Number Of Small Areas Research Articles

Moran’s Index Spatial Analysis On The New Case Detection Rate of Leprosy in West Java 2022

Introduction: Leprosy is a chronic infectious disease, neglected tropical disease caused by Mycobacterium leprae. Leprosy is divided into two types based on the number of lesions on the skin. If there are less than 5 lesions, leprosy is classified as paucibacillary (PB) and if there are more than 5 lesions, leprosy is classified as multibacillary (MB). Indonesia is ranked third in the world after Brazil with 762 new cases of grade 2 disability leprosy by 2022. Therefore, spatial analysis of the new case detection rate of leprosy in West Java 2022 is needed. Objective: To determine wheter there are spatial dependency with the new case detection rate of leprosy in West Java 2022 Method: Quantitative research that utilises secondary data by conducting spatial analysis on the new case detection rate of leprosy in West Java 2022 using Moran's Index and LISA tests. Result: There are 5 areas namely Bekasi, Karawang, Subang, Indramayu, and Cirebon that have not yet reached the national target in eliminating leprosy. The value of Moran's I = 0.241 and p-value 0.0090 < ? (0.05) which means that there is a weak positive spatial dependence on the number of new leprosy cases in West Java Province 2022. There is 1 region that is in the high-high quadrant, namely Cirebon and there are 4 regions in the low-low quadrant, namely Cimahi City, Bandung City, Garut, and Tasikmalaya. Conclusion: A small number of areas in West Java Province still have not reached the national target of eliminating leprosy, which is a CDR of <5 per 100.000 population, namely Bekasi, Karawang, Subang, Indramayu, and Cirebon. There are 5 areas that have spatial linkages in the new case detection rate of leprosy in West Java Province in 2022 based on the significance value. The region in the high-high quadrant is Cirebon and there are 4 regions in the low-low quadrant, namely Cimahi City, Bandung City, Garut District, and Tasikmalaya District.

The impact of global cropland irrigation on soil carbon dynamics

Irrigation can increase crop yields and could be a key climate adaptation strategy. At present, under the background of increasing food demand and continuous expansion of irrigation cropland, there still uncertainties about the soil carbon dynamics under the change of irrigation water volume and irrigated area in view of large-scale spatial heterogeneity. Therefore, this paper uses space-for-time + meta-analysis and a two-step methodology based on the residual trend analysis to quantitatively analyze the relationship between soil organic carbon (SOC) and soil respiration (Rs) in response to fluctuations in irrigation water volume and irrigated land extents. Here we show that the irrigation water volume within 100–1000 mm had a negative impact on SOC, and the impact was correlated with the irrigation water volume. Different levels of irrigation water manifest distinct effects on SOC content across varying soil depths. When irrigation water quantities are less than 700 mm, the impact on SOC content in the 0–30 cm depth layer surpasses that in the 30–200 cm depth layer. Conversely, when irrigation water quantities equal or exceed 700 mm, this pattern is reversed. The overall impact of irrigation on SOC stock at a depth of 0–200 cm was −14.88±6.7%. Tillage, planting intensity, topography, and soil type within irrigated cropland all exert variable impacts on SOC content. Whether these influences are deleterious or beneficial hinges predominantly upon the balance between the augmentation of SOC stock due to heightened carbon inputs from crops and the reduction of SOC through alterations in microbial activity. Mann-Kendall trend analysis showed that from 2000 to 2015, the overall Rs of cropland showed an increasing trend, with an increase rate of 3.67 g/m2/year. The increase of global Rs is mainly driven by climate change factors (temperature, precipitation and solar radiation), while the decrease of Rs in a small number of areas is mainly driven by management practices (fertilizer nitrogen, irrigation, and tillage). Our study further quantifies the impact of irrigation on soil carbon dynamics, thereby offering potential pathways and data support for the advancement of sustainable agriculture.

고등학생 대상의 포토보이스를 활용한 지속가능한 식생활에 대한 개념 인식 탐색

This study aimed to identify sustainable dietary practices, concepts, and factors that hinder practice from the perspective of high school students and suggest what the current sustainable dietary education should be supplemented. The study utilized photovoice to compare and analyze the differences in sustainable dietary practices based on the implementation of sustainable dietary education among high school students. A group of 7 participants, who did not provide sustainable dietary education, submitted 65 photos, while a group of 8 participants who underwent the education submitted 80 photos. The results of this study showed that the overall awareness of sustainable diet concepts among all participants was highest in the health area (66.54%), followed by the environment (23.74%) and consideration (9.73%). Categorizing the results according to the sustainable dietary area revealed a diverse range of concepts extracted through sustainable dietary education, with an increase in the number of small areas. It was observed that the majority of the research participants were consuming foods with high sugar and sodium content, and they tended to consume more delivery food or dining out rather than cooking meals themselves. Interview results also indicated that while students demonstrated a conceptual understanding of sustainable dietary habits, there were cases where this understanding needed to translate into practical actions. This study is significant in utilizing the qualitative research method of photovoice to uncover the meaning and practical examples of sustainable dietary habits from the perspective of high school students. The results can be utilized to enhance and guide sustainable dietary education programs.

Identifying the Minimum Number of Flood Events for Reasonable Flood Peak Prediction of Ungauged Forested Catchments in South Korea

The severity and incidence of flash floods are increasing in forested regions, causing significant harm to residents and the environment. Consequently, accurate estimation of flood peaks is crucial. As conventional physically based prediction models reflect the traits of only a small number of areas, applying them in ungauged catchments is challenging. The interrelationship between catchment characteristics and flood features to estimate flood peaks in ungauged areas remains underexplored, and evaluation standards for the appropriate number of flood events to include during data collection to ensure effective flood peak prediction have not been established. Therefore, we developed a machine-learning predictive model for flood peaks in ungauged areas and determined the minimum number of flood events required for effective prediction. We employed rainfall-runoff data and catchment characteristics for estimating flood peaks. The applicability of the machine learning model for ungauged areas was confirmed by the high predictive performance. Even with the addition of rainfall-runoff data from ungauged areas, the predictive performance did not significantly improve when sufficient flood data were used as input data. This criterion could facilitate the determination of the minimum number of flood events for developing adequate flood peak predictive models.

Detection approach for GEO space objects with a wide-field optical telescope array.

In 2020, Changchun Observatory developed a 280 mm wide-field optical telescope array to improve surveillance of space debris in the geosynchronous belt. There are many advantages including a wide field of view, the ability to observe a large area of sky and high reliability. However, the wide field of view causes a significant number of background stars to appear in the image when photographing space objects, making it difficult to detect them. This research focuses on the precise detection of GEO space objects from images taken by this telescope array in order to position them in large quantities. Our work further investigates the motion feature of an object, namely that the object can be seen as being in a uniform linear motion for a brief length of time. Based on this feature, the belt can be divided into a number of smaller areas and the telescope array scans each smaller area one at a time from east to west. To detect objects in the subarea, a combination of image differencing with trajectory association is used. The image differencing algorithm is used to remove most stars and screen out suspected objects in the image. Next, the trajectory association algorithm is employed to further filter out the real objects among the suspected ones, and the trajectories attributed to the same object are linked. The feasibility and accuracy of the approach were verified by the experiment results. The accuracy rate of trajectory association exceeds 90% and on average, more than 580 space objects can be detected per observation night. Since the J2000.0 equatorial system can accurately describe the apparent position of an object, the object can be detected by using this coordinate system as opposed to the pixel coordinate system.

Big problems in spatio-temporal disease mapping: Methods and software

Background and objective: Fitting spatio-temporal models for areal data is crucial in many fields such as cancer epidemiology. However, when data sets are very large, many issues arise. The main objective of this paper is to propose a general procedure to analyze high-dimensional spatio-temporal areal data, with special emphasis on mortality/incidence relative risk estimation.Methods: We present a pragmatic and simple idea that permits hierarchical spatio-temporal models to be fitted when the number of small areas is very large. Model fitting is carried out using integrated nested Laplace approximations over a partition of the spatial domain. We also use parallel and distributed strategies to speed up computations in a setting where Bayesian model fitting is generally prohibitively time-consuming or even unfeasible.Results:Using simulated and real data, we show that our method outperforms classical global models. We implement the methods and algorithms that we develop in the open-source R package bigDM where specific vignettes have been included to facilitate the use of the methodology for non-expert users.Conclusions: Our scalable methodology proposal provides reliable risk estimates when fitting Bayesian hierarchical spatio-temporal models for high-dimensional data.

Spatial Disparities and Correlated Variables of Community Care Facility Accessibility in Rural Areas of China

With the increasing aging of the world’s population, research on the equitable allocation of elderly care facilities has received increasing attention, but measuring the accessibility of community care facilities (CCFs) in rural areas has received little attention. In this study, which covered 7985 CCFs in 223,877 villages, we measured the accessibility of CCFs in rural areas of Hubei Province by using the nearest distance method. Based on the accessibility calculation, the spatial disparities and agglomeration characteristics of spatial accessibility were analyzed, and the correlated variables related to the accessibility were analyzed from both natural environment and socioeconomic aspects by employing a geographically weighted regression (GWR) model. Our results show that 87% of villages have a distance cost of less than 7121 m and 81% of townships have a distance cost of less than 5114 m; good spatial accessibility is present in the eastern and central regions, while poor spatial accessibility is shown in a small number of areas in the west. The results from the clustering analysis show that the hot spot areas are mainly clustered in the western mountainous areas and that the cold spot areas are mainly clustered around Wuhan city. We also observed that area, elevation, population aged 65 and above, and number of villages are significantly correlated with accessibility. The results of this study can be used to provide a reference for configuration optimization and layout planning of elderly care facilities in rural areas.

ENSO impacts child undernutrition in the global tropics

The El Niño Southern Oscillation (ENSO) is a principal component of global climate variability known to influence a host of social and economic outcomes, but its systematic effects on human health remain poorly understood. We estimate ENSO’s association with child nutrition at global scale by combining variation in ENSO intensity from 1986-2018 with children’s height and weight from 186 surveys conducted in 51 teleconnected countries, containing 48% of the world’s under-5 population. Warmer El Niño conditions predict worse child undernutrition in most of the developing world, but better outcomes in the small number of areas where precipitation is positively affected by warmer ENSO. ENSO’s contemporaneous effects on child weight loss are detectable years later as decreases in height. This relationship looks similar at both global and regional scale, and has not appreciably weakened over the last four decades. Results imply that almost 6 million additional children were underweight during the 2015 El Niño compared to a counterfactual of neutral ENSO conditions in 2015. This demonstrates a pathway through which human well-being remains subject to predictable climatic processes.

Transformed Fay-Herriot model with measurement error in covariates

Statistical agencies are often asked to produce small area estimates (SAEs) for positively skewed variables. When domain sample sizes are too small to support direct estimators, effects of skewness of the response variable can be large. As such, it is important to appropriately account for the distribution of the response variable given available auxiliary information. Motivated by this issue and in order to stabilize the skewness and achieve normality in the response variable, we propose an area-level log-measurement error model on the response variable. Then, under our proposed modeling framework, we derive an empirical Bayes (EB) predictor of positive small area quantities subject to the covariates containing measurement error. We propose a corresponding mean squared prediction error (MSPE) of EB predictor using both a jackknife and a bootstrap method. We show that the order of the bias is where m is the number of small areas. Finally, we investigate the performance of our methodology using both design-based and model-based simulation studies.

Scalable Bayesian modelling for smoothing disease risks in large spatial data sets using INLA

Several methods have been proposed in the spatial statistics literature to analyse big data sets in continuous domains. However, new methods for analysing high-dimensional areal data are still scarce. Here, we propose a scalable Bayesian modelling approach for smoothing mortality (or incidence) risks in high-dimensional data, that is, when the number of small areas is very large. The method is implemented in the R add-on package bigDM and it is based on the idea of “divide and conquer“. Although this proposal could possibly be implemented using any Bayesian fitting technique, we use INLA here (integrated nested Laplace approximations) as it is now a well-known technique, computationally efficient, and easy for practitioners to handle. We analyse the proposal’s empirical performance in a comprehensive simulation study that considers two model-free settings. Finally, the methodology is applied to analyse male colorectal cancer mortality in Spanish municipalities showing its benefits with regard to the standard approach in terms of goodness of fit and computational time.

Dealing with risk discontinuities to estimate cancer mortality risks when the number of small areas is large

Many statistical models have been developed during the last years to smooth risks in disease mapping. However, most of these modeling approaches do not take possible local discontinuities into consideration or if they do, they are computationally prohibitive or simply do not work when the number of small areas is large. In this paper, we propose a two-step method to deal with discontinuities and to smooth noisy risks in small areas. In a first stage, a novel density-based clustering algorithm is used. In contrast to previous proposals, this algorithm is able to automatically detect the number of spatial clusters, thus providing a single cluster structure. In the second stage, a Bayesian hierarchical spatial model that takes the cluster configuration into account is fitted, which accounts for the discontinuities in disease risk. To evaluate the performance of this new procedure in comparison to previous proposals, a simulation study has been conducted. Results show competitive risk estimates at a much better computational cost. The new methodology is used to analyze stomach cancer mortality data in Spanish municipalities.

厦门市地面沉降影响分析与风险评价

PDF HTML阅读 XML下载 导出引用 引用提醒 厦门市地面沉降影响分析与风险评价 DOI: 10.5846/stxb202007031727 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金（41571148）；中国科学院战略性先导科技专项（XDA23030103） Impact analysis and risk assessment of urban land subsidence in Xiamen City Author: Affiliation: Fund Project: the National Natural Science Foundation of China(41571148); the Strategic Priority Research Program (A) of the Chinese Academy of Sciences(XDA23030103) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:城市的地面沉降一直以来都是沿海城市密切关注的问题。在沿海城市对高层建筑群诱发的地面沉降进行深入研究具有重要意义。获取地面沉降监测点的高程信息，分析了厦门市地面沉降情况与空间差异性。重点探讨建设用地、建筑容积率与土地利用转化信息这3种因素对厦门市地面沉降的影响，并对厦门岛地面沉降风险进行评估，可为厦门岛的高层建筑三维空间布局与优化研究提供先验知识。结果表明：（1）在2001-2015年厦门市整体的高程存在下降的趋势且较为缓慢，同时厦门市的城市建设规模不断变大且建设面积增速不断增大，与厦门市的城市地面的沉降发展趋势相同。（2）厦门市的高层建筑密度需达到一定程度才对地面沉降有影响，厦门岛的建筑容积率与地面沉降没有存在显著的相关性。（3）在不同土地利用转化建设用地对地面沉降的影响中：原水域用地沉降最为明显，建成时间越长，总沉降量越大；原耕地用地转化成高层建筑之后沉降有明显发生，也随着建成时间的延长有逐渐增加沉降量的态势；原园地、林地用地沉降只在少部分地区发生且与建成时间无明显差别。（4）厦门岛可用于城市建设区域中的53.34%是地面沉降中风险等级以上的。此外，本研究给出防御防治城市地面沉降风险的有效建议与措施。本研究结果可为厦门市城市化进程中的地面沉降监测、规划设计与决策等提供重要的数据支撑，为城市可持续发展提供科学的依据；也可为国内外其他城市的地面沉降监测分析和可持续发展研究提供理论依据和借鉴意义。 Abstract:Urban land subsidence has always been a problem that coastal cities pay close attention to. It is of great significance to conduct in-depth research on land subsidence which was induced by high-rise buildings in the coastal cities. In this study, the elevation information of land subsidence monitoring sites were firstly obtained to analyze the spatial difference of land subsidence in Xiamen City. Secondly, the influence of construction land, floor area ratio and land use conversion information on land subsidence in Xiamen City was discussed emphatically. The land subsidence risks of Xiamen Island were evaluated, which can provide a priori knowledge for the research on three-dimensional layout and optimization of high-rise buildings in Xiamen Island. The results show that: (1) from 2001 to 2015, the overall elevation of Xiamen City presented a downward trend and was slow. Meanwhile, the urban construction scale continued to increase and the construction area increased in Xiamen City, which were the same as the development trend of urban land subsidence in Xiamen City. (2) The density of high-rise buildings needs to reach a certain degree to have an impact on the land subsidence in Xiamen City. There is no significant correlation between floor plot ratio and land subsidence in Xiamen Island. (3) Among the impacts of land subsidence due to different land transformed construction land, the original water area total land subsidence is the most obvious. The longer the completion time, the greater the amount of subsidence. After the original cultivated land conversion into high-rise buildings, the settlement obviously occurred. With the extension of the construction time, the settlement increases gradually. The settlement of the original garden and forest land only occurs in a small number of areas and there is no significant difference from the completion time. (4) Xiamen Island can be used in urban construction areas where 53.34% of it is above the risk level of land subsidence. In addition, the effective suggestions and measures are also given to prevent the risk of urban land subsidence in this study. The results of this study can provide important data support for land subsidence monitoring, planning, design and decision-making in the urbanization process of Xiamen City, and provide a scientific basis for the sustainable development of the cities. The results can also provide theoretical basis and reference significance for the land subsidence monitoring analysis and sustainable development research for other cities at home and abroad. 参考文献 相似文献 引证文献

Small Area Estimation of Sub-District’s Per Capita Expenditure through Area Effects Selection using LASSO Method

Abstract This paper discusses a small area estimation (SAE) problem when the number of small areas is relatively small compared to size of the observations. This problem is known as a sparsity problem which can caused slow convergence in obtaining the parameter estimates. The sparsity problem on small area can be imposed by assigning zero for i-th area with adequate sample size, whereas it preserve the nonzero value for i-th small area. The sparsity of area specific effects vector brings heavy tails if the SAE method cannot properly handle this complexity of specific area effect characteristic. Thus, the aim of this study is to investigate the sparsity issue by developing small area estimation model using the LASSO method to shrinkage the parameter estimates and select the area specific effects properly. The simulation results showed that the LASSO method produced the smallest mean square error (MSE) while the precision of the prediction were not significantly different when compared to other methods. The LASSO method was also applied to estimate the mean of per capita expenditure of sub-district levels in Kepulauan Bangka Belitung Province and produced smaller MSE when compared to other methods.

Trends in the Territorial Organization of Industry in Post-Soviet Russia and Their Potential Environmental Consequences

The trends of the territorial organization of industry associated with new industrial construction in post-Soviet Russia are determined. An exceptionally high territorial differentiation of industrial construction was revealed. This is manifested in its concentration in a small number of areas and in its superconcentration in the metropolitan regions. The northern and eastern regions of the country are distinguished by focal industrial development and the dominance of mining enterprises. In Asian Russia, the Yamalo-Nenets and Khanty-Mansi autonomous okrugs are distinguished by large-scale industrial construction, but the density of development is low. Against the total fragmentation of the single national economic complex in the course of market reforms, certain signs of spontaneous complexation appeared. In the new economic conditions, new industries have arisen: the extraction of oil and natural gas resources on the shelf of the Okhotsk, Caspian, Baltic, Pechora and Black seas; liquefied natural gas plants; and car assembly enterprises. Analysis of the structure of the new industrial construction revealed no signs of its greening. Two-thirds of the new facilities are related to basic, environmentally aggressive industries. The increasing concentration of industry in a few areas of economic activity, oriented mainly outward, is the main vector of Russia’s territorial development. A similar vector prevailing in the conditions of the market element is poor in quality from both environmental and geopolitical aspects.

Task-Dependent Changes in the Large-Scale Dynamics and Necessity of Cortical Regions

Neural activity throughout the cortex is correlated with perceptual decisions, but inactivation studies suggest that only a small number of areas are necessary for these behaviors. Here we show that the number of required cortical areas and their dynamics vary across related tasks with different cognitive computations. In a visually guided virtual T-maze task, bilateral inactivation of only a few dorsal cortical regions impaired performance. In contrast, in tasks requiring evidence accumulation and/or post-stimulus memory, performance was impaired by inactivation of widespread cortical areas with diverse patterns of behavioral deficits across areas and tasks. Wide-field imaging revealed widespread ramps of Ca2+ activity during the accumulation and visually guided tasks. Additionally, during accumulation, different regions had more diverse activity profiles, leading to reduced inter-area correlations. Using a modular recurrent neural network model trained to perform analogous tasks, we argue that differences in computational strategies alone could explain these findings.

Economic analysis of distributed solar photovoltaics with reused electric vehicle batteries as energy storage systems in China

As the development of distributed solar photovoltaics (DSPV), battery energy storage systems are growing in popularity to promote the performance of DSPV, for both mitigating the impact on the grid and increasing the economic performance. In the meantime, reused batteries retired from electric vehicles provide another option for energy storage systems. In this paper, the economic performance evaluated by net present value of DSPV with reused batteries as energy storage systems (RBESS) is studied at the provincial level in China, so as to depict a comprehensive understanding of the current DSPV status and the development potential of RBESS in China. Instead of only focusing on the role of increasing the percentage of self-consumption (Model 1), a combined usage strategy of RBESS (Model 2) is also proposed. Moreover, the residual value of lithium-ion batteries is taken into consideration in this paper. Our results show that for the residential sector, only a small number of areas can benefit from RBESS under Model 2, while none can benefit under Model 1. Only when the price of RBESS decreases to 200 CNY/kWh, the profitability could be nationwide. However, for the commercial and industrial sector, the performance of RBESS is much better. In the majority of areas, integrating RBESS with DSPV is already profitable.

Understanding spatio-temporal electricity demand at different urban scales: A data-driven approach

Cities and communities worldwide are seeking to become more sustainable by transitioning to renewable energy resources, and by introducing electric transportation and heating. The impact and suitability of such technologies for a given area heavily depend on local conditions, such as characteristics of local demand. In particular, the shape of a local demand profile is an important determinant for how much renewable energy can be used directly, and how charging of electric vehicles and use of electric heating affect a local grid. Unfortunately, a systematic understanding of local demand characteristics on different urban scales (neighbourhoods, districts and municipalities) is currently lacking in literature. Most energy transition studies simplify local demand to household demand only. This paper addresses this knowledge gap by providing a novel data-driven classification and analysis of demand profiles and energy user compositions in nearly 15000 neighbourhoods, districts and municipalities, based on data from the Netherlands. The results show that on all urban scales, three types of areas can be distinguished. In this paper, these area types are termed “residential”, “business” and “mixed”, based on the most prevalent energy users in each. Statistic analysis of the results shows that area types are pairwise significantly different, both in terms of their profiles and in terms of their energy user composition. Moreover, residential-type demand profiles are found only in a small number of areas. These results emphasise the importance of using local detailed spatio-temporal demand profiles to support the transition of urban areas to sustainable energy generation, transportation and heating. To facilitate the implementation of the obtained insights in other models, a spreadsheet modelling tool is provided in an addendum to this paper.

Modeling Random Effects Using Global–Local Shrinkage Priors in Small Area Estimation

ABSTRACTSmall area estimation is becoming increasingly popular for survey statisticians. One very important program is Small Area Income and Poverty Estimation undertaken by the United States Bureau of the Census, which aims at providing estimates related to income and poverty based on American Community Survey data at the state level and even at lower levels of geography. This article introduces global–local (GL) shrinkage priors for random effects in small area estimation to capture wide area level variation when the number of small areas is very large. These priors employ two levels of parameters, global and local parameters, to express variances of area-specific random effects so that both small and large random effects can be captured properly. We show via simulations and data analysis that use of the GL priors can improve estimation results in most cases. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Indoor Positioning Algorithm Based on Nonlinear PLS Integrated With RVM

Indoor positioning based on received signal strength indicator of WLAN has received more and more attention because of low cost and easy implementation. However, traditional localization algorithms often fail to achieve better positioning results because of multi-path effect and shadow effect. In order to solve the problem of multi-collinearity and more noise in WLAN indoor location data, this paper presents a novel nonlinear partial least square (PLS) method to address the problem of low precision in WLAN location. The proposed method integrates an inner relevant vector machine (RVM) function with an external linear PLS framework. First, the localization area is divided into a number of small areas by K-means algorithm. Then, PLS is applied to extract the features of the fingerprint database to reduce the number of the variable dimensions and eliminate the correlations. The obtained score matrices are used as the input and output of RVM. Finally, the coordinates of test points are regressed and predicted by the RVM-PLS algorithm. Simulation and experiments in real scenario prove the effectiveness of the proposed method. Compared with SVM-PLS, RBF-PLS, SVM-PCA, EBQPLS, PLS, SVM, RBF, RVM, and WKNN algorithm, the experimental results show that the proposed algorithm has higher positioning accuracy.

Success Factors in R&D Leadership

OVERVIEW:A wealth of information and research is available about business leadership in general and its importance, but much less attention has been given to the nature of R&D leadership specifically. Given the importance of innovation to most organizations’ success, and even survival, understanding the skills and attributes required to achieve success in R&D management is critical. To address this gap, IRI’s Research-on-Research working group R&D Leadership Skills and Styles sought to both identify leadership skills and attributes important to R&D success and to assess the relative importance of known leadership attributes for R&D compared to other business units. After identifying key themes in innovation leadership through a series of interviews with established technical leaders, the group embarked on comprehensive analyses of data from CCL leadership survey instruments to explore these themes. The analyses reveal a general perception that R&D managers are typically not as effective as those outside R&D on the majority of leadership skill measures, except in a small number of areas, most of which are technical. Further analysis of the interview trends and data suggests unique opportunities to develop successful R&D leaders by maintaining technical insight while expanding interpersonal skills. In other areas, R&D leaders would be well served by work to develop the leadership skills and attributes identified as generally important, as they are widely perceived as lacking a number of these attributes.