCLU suitability model using a support vector machine (SVM) via an error-correcting output codes (ECOC) classifier for multiclass learning. The ECOC obtains posterior probability values that indicate the suitability level. The overall accuracy was 82.81%, with a kappa coefficient value 0.772. The next step is LCLU location-allocation based on spatial contiguity and compactness (2C) simultaneously with the Pareto optimization approach. The verification results show that the algorithms can facilitate each 2C definition. The results show that estate crop plantations still exhibit deficits, but deflation exists. The deficit-cutting for paddy rice croplands reached 59,185.92 ha, and estate crop plantations reached 676,962.86 ha. The LCLU location candidates have limited choices because Java is already densely populated. There are limited choices of LCLU locations because Java is already densely populated. Despite the results, geometrically, the results of location selection are more compact, and there is a reduction in land fragmentation.

Healthcare Facilities Location-Allocation: A Bi-Level Optimization FrameworkThis repository provides a simulation and optimization framework for the location-allocation of healthcare facilities. Due to data sensitivity and privacy constraints, only raw input files and representative sample data are shared. These include:- od.xml: a sample origin-destination trip file - osm.net.xml: the base SUMO network file - od.taz.xml: TAZ-based OD mapping - Base_Station_to_Area_Matching.json: spatial correspondence between mobile base stations and traffic analysis zones - boundary_suzhou.shp: shapefile of the study area boundary - Pop.shp: spatial distribution of population - hospital.csv: actual candidate facility locations used in the optimization - non_colon_edgeids.csv: File containing a list of valid edge IDs (excluding junction-based virtual edges)- Partial attraction/occurrence coefficients, as defined in compute_hospital_trip.py In addition to the raw inputs, we also provide representative intermediate and output data for demonstration purposes:- origin_ok_multi.csv: a sample processed OD matrix used for accessibility computation - data/4/emission_004.xml: sample carbon emission output from the SUMO simulation - data/4/dump_004_900.xml: SUMO network snapshot including edge-level average speeds and flow data (for MSA-based SUE equilibrium results)The complete codebase is fully operational, and the entire simulation–evaluation–optimization pipeline is described below.Although this study focuses on a resource allocation use case as an example, users may also refer to the placeholder interface in lines 127–128 of Healthcare_facilities_location_allocation.py to extend the framework to other location sets. By modifying the candidate facilities in hospital.csv (both in terms of number and location), and adjusting the selected_ids, users can control the candidate set involved in location planning.---## 1. Running the Full Bi-Level Optimization PipelineThe script Healthcare_facilities_location_allocation.py is the main entry point to execute the full bi-level optimization process. The remaining scripts serve as supporting modules in this workflow.### 1.1 Base Station to Traffic Zone Matching- Input: Base_Station_to_Area_Matching.json (Result of spatial matching between mobile base stations and traffic analysis zones)### 1.2 Load Existing OD Data- Input: Sample OD matrices (uncalibrated) located in the data directory (Derived from mobile signaling data and validated against official survey reports)### 1.3 Generate Traffic Trips Using SUMO’s od2trips- Input: Calibrated OD data- Output: Trip files for SUMO simulation### 1.4 Perform Stochastic User Equilibrium (SUE) Assignment via MSA Algorithm- Scripts: - duaIterate.py - stochastic_user_equilibrium.py### 1.5 Extract and Convert Network Flow Dynamics- Input: SUMO output files- Script: extract_network.py- Output: .shp files containing path speed, average travel time, etc. (only samples provided due to data protection)### 1.6 Compute Accessibility and Equity with Multi-Modal Network (MMN-2SFCA)- Input: Multi-modal network files and OD flow data- Script: super_network.py- Output: Accessibility and equity metrics under current planning solution### 1.7 Estimate Transportation Carbon Emissions- Script: carbon_emission.py- Input: Simulated traffic flows from SUMO- Output: Emission statistics under the evaluated solution### 1.8 Solve Using NSGA-II and Retrieve Final Pareto Solutions- Output: - ndset_final_results_beds.csv – Pareto-optimal solutions - all_evaluated_solutions.csv – All evaluated solutions across generations---## 2. Running the Static (Non-Bi-Level) Optimization Version (without bi-level)The simplified version of (without bi-level) Healthcare_facilities_location_allocation.py can be executed to perform location-allocation without stochastic user equilibrium modeling. In this mode, the network is treated as static, as is common in most conventional location-allocation models.This pipeline includes:- Step 1.1: Base station matching- Step 1.6: Accessibility and equity computation -multi_objective_calculation_nobilevel.py- Step 1.8: NSGA-II optimization and Pareto set evaluation (without bi-level)---## 3. Visualization and Table Generation ScriptsThese scripts are designed to generate final visualizations (Figure 5 to Figure 8) and summary tables (Table 2) as referenced in the main manuscript.### 3.1 Plot_Fig_5.py – Generate Figure 5- Input: - data/existing_plan.shp – Existing facility locations - data/single_level_max_acc.shp – Plan maximizing accessibility - data/single_level_min_ineq.shp – Plan minimizing inequality### 3.2 Plot_Fig_6 (Accessibility & Inequity).py – Generate Figure 6 & 7- Input: - hospital_config.csv – Facility capacity and configuration - network_data/origin_ok_multi.csv – Multi-modal OD network- Output: - output/accessibility_result.shp – Accessibility index shapefile### 3.3 Extract_Table_2.py – Generate Table 2- Input: - 4/dump_004_900.xml – SUMO XML dump containing average edge speeds### 3.4 Plot_Fig_7 (Emission).py – Generate Emission Visualization (Figure 7)- Input: - output_background/osm.net.xml – SUMO network file - 4/emission_004.xml – Emission output file from SUMO simulation### 3.5 Plot_Fig_8.py – Generate Figure 8- Input: - ndset_final_results_beds.csv – Final Pareto set - all_evaluated_solutions.csv – Complete NSGA-II search space### 3.6 -All the other Figures are presented by the QGIS software and without any code, therefore we don't share them in this markdown.---## Notes for Users- To extend the candidate facility set, update hospital.csv with new coordinates and adjust the selected_ids list to control participation in location planning.- All sample data are anonymized and used for demonstration purposes only. Due to privacy agreements, raw OD and flow data cannot be publicly released.- The simulation workflow depends on Eclipse SUMO – ensure your environment is correctly configured before running trip and flow simulations.- The framework is implemented in Python 3.8+. Please install required packages as specified at the beginning of each script.---For further questions or collaboration inquiries, feel free to open an issue or contact the author directly.

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

This project studied the pooling location optimization problem by visually creating heatmap to visualize the distribution of registered voters for each precinct, the polling location area type layer by spatial join, and by computationally performed Location-Allocation Analysis on candidate polling locations and demand locations, with weights based on the rate of in-person voters in a certain election precinct.Notable Modules Used: Python: pandas, geopandas, matplotlib, seaborn ArcGIS: dissolve_boundaries, find_existing_locations, enrich, aggregate_points, create_buffers, solve_location_allocation, Storymaps, WebMap No permission to access the contents.

COVID-19 Response: Location AllocationThis video demonstrates siting testing facilities to equitably serve vulnerable communities using location allocation._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

Location Intelligence Analytics Market Outlook

The global location intelligence analytics market size is projected to grow from USD 14.2 billion in 2023 to USD 31.7 billion by 2032, exhibiting a CAGR of approximately 9.4% during the forecast period. This robust growth is primarily driven by the increasing demand for spatial data and analytical tools across various industries to enhance decision-making processes and optimize business operations. As organizations increasingly recognize the value of location-based insights, they are investing in sophisticated analytics solutions that leverage geographic data to drive business outcomes and gain competitive advantages.

One of the primary growth factors for the location intelligence analytics market is the proliferation of IoT devices and the consequent surge in location-based data generation. With billions of connected devices expected to be operational in the coming years, the volume of location-specific data is set to explode. Businesses across industries are eager to harness this data to gain insights into consumer behavior, improve operational efficiency, and develop targeted marketing strategies. Moreover, advancements in AI and machine learning are enabling more sophisticated analysis of location data, providing deeper insights and predictive capabilities that are invaluable to enterprises.

Another significant driver for market growth is the growing adoption of smart city initiatives across the globe. Governments and municipalities are increasingly implementing location intelligence solutions to enhance urban planning, traffic management, and public safety. By leveraging location-based analytics, cities can optimize resource allocation, improve citizen services, and drive sustainable development. Furthermore, the integration of real-time data from various sources, such as sensors and social media, with geographic information systems (GIS) is facilitating more dynamic and responsive urban management systems, thus propelling the demand for location intelligence analytics.

The increasing emphasis on business intelligence and data-driven decision-making is also fueling the demand for location intelligence analytics. In today's competitive landscape, organizations are seeking to leverage every bit of data to gain actionable insights and stay ahead. Location intelligence provides a unique perspective by overlaying geographic data on traditional business data, offering a holistic view of trends and patterns. This capability is particularly valuable in sectors such as retail, transportation, and logistics, where location-based insights can directly impact revenue generation, cost savings, and customer satisfaction.

Regionally, North America is expected to hold the largest share of the location intelligence analytics market, driven by the presence of major technology companies and the rapid adoption of advanced analytics solutions across industries. The region's commitment to innovation and technological advancement is further supported by substantial investments in R&D activities. Additionally, Europe is anticipated to witness significant growth, influenced by stringent regulatory frameworks and a heightened focus on data privacy and security. In contrast, the Asia Pacific region is projected to demonstrate the highest growth rate, attributed to the rapid digital transformation and increasing investments in smart city projects across emerging economies like India and China.

Component Analysis

The location intelligence analytics market is broadly segmented into software and services. Software solutions are a critical component of this market, offering the necessary tools and platforms for collecting, analyzing, and visualizing geographic data. These software solutions are designed to process large volumes of spatial data, integrate various data sources, and provide users with intuitive and interactive interfaces for data exploration. The advancements in cloud computing and the increasing adoption of Software as a Service (SaaS) models are further driving the demand for location intelligence software, as they offer greater scalability, flexibility, and cost-effectiveness to organizations of all sizes.

Within the software segment, Geographic Information System (GIS) solutions are particularly prominent. GIS technology enables the mapping and analysis of spatial data, allowing users to visualize relationships, patterns, and trends in complex datasets. The ability to integrate GIS with other enterprise systems, such as Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP), enhances its ut

This dataset contains a network of roads linked to addresses from the BAG in Amsterdam and the surrounding area. The aim is to perform analyzes with this network for research into: - proximity, location optimization, - location allocation analysis and - incorporation of proximity in spatial autocorrelation, spatial regression, spatial econometrics (prediction).

Analysis of ‘Hydropower Allocations’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ced38bd4-37e3-448a-8df8-ce63a98e81ae on 27 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains the New York Power Authority’s hydropower customers having an allocation of Expansion Power, Replacement Power, and/or Preservation Power, including their location, amount of allocation, and amount of jobs committed.

--- Original source retains full ownership of the source dataset ---

Change in Capacity of Facilities pre & post allocation period.

The Location Intelligence Analytics market is experiencing robust growth, driven by increasing adoption of location-based services across diverse sectors. The market size in 2025 is estimated at $15 billion, projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant growth is fueled by several key factors. The rising availability of granular location data from various sources, including GPS, mobile devices, and IoT sensors, is enabling businesses to gain deeper insights into customer behavior, optimize operations, and improve decision-making. Furthermore, advancements in analytics techniques, such as machine learning and AI, are enhancing the capabilities of location intelligence platforms, allowing for more sophisticated analysis and prediction. The growing adoption of cloud-based solutions is also contributing to market expansion, providing scalability and cost-effectiveness. Major industries like BFSI (Banking, Financial Services, and Insurance), Healthcare, and Retail are significantly contributing to this growth, leveraging location analytics for targeted marketing campaigns, risk management, and supply chain optimization. The market segmentation reveals strong growth across various sectors. BFSI utilizes location intelligence for fraud detection, branch optimization, and targeted financial services. Healthcare benefits from improved patient care, efficient resource allocation, and epidemiological analysis. The retail sector leverages location analytics for optimizing store locations, understanding customer foot traffic, and personalizing marketing strategies. Government and utility companies employ location analytics for infrastructure management, emergency response, and resource planning. The competitive landscape is characterized by a mix of established players, such as SAP, IBM, Oracle, and Microsoft, and specialized location intelligence vendors. These companies are constantly innovating to meet the evolving needs of businesses and enhance their offerings through strategic partnerships and acquisitions. The global spread of the market, with significant regional presence in North America, Europe, and Asia Pacific, indicates a widespread demand for location intelligence solutions.

The Middle East & Africa Location Analytics market is experiencing robust growth, driven by increasing digitalization across various sectors and a rising need for data-driven decision-making. The market's Compound Annual Growth Rate (CAGR) of 6.20% from 2019-2033 indicates a significant expansion, projected to reach a substantial value by 2033. Key drivers include the burgeoning adoption of smart city initiatives, the expansion of IoT infrastructure, and the growing demand for improved operational efficiency across industries like retail, manufacturing, and healthcare. The increasing availability of location-based data, coupled with advancements in analytics technologies, is further fueling market growth. Specific applications like asset management, remote monitoring, and facility management are witnessing particularly high adoption rates, as businesses leverage location intelligence to optimize logistics, enhance security, and improve customer experiences. The market is segmented across deployment models (on-premise and on-demand), components (software and services), and various verticals, offering diverse opportunities for market players. While data privacy concerns and the need for robust cybersecurity measures present some restraints, the overall market outlook remains positive, with considerable potential for further expansion throughout the forecast period. The region's significant investments in infrastructure development and technological advancements will continue to propel market growth in the coming years. The competitive landscape involves established players like Cisco, Microsoft, and SAP, alongside specialized location analytics providers, indicating a dynamic and evolving market environment. The concentration of market activity within the Middle East, particularly in countries such as Saudi Arabia and the UAE, mirrors the region's economic strength and technological adoption rate. Growth in the region is fueled by government initiatives to promote smart cities and digital transformation, leading to increased demand for location-based solutions. Furthermore, the region's unique challenges, such as managing vast geographical areas and optimizing resource allocation, create a high demand for location analytics. The on-demand deployment model is likely gaining traction, offering flexibility and scalability to businesses of all sizes. The retail and healthcare sectors are expected to show strong growth within the MEA region driven by the need to enhance customer experiences and improve service delivery. Despite some challenges related to regulatory compliance and data security, the overall market trajectory remains upward, presenting lucrative opportunities for both established and emerging players in the location analytics space. Continuous innovation in AI and machine learning applications within location analytics is further enhancing its capabilities and value proposition, driving further market expansion. Recent developments include: October 2022 - Oracle Fusion Analytics now has additional CX, ERP, HCM, and SCM analytics capabilities. Decision-makers can now access a prebuilt library of more than 2,000 KPIs, dashboards, and reports that follow best practices for assessing performance about long-term objectives., Additionally, Oracle Analytics Cloud's most recent updates aim to increase business users' productivity by reducing their dependency on IT while allowing them to take advantage of selected data assets produced by IT, including the centralized semantic model. Data analysis has been made more accessible by new and improved composite visualizations. AI/ML advancements have expanded the ML capabilities in Oracle Analytics Cloud to include other cognitive services like AI Vision, allowing visual data processing., May 2022 - SAS' cloud-first portfolio soars cloud-first industry solutions. The analytics and AI leader's cloud-first approach eases customers' digital transformations; now, the company portfolio is to be cloud-native and cloud-portable so customers can accelerate their move to the cloud and expand their use of analytics, machine learning, and AI.. Key drivers for this market are: Rise of Technological Advances in Various Applications to Witness the Growth, Increasing Adoption of Analytical Business Intelligence and Geographical Information Systems technology; Increaseing Usage of IoT. Potential restraints include: Rise of Technological Advances in Various Applications to Witness the Growth, Increasing Adoption of Analytical Business Intelligence and Geographical Information Systems technology; Increaseing Usage of IoT. Notable trends are: Rise of Technological Advances in Various Applications to Witness the Growth.

Top 10 Municipalities by OHCA count, also indicating whether they are urban or rural areas.

Needing to answer the question of “where” sat at the forefront of everyone’s mind, and using a geographic information system (GIS) for real-time surveillance transformed possibly overwhelming data into location intelligence that provided agencies and civic leaders with valuable insights.This book highlights best practices, key GIS capabilities, and lessons learned during the COVID-19 response that can help communities prepare for the next crisis.GIS has empowered:Organizations to use human mobility data to estimate the adherence to social distancing guidelinesCommunities to monitor their health care systems’ capacity through spatially enabled surge toolsGovernments to use location-allocation methods to site new resources (i.e., testing sites and augmented care sites) in ways that account for at-risk and vulnerable populationsCommunities to use maps and spatial analysis to review case trends at local levels to support reopening of economiesOrganizations to think spatially as they consider “back-to-the-workplace” plans that account for physical distancing and employee safety needsLearning from COVID-19 also includes a “next steps” section that provides ideas, strategies, tools, and actions to help jump-start your own use of GIS, either as a citizen scientist or a health professional. A collection of online resources, including additional stories, videos, new ideas and concepts, and downloadable tools and content, complements this book.Now is the time to use science and data to make informed decisions for our future, and this book shows us how we can do it.Dr. Este GeraghtyDr. Este Geraghty is the chief medical officer and health solutions director at Esri where she leads business development for the Health and Human Services sector.Matt ArtzMatt Artz is a content strategist for Esri Press. He brings a wide breadth of experience in environmental science, technology, and marketing.

Analysis of ‘Recharge New York Customers’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/69fc5345-63fe-4aea-8313-c6df415ad293 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Recharge New York Power is available to businesses and not-for-profit corporations for job retention and business expansion and attraction purposes. This dataset contains Recharge New York Customers, including their location, amount of allocation, and amount of jobs committed.

--- Original source retains full ownership of the source dataset ---

Top 20 number of AEDs added, along with priority ranks, access scores, model predictions, and whether the areas were rural or urban.

We refine an earlier cross-national analysis by Koch, et al (2009) on the determinants of location and allocation decisions by international non-governmental organizations. Using a more explicit causal framework, multiple imputation and coarsened exact matching, we come to qualitatively similar but analytically more robust conclusions. Shared colonial heritage and shared religion increase the probability that a large international NGO locates in a country. The share of exports from an NGO’s home country that go to a recipient country does not affect the level of aid allocated by the NGO in the recipient country. More harmonious UNGA voting between an NGO’s home country and the recipient country decreases the level of aid allocated by the NGO in the recipient country. We also provide more credible and easily interpretable measures of effect size, and we discuss implications and directions for future research.

In our thorough evaluation aimed at identifying optimal locations for funding allocation towards solar microgrid development, we employed a multifaceted approach, considering key metrics to pinpoint areas with the highest potential for both environmental and societal impact.The following metrics were assessed to guide our decision-making process to assess what JCC facilities are able to apply for PG&E microgrid funding:Tier 2 or 3 High-Fire Threat District Is represented by:High Fire Threat DistrictA blank column means not located within a threat district, and the ones that are, have the threat district labeled in them.Area that experienced prior PSPS outage(s) is represented by:10 yr Average PSPS Outage Frequency this data was gathered from 2013 to 2021 and shows yearly average PSPS outages, 0 means that a specific facility has not experienced PSPS outages in the past 10 years.Elevated earthquake risk zone is represented by:% Gravity (G-Force) od Ground ShakingThis data was provided by the USGS and was used instead of the risk. The seismic risk map we both saw was said to be not used for analysis as the representation is not sound for specific analysis with any human habitation. So we used ground shaking in percent of gravity as it is a better risk indicator of potential building risk to seismic events as outlined by the USGS. Census tracts with median household incomes less than 60% of the state's median is represented by:Median Household Income per tract 2021I left this class unfiltered because the median income has been different everywhere I looked. These are in 2022 inflation-adjusted dollars.California Native American Tribal Community only one facility was located in NATC, so I didn't include an analysis of all 447 locations. I listed the only JCC location in NATC lands below. USER_Bldg_12-E1USER_Bldg1Hoopa CourthouseUSER_AddreHighway 96Match_addrCA-96, Hoopa, California, 95546Community with the highest risk per CalEnciroscreenCAL EnviroScreen PercentileI think pg&e are pretty subjective with what they are considering the highest risk per CalEnviroscreen, so again I just used a more objective measurement of the Enviroscreen. I would use anything greater than or equal to the 75th percentile being deemed high risk.

Top 20 priority ranks, access scores, model predictions, the number of AEDs added, and whether the areas were rural or urban.

Plants allocate resources to growth, defense, and stress resistance, and resource availability can affect the balance between these allocations. We measured growth and defense in a provenance trial of rubber trees (Hevea brasiliensis) with clones originating from the Amazon basin. To test hypotheses on the allocation to growth vs. defense, we relate biomass growth and latex production to wood and leaf traits, to climate and soil variables from the original location, and to the genetic relatedness of the Hevea clones. Growth was weakly correlated with leaf traits, such as leaf mass per area, intrinsic water use efficiency, and leaf nitrogen content, but the relative investment in growth vs. defense was not associated with specific traits or environmental variables. Wood and leaf traits showed clinal correlations to rainfall and soil variables of the original location. These traits exhibited strong phylogenetic signals, highlighting the role of genetic factors in trait variation and adaptation. Contrary to expectations, there was no trade-off between growth and defense, but latex yield and biomass growth were positively correlated, and both increased with tree size. The absence of a trade-off may be attributed to high resource availability in the plantation, allowing trees to allocate resources to both growth and defense. The study provides insights into the interplay between resource allocation, environmental adaptations, and genetic factors in trees. While latex production (but not growth) also had strong phylogenetic signal, the underlying drivers for high variation of latex production in one of the commercially most important tree species remains unexplained. Methods The study utilized Brazilian rubber trees, originally sampled from wild populations in the Amazon basin. Trees were planted in a provenance trial in Thailand with replicates per clone. Latex yield and growth data were collected over several years, while leaf and wood traits were measured from leaf and wood samples collected from various clones. Traits related to growth, defense, leaf, and wood traits were analyzed to explore potential trade-offs among these categories. Additionally, environmental variables from the original locations of the clones were considered in the analysis.

Cognitive Radio Market Report is Segmented by Application (Spectrum Sensing and Allocation, Location Detection, Cognitive Routing, and More), Component (Hardware, Software and Firmware, and Services), Spectrum Band (HF/VHF/UHF [Less Than 1 GHz], SHF [1-6 GHz], and EHF [More Than 6 GHz, Mmwave]), End-User Industry (Telecommunications, IT and ITes, Government and Defense, Transportation and Logistics, and More), and Geography.