District Poverty Research Articles

Emergency Action Planning in United States High Schools.

Rates of emergency action plan (EAP) implementation and compliance with EAP guidelines vary in United States secondary schools. There are limited data on emergency preparedness in schools without athletic trainers (ATs). Rural and high poverty schools have poor emergency preparedness for high school athletic events due to financial barriers and lack of ATs. Cross-sectional study. Level 5. A web-based questionnaire was emailed by each State High School Athletics Association to ATs, athletic directors, and coaches of recipient schools to assess emergency preparedness. A total of 686 responses were received (response rate ~3.5%). Schools with an AT on staff were more likely to have an EAP (P < 0.01). Schools with a higher enrollment were more likely to have an EAP (P < 0.01), an AT on staff (P < 0.01), require additional training for coaches (P < 0.01), and have an automated external defibrillator (AED) onsite for all events (P < 0.01). Urban (odds ratio [OR], 3.514; 95% CI [2.242, 5.507]; P < 0.01) and suburban (OR, 4.950; 95% CI [3.287, 7.454]; P < 0.01) districts were more likely than rural districts to have an AT on staff. High poverty districts were less likely to have an AED (OR, 0.660; 95% CI [0.452, 0.964]; P = 0.03) or EAP (OR, 0.511; 95% CI [0.306, 0.853]; P < 0.01) at athletic venues. Lower enrollment, high poverty, and rural schools are less prepared for athletic emergencies than their higher enrollment, low poverty, and suburban counterparts as these schools are generally less likely to have an AT on staff, AEDs, and EAPs and less likely to provide additional training to coaches and other staff. This study assesses athletic emergency preparedness-a critical component of ensuring athlete safety and recovery after injury.

The Role of Implementation Climate in Moderating Educator Use of Evidence-Based Practices and Outcomes for Autistic Students.

Ensuring effective use of evidence-based practice (EBP) for autism in schools is imperative due to the significantly increasing number of autistic students receiving school services each year. High-quality EBP use has proven challenging in schools. Research indicates implementation climate, or how EBP are supported, rewarded, and valued, and EBP resources are related to successful implementation. However, limited understanding of system-level contextual factors that impact EBP implementation for school-based providers makes development of appropriate implementation supports challenging. Understanding these factors is crucial for selecting and tailoring implementation strategies to support EBP scale up. In this observational study, California school-based providers (n = 1084) completed surveys related to implementation climate, leadership, autism experience and EBP implementation (use, competence, knowledge). Student outcomes included state level academic and behavioral indicators. Using an implementation science framework (Aarons et al., in Administration and Policy in Mental Health and Mental Health Services Research 38:4-23, 2011) and multilevel modeling, we examined the relationship between EBP Implementation and student outcomes and the moderation effects of provider and district level factors. Higher implementation climate predicted better EBP implementation outcomes, and proved more impactful when provider hands-on autism experience was low. Greater EBP resources predicted a higher percentage of students who met math standards only when district poverty level was high. Our findings suggested moderating effects on EBP implementation from both provider and system level factors. Implementation climate and resources may be especially key in addressing equity issues related to high poverty schools in which teachers often have less autism experience.

Mortality from leading cancers in districts of England from 2002 to 2019: a population-based, spatiotemporal study

Cancers are the leading cause of death in England. We aimed to estimate trends in mortality from leading cancers from 2002 to 2019 for the 314 districts in England. We did a high-resolution spatiotemporal analysis of vital registration data from the UK Office for National Statistics using data on all deaths from the ten leading cancers in England from 2002 to 2019. We used a Bayesian hierarchical model to obtain robust estimates of age-specific and cause-specific death rates. We used life table methods to calculate the primary outcome, the unconditional probability of dying between birth and age 80 years by sex, cancer cause of death, local district, and year. We reported Spearman rank correlations between the probability of dying from a cancer and district-level poverty in 2019. In 2019, the probability of dying from a cancer before age 80 years ranged from 0·10 (95% credible interval [CrI] 0·10-0·11) to 0·17 (0·16-0·18) for women and from 0·12 (0·12-0·13) to 0·22 (0·21-0·23) for men. Variation in the probability of dying was largest for lung cancer among women, being 3·7 times (95% CrI 3·2-4·4) higher in the district with the highest probability than in the district with the lowest probability; and for stomach cancer for men, being 3·2 times (2·6-4·1) higher in the district with the highest probability than in the one with the lowest probability. The variation in the probability of dying was smallest across districts for lymphoma and multiple myeloma (95% CrI 1·2 times [1·1-1·4] higher in the district with the highest probability than the lowest probability for women and 1·2 times [1·0-1·4] for men), and leukaemia (1·1 times [1·0-1·4] for women and 1·2 times [1·0-1·5] for men). The Spearman rank correlation between probability of dying from a cancer and district poverty was 0·74 (95% CrI 0·72-0·76) for women and 0·79 (0·78-0·81) for men. From 2002 to 2019, the overall probability of dying from a cancer declined in all districts: the reductions ranged from 6·6% (95% CrI 0·3-13·1) to 30·1% (25·6-34·5) for women and from 12·8% (7·1-18·8) to 36·7% (32·2-41·2) for men. However, there were increases in mortality for liver cancer among men, lung cancer and corpus uteri cancer among women, and pancreatic cancer in both sexes in some or all districts with posterior probability greater than 0·80. Cancers with modifiable risk factors and potential for screening for precancerous lesions had heterogeneous trends and the greatest geographical inequality. To reduce these inequalities, factors affecting both incidence and survival need to be addressed at the local level. Wellcome Trust, Imperial College London, UK Medical Research Council, and the National Institute of Health Research.

Extend of Multidimensional Poverty Index in Haryana: A Multidimensional Approach

To quantify poverty merely monetary approach does not provide a true representation of deprivations or wellbeingness of households. Multidimensional Poverty Index measures multiple dimensions of deprivation of poverty that gives a more complete picture of poverty. For the study MPI status of Haryana and India as a whole the secondary data on India National Multidimensional Poverty Index, Baseline Report, Based on National Family Health Survey-4 (NFHS) 2015-16 published in 2021 has been used. Main objective of the paper is to study the status of Multidimensional Poverty Index of India and the position of Haryana as it’s in top states with respect to per capita income from 2006 to 2021. The district wise deprivation and counting of different types of deprivations that individuals experience at the same time has been discussed. The study observed that Haryana stands in better position in Multidimensional poverty index (value 0.055) and less deprived in all the indicators of three dimensions of Multidimensional poverty index compare the average status of India as a whole (0.118). District level results revealed the district Mewat, Palwal, Sirsa and Rohtak, Mahendragarh have higher level of multidimensional poverty districts whereas Ambala stands at first rank in lowest head count ratio 1.99%, followed by Panchkula with 2.47%. Comparatively in third dimension of MPI status of Haryana is far better like in electricity. Its deprivation is 0.75 percent compare to 8.29 percent in India. Results show that there is inter district disparity among the indicators which has been reduced. Few districts are above in all indicators due to economic factors and few are deprived in all indicators like Mewat , which is just 58 km far from millennium city of India. Status of health, education and standard of living is worsening in Mewat. Causes are many Like Religion, political, socio orthodox and Illiteracy.

Poverty at a distance: Supply and demand side factors and the formation of civil society organizations in Iraq

AbstractThis paper contributes empirically to the international debate on the drivers for the formation of civil society organizations (CSOs) by testing three separate hypotheses: demand, supply and integration. To investigate this question, we analyse the CSO sector in Iraq, which transformed since 2003 from being implicitly banned to being a central pillar of poverty alleviation and social service provision with thousands of formally registered organizations. Using OLS regression analysis with district level and official register data, we examine the geographic distribution of CSOs in relation to poverty levels in Iraq. We find no significant confirmation for demand side drivers that social service providing CSOs form in high poverty districts. Instead, we find strong support for CSO formation in governorate administrative centres, which confirms supply‐side drivers of CSO development. However, we do find some support for a higher concentration of human rights‐based CSOs in high poverty areas. The findings raise important policy implications for the UN Sustainable Development Goal of no poverty in an Iraqi context and internationally, but also how to fund CSOs to meet the demands of the primary recipients.

Educational Inequality and the Poverty Trap in Teacher Recruitment

The regional concentration of poverty and the resulting disparities in living conditions create conditions where educational inequalities are intensified. These adverse conditions could lead teachers to refuse to work in disadvantaged locations that are characterised by high incidences of poverty and low-performing students. In this paper, we estimate how poverty in the districts where the schools are situated influences the probability that teachers accept a job offer in Costa Rica. Working with data on contract offers acceptance or rejection is a methodological novelty that makes it possible to dissociate students’ poverty from the poverty of the schools’ location. The estimation of a three‐level hierarchical model allows us to deal with aggregation bias and unobserved heterogeneity. The results show evidence that district poverty is a key determinant of teachers’ rejection of offers. Although the study uses data from Costa Rica, the results indicate more generally, how educational inequity can perpetuate poverty.

Poster 126: Emergency Action Planning in United States High Schools

Objectives:Current evidence shows a variable rate of emergency action plan (EAP) implementation and a low rate of compliance to EAP guidelines in United States secondary schools. There is limited data on emergency preparedness of schools without access to an athletic trainer (AT). The purpose of this study was to identify the emergency preparedness of public high school athletics in the United States.Methods:A web-based questionnaire was developed to perform a cross-sectional analysis of the emergency preparedness of high schools. The questionnaire included 24 questions focused on demographics of the study population, EAP adoption, compliance to national EAP guidelines, access to certified medical personnel, and training received by athletics personnel. The questionnaire was delivered electronically through email by each State High School Athletics Association (SHSAA) to athletic trainers, athletic directors, and coaches of recipient schools.Results:Schools with a larger number of students enrolled were significantly associated with greater proportions of having an EAP (p<.0001), having an AT on staff (p<.0001), requiring additional training for coaches (p = .0003), and having an AED on-site for all events (p = .0021). Urban districts (OR=3.514, 95% CI=[2.242, 5.507], p <.0001) and suburban districts (OR=4.950, 95% CI=[3.287, 7.454], p <.0001) were more likely than rural districts to have an athletic trainer on staff. Rural districts were more likely than suburban districts to report financial barriers (OR=1.867, 95% CI=[1.051, 3.318], p=.0321). Rural districts were more likely than both urban (OR=1.901, 95% CI=[1.104, 3.268], p=.0192) and suburban (OR= 2.825, 95% CI=[1.770, 4.505], p<.0001) to report that additional funding would help meet NATA EAP best practice standards. High poverty districts (96 urban, 69 suburban and 157 rural) were less likely to have an AED for all athletic venues (OR=.660, 95% CI=[.452, .964], p=.0311) and less likely to have an EAP (OR=.511, 95% CI=[.306, .853], p=.0092). Among districts that were impoverished, rural districts were less likely than urban (OR=.268, 95% CI=[.153, .469], p<.0001) and suburban (OR=.121, 95% CI=[.056, .260], p <.0001) to have an AT on staff, more likely than suburban districts to report financial barriers (OR=1.867, 95% CI=[1.051, 3.318], p=.0321), and more likely to report barriers related to access to medical providers than urban (OR=3.403, 95% CI=[1.666, 6.949], p=.0005) and suburban (OR=3.900, 95% CI=[1.664, 9.144], p=.0010).Conclusions:The results of this study suggest that lower enrollment, high poverty and rural schools are less prepared for athletic emergencies than their higher enrollment, low poverty and suburban counterparts given the fact that these schools are generally less likely to have an AT on staff, less AEDs available at sporting events, lor to have EAPs implemented and less likely to provide additional training. Among these classifications, rural status may be the most important indicator, given that a comparison of impoverished schools demonstrates that rural schools were less likely to have ATs on staff and more likely to report financial barriers and barriers related to access to medical providers compared to poor urban and suburban schools. Financial barriers likely underly many of the findings in this study, as well as barriers related to access to medical professionals. Future improvement strategies should seek to identify ways to overcome these barriers and encourage compliance with NATA recommendations.

Were Federal COVID Relief Funds for Schools Enough?

Congress responded to the COVID pandemic’s disruptions to instruction with unprecedented federal aid for school districts. While this relief has been widely characterized as a major windfall for K-12 education, per-pupil amounts vary considerably across districts, as will the costs districts face for COVID mitigation and recovery. In this paper, we conduct simulations to understand the potential distribution of net effects of the pandemic and federal aid on the finances of local school districts in the next several years. In our baseline scenario, we assume one-time adjustment costs of $500 per pupil plus additional costs of $1,000 per student in poverty and $500 per student not in poverty per year for four years. Federal aid was distributed proportional to the longstanding Title I program, which sends more money per pupil to higher poverty districts. Low-poverty districts are therefore projected to face some budgetary shortfalls, while many higher poverty districts are projected to have excess funds, which they could direct towards long-standing challenges. While our findings depend on key assumptions about the COVID-related costs, we find significant district-level variation in simulated net fiscal impacts, in part but not completely due to poverty rates, across all the scenarios we consider.

Editorial: Urban Energy Poverty and Positive Energy Districts

Editorial: Urban Energy Poverty and Positive Energy Districts

A Bayesian Logit-Normal Model in Small Area Estimation

The Logit-Normal model is one of the GLM Bayes models with random covariates used in binary data. This study aimed to examine and evaluate the characteristics of the Logit-Normal model. The second objective was to apply the Logit-Normal model to estimate the proportion of poverty in the small area of Mukomuko District in Bengkulu Province. We used the Hierarchical Bayes (HB) method to estimate parameters model. The simulation results obtained from the optimum number of iterations and gibbs samples, namely 500 iterations and 100 gibbs samples, respectively. In addition, when seen the value of has the same tendency with the parameter pi and . The value of tends to overestimate at ⩽ 60%. Conversely, tends to underestimate > 60%. Furthermore, in the simulation results the estimated value of variance and MSE tends to be smaller than the variance of proportion. The application of the HB method for Logit-Normal model in the Mukomuko District poverty data produces which has the same tendency as the result of direct estimator (). The majority (21 villages) value of var() are smaller or equal to the var(). This indicates that the estimation using the HB method can improve the estimation of the proportion parameters obtained by using the direct estimation method on poverty data.

Racial and Socioeconomic Test-Score Gaps in New England Metropolitan Areas: State School Aid and Poverty Segregation

Test-score data show that both low-income and racial-minority children score lower, on average, on states’ elementary-school accountability tests compared with higher-income children or white children. While different levels of scholastic achievement depend on a host of influences, such test-score gaps point toward unequal educational opportunity as a potentially important contributor. This report explores the relationship between racial and socioeconomic test-score gaps in New England metropolitan areas and two factors associated with unequal opportunity in education: state equalizing school-aid formulas and geographic segregation of low-income students. The underlying methods do not allow a strict causal interpretation; however, both aspects are strongly related to test-score gaps, with poverty segregation between school districts especially important in New England. The report first explores the degree to which state school aid is progressive, that is, distributed disproportionately to districts with high fractions of students living in poverty; more progressive distributions are associated with smaller test-score gaps in high-poverty metropolitan areas. All U.S. states distribute some state revenue to support local school districts, but the extent to which such aid is focused on districts with greater concentrations of poverty varies considerably. The relationships estimated in the empirical analysis suggest that New England metro areas with high average district poverty in states with more progressive aid distributions, such as Springfield, Massachusetts, should see somewhat smaller racial and socioeconomic test-score gaps than metro areas with lower district poverty in states with less progressive school aid, such as Burlington, Vermont; that predicted difference in white-Black test-score gaps amounts to about one-quarter of the actual difference between Springfield’s gap and Burlington’s gap. The second factor explored is poverty segregation; test-score gaps are larger in metropolitan areas where, compared with white children or higher-income children, minority children or low-income children go to school with, or are in school districts with, more students from low-income families. Partly because school districts (and cities and towns) are relatively small geographically in New England, poverty segregation in the region’s metropolitan areas is most pronounced between districts, not between schools within school districts. The sizes of the estimated relationships suggest that metro areas with the highest between-district poverty segregation, such as Bridgeport-Stamford-Norwalk, Connecticut, should have markedly larger test-score gaps than metro areas with moderate poverty segregation between districts, such as Manchester-Nashua, New Hampshire; those predicted differences amount to 60 percent to 90 percent of the actual test-score gap differences between the Bridgeport and Manchester metro areas. States can alter either or both of these factors via policy changes. States set the terms—and thereby the progressivity—of school-aid policy. Many states include cost adjustments in their aid formulas to offset some of the additional costs of educating students from low-income families, and some recent proposals (such as for Connecticut) or policy changes (such as in Massachusetts) involve more closely targeting state equalizing aid to high-poverty districts. State policy levers regarding between-district poverty segregation are less direct and potentially more controversial. Nonetheless, statewide affordable housing policies, such as those in Massachusetts and Rhode Island, if applied more comprehensively, might reduce concentrations of poverty and provide more low-income families access to the higher-quality schools in low-poverty suburban districts.

Socio-economic resilience of Pidie Jaya to against disaster in the terms of national security

Pidie Jaya (Piday) District of Aceh Province is a newly formed district in 2007. In addition to the COVID-19 pandemic, the Pidie Jaya district also experienced a series of disasters throughout 2020. Nevertheless, the Piday District Human Development Index (HDI) in 2017 exceeded the National achievement. But piday district poverty ranks 3rd in the Province. This has the potential to make the Piday district prone to disasters in addition to geographical location factors. Therefore, the existence of such gaps in this study will be discussed. This study uses qualitative methods through descriptive narrative approaches. Sources of information and data were obtained through Focus Group Decision (FGD) from several sources and studies of some literature. The results stated that the gap is due to economic development being less evenly distributed and less felt by lower-level people. This is due to low education factors and lack of capital for farmers and fishermen as the majority of workers in Piday District. The research suggests increasing community capacity and resilience by 1) Improving the quality of human resources for farmers and fishermen in the form of education and training based on improving the economy and living standards of farming communities wrapped in local wisdom; 2) Infrastructure development both facilities and infrastructure; 3) Provision of capital and ease of access in business and insurance coverage against disasters by taking into account local wisdom; 4) Empowerment of the role and involvement of local community leaders in the formulation of policies and local government institutions.

ANALISIS PERTUMBUHAN EKONOMI, PENDIDIKAN DAN INDEKS PEMBANGUNAN MANUSIA TERHADAP TINGKAT KEMISKINAN DI MADURA

One of the main indicators of the success of a development is the reduction in poverty levels. Regency on Madura Island is the region with the highest poverty rate in East Java Province. The purpose of this study was to analyze the effect of economic growth, education and human development index on the poverty level of districts on the island of Madura 2012-2018. In this study using quantitative research and secondary data types sourced from the Central Statistics Agency. The regression analysis used is panel data with the chosen model that is Random Effect. The results of this study indicate that: 1). Partially economic growth, education and HDI have a negative and significant influence on the poverty level of districts on the island of Madura 2012-2018 2). Simultaneously economic growth, education and HDI have an influence on district poverty levels on the island of Madura 2012-2018. In this study concluded that the economic growth variable has a negative and significant effect on poverty levels with a probability value of 0,0001 and a coefficient of -0.180288. The education variable that uses the average length of school has a negative and significant effect on poverty levels with a probability value of 0.0000 and a coefficient of -2.174169. The human development index variable has a negative and significant effect on poverty levels with a probability value of 0,0001 and a coefficient of -0.540652.

Activism in Practice: The Influence of a Rural School Leader’s Beliefs and Practices in Disrupting Historical Patterns of Underachievement in Traditionally Marginalized Students

Despite the fact that rural communities across the United States are rapidly diversifying (Fusarelli &amp; Militello, 2012), little research has examined the beliefs and practices of successful rural educational leaders, specifically in high poverty schools and districts where traditionally marginalized students demonstrate improving learning outcomes. The purpose of this study was to examine the beliefs and practices of a rural educational leader whose school or district met established study criteria for a high poverty, high performing school, in which traditionally marginalized students demonstrate increasingly productive learning outcomes. Interviews with the leader were conducted, and the data were coded and analyzed using a constant comparative method (Lincoln &amp; Guba, 1985).The following research question guided the study:What impact do the beliefs and practices of a rural school district leader have on the learning outcomes of traditionally marginalized students in the Rocky Mountain West?The findings from this study contribute to the paucity of research on culturally responsive rural superintendent-principals. Identifying the rural leader’s beliefs and practices provides support for educational leaders who serve in that uniquely rural dual role, about which very little has been written. It informs leadership preparation programs, graduate students, researchers, and policy makers about the need for nuanced culturally responsive training for rural educational leaders.

Forgotten equity: The promise and subsequent dismantling of education finance reform in New York State

This study examines the impact of court-ordered finance reform in New York State resulting from Campaign for Fiscal Equity v. State of New York on equity of inputs using synthetic controls. The findings herein indicate court mandated education finance reform in New York had little to no impact on equity of educational inputs despite an overhauled education finance system intending to distribute more state funding based on student need. In the period during and following the Great Recession, the State of New York chose to cut districts’ foundation aid, a form of aid designed to be distributed progressively, halting any improvement in equity. Had funding been distributed to districts according to the foundation formula that was specified by the education finance reform legislation passed in 2007, high poverty districts would have received more funding and disparities in funding across districts with similar characteristics would have been reduced. I also show that other forms of aid, which are regressively distributed, could have been cut instead of foundation aid, allowing for more funding to flow to high poverty districts in a time of fiscal constraint.

Discriminant AnalysisFor Cluster Validation In A Case Study of District Grouping In Jember Regency Based On Poverty

Cluster validation is a procedure to evaluate the results of cluster analysis quantitively and objectively on a data. The validation process is very important to get the results of a good and appropriate grouping. In the validation process, the author uses internal validation, stability, and discriminant analysis test. This study aims to obtain validation results from the hierarchy and kmeans method. This data grouping uses “iris” simulation data, which results from the grouping method used can be applied to the original data to see which vaidation method is used for all data and produce an optimal grouping. The result of the study show that in the “iris” data, a single linkage link is an appropriate grouping method because the result of the grouping are optimal for all validations and classification of group members whose groups are significant. In District poverty data in Jember Regency with a single linkage link optimal grouping was obtained and complete linkage links were also used as a method that resulted in optimal groupig for all validation. Cluster validation discriminant analysis test is appropriate for various types of data in general annd shows that single linkage methods are better than other methods for grouping and validation methods for “iris” data and District data in Jember Regency based on variabels of poverty status. Keywords: Cluster Analysis, Diskriminant Analysis, Multivariate Analysis, Validation Cluster.

Does the Measurement Matter? Assessing Alternate Approaches to Measuring State School Finance Equity for California’s Local Control Funding Formula

Scholars have not reached consensus on the best approach to measure state school finance equity. The regression-based approach estimates the relationship between district poverty rate and funding level, controlling for other district cost factors. A second commonly used approach involves estimating the weighted average funding level for low-income students or other subgroups. Meanwhile, policymakers have preferences for their own data systems and poverty indicators when reading reports and assessing progress. We constructed parallel, district-level panel data sets using data from the California Department of Education and the U.S. Census. We estimated changes over time in district-level school finance equity under California’s Local Control Funding Formula, using multiple school finance measurement approaches, with each of the two data sets. Our results show that different methods and analytic choices result in policy-relevant differences in findings. We discuss the implications for policy and future research.

Lifetime cancer risks from hazardous air pollutants in US public school districts

BackgroundChildren are sensitive to the health impacts of environmental contaminants, but research assessing outdoor environmental exposures for children and schools is underdeveloped. There are no national-level studies examining geographical and...

FUZZY LOGIC APPROACH IN DETERMINING POOR FAMILIES IN THE POVERTY DATABASE IN MALANG DISTRICS

Determination of poor families in the poverty database is still less than perfect. There is still no multi criteria decision making (MCDM) technique in the grouping of poor families, making the results of the criteria in grouping poor families still far from expectations. So, this article discusses the use of the multi criteria decision making (MCDM) technique for grouping poor families in the poverty database in the Malang district. Fuzzy logic is one technique of MCDM which is commonly used for affirmation of decisions. In a random sampling of 35 families taken from the Malang District poverty database, the classification that was originally obtained was only obtained by 2 (two) classifications of poor families, namely: very poor families and poor families. But after it was calculated using the Fuzzy Logic method, it was found 3 (three) classifications of poor families, namely very poor families, poor families, and almost poor families. The magnitude of the distribution of the poor family classification is: 17 (seventeen) very poor families which previously were 14 (fourteen), 17 (seventeen) families were categorized as poor families that were previously 21 (twenty one), and 1 (one) family in the category of near-poor families that were not previously found. With these results, it can be concluded that the Fuzzy Logic method can and is able to provide better and more diverse results in determining poor families in the Malang District poverty database.

An evaluation of vegetation health and the socioeconomic dimension of the vulnerability of Jharkhand state of India in climate change scenarios and their likely impact: a geospatial approach

Abstract Geospatial evaluation of various datasets is extremely important because it gives a better comprehension of the past, present and future and can therefore be significantly utilized in effective decision making strategies. This study examined the relationships, using geospatial tools, between various diversified datasets such as land use/land cover (LULC), long term Normalized Difference Vegetation Index (NDVI) based changes, long term forest fire points, poverty percentage, tribal percentage, forest fire hotspots, climate change vulnerability, agricultural vulnerability and future (2030) climate change anomalies (RCP-6) of Jharkhand state, India, for a better understanding and knowledge of its vegetation health, LULC, poverty, tribal population and future climate change impact. The long term NDVI (1982-2006) evaluation revealed negative change trends in seven northwest districts of Jharkhand state, these were: Hazaribag, Ramgarh, Palamu, Lohardaga, Chatra, Garhwa and Latehar. The forests as well as the agriculture of these districts have lost their greenness during this period. The forest fire frequency events were found to be more pronounced in the land use/land cover of “tropical lowland forests, broadleaved, evergreen, &lt;1000 m” category, and were roughly twice the intensity of the “tropical mixed deciduous and dry deciduous forests” category. In the nine districts of Jharkhand it was found that 40 % of the population was living below the poverty line which is around twice the national average. The highest poverty districts, in percentage, were: Garwah (53.93), Palamu (49.24), Latehar (47.99) and Chatra (46.2). The southwest and south of Jharkhand state shows a tribal population density of more than 40%. The climate change vulnerability was found to be highest in the district of Saraikela followed by Pashchim Singhbhum, whereas agricultural vulnerability was found to be highest in the district of Pashchim Singhbhum followed by Saraikela, Garhwa, Simdega, Latehar, Palamu and Lohardaga. The temperature anomalies prediction for the year 2030 shows an increasing trend in temperature with values of 0.8°C to 1°C in the state of Jharkhand. The highest increases were observed in the districts of Pashchim Singhbhum, Simdega and Saraikela. Based on these evaluations we can conclude that a few of the districts of Jharkhand, such as Pashchim Singhbhum, Garhwa, Palamu and Latehar need to be prioritized for development on an urgent basis. The outcomes of this study would certainly guide the policymakers to prepare more robust plans when keeping in mind the future climate change impacts for the prioritization of various districts of Jharkhand which suffer from extreme poverty, diminished livelihood and insignificant agricultural productivity for the betterment of the people of Jharkhand based on their adaptive capacity.