Location Database Research Articles

Human insecurity and child deaths in conflict: evidence for improved response in Yemen.

Since the beginning of the ongoing conflict in Yemen, >23 000 air strikes and >100 000 fatalities have been recorded. Data from Yemen Data Project linked >1300 child fatalities and >900 child injuries to air raids. However, there is little literature on the effect of the protracted armed conflict on the pattern of child mortality using data from small-scale surveys. We aimed to identify the pattern of the death rate for children aged <5 years ('under-5') and its relationship with human insecurity in Yemen. We created a human insecurity index (i.e. severely insecure vs insecure) for the 22 governorates in Yemen from 2015 to 2019, using data from the Armed Conflict Location and Event Database. We matched this insecurity index with the corresponding under-5 mortality data from the Complex Emergency Database. We analysed the relationship between the under-5 death rate (U5DR) and the insecurity level using a Bayesian finite mixture model in order to account for unobserved heterogeneity in clustered finite subsets of a population. We extracted 72 surveys and 77.8% (n = 56) were included in this study. The mean of the recall period for mortality was 106 days with a standard deviation of 93 days. We identified two subpopulations: Subpopulation I-high average number of child deaths and Subpopulation II-low average number of child deaths. The log posterior mean of the U5DR is 1.10 (95% credible intervals: 0.36, 1.82) in the severely insecure group in Subpopulation I and 3-fold the estimate in Subpopulation II. However, in Subpopulation II, we found no association between the insecurity level and the U5DR. The pattern of child deaths is crucial in understanding the relationship between human insecurity and the U5DR.

Mapping the spatial distribution of global mariculture production

Mariculture (marine and brackish water aquaculture) has grown rapidly over the past 20 years, yet publicly-available information on the location of mariculture production is sparse. Identifying where mariculture production occurs remains a major challenge for understanding its environmental impacts and the sustainability of individual farms and the sector as a whole. We compiled known mariculture locations and applied a simple production-allocation approach to map remaining global mariculture locations across 73 countries using the key determinants of distance to shore and ports, and average productivity (tonnage) of known farms. Our map represents 96% of reported fish and invertebrate mariculture production for 2017, but excludes algae which constitutes half of global mariculture production. We provide, for the first time, a publicly available spatial database of known and estimated mariculture locations. We discuss the utility and limitations of the existing data and our modeling approach, and highlight the key data gaps and future challenges for mapping aquaculture. Our results provide a vital resource for mariculture and environmental researchers, but we emphasize the need for a standardized, ground-truthed global spatial database of aquaculture locations and farm-level attributes (e.g., species, production type) to better understand the distribution of production and adequately plan for future growth.

A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks.

Indoor localization is an important issue for indoor location-based services. As opposed to the other indoor localization approaches, the radio frequency (RF) based approaches are low-energy solutions with simple implementation. The kernel learning has been used for the RF-based indoor localization in 2D environment. However, the kernel learning has not been used in 3D environment. Hence, this paper proposes a multi-kernel learning scheme for 3D indoor localization. Based on the signals collected in the area of interest, the WiFi signals with better quality and closer to the user are selected so as to reduce the multipath effect and the external interference. Through the construction of multi-kernel, the localization accuracy can be improved as opposed to the localization based on the single kernel. We build multiple kernels to get the user’s location by collecting wireless received signal strengths (RSS) and signal-to-noise ratios (SNR). The kernel learning maps data to high dimension space and uses the optimization process to find the surface where the data are mapped. By multi-kernel training, the surface is fine-tuned and eventually converges to form the location database during the mapping process. The proposed localization scheme is verified by the real RSS and SNR collected from multiple wireless access points (AP) in a building. The experimental results verify that the proposed multi-kernel learning scheme performs better than the multi-DNN scheme and the existing kernel-based localization schemes in terms of localization accuracy and error in 3D indoor environment.

How shareholders react to sustainable narratives about leading European energy companies? An event study using sentiment data from the global database for events, language and tone (GDELT)

ABSTRACT This study aims to investigate how the shareholders of leading European energy companies value sustainability narratives. It uses news from the Global Database of Events, Location, Language, and Tone (GDELT) and analyses the cumulative average abnormal returns (CAAR) and abnormal volatilities (AV), incorporating the event study methodology. A total of 279,546 big news items were used, and 4,026 event studies were conducted. The extensive analysis of data and the segmentation of the news by tone, type of energy generation and environmental consequences helps to understand shareholders’ investment decisions. This study found that the sustainability narrative significantly impacts shareholder value; however, this narrative’s interpretation has no consensus. The sustainability news about these companies moves the stock market upwards for some shareholders, while others do the contrary. These results are observed by comparing CAAR and AV. The results found by this article are crucial for regulators to push forward an effective ecological transition. It should be legislated so that there is a common shareholders’ narrative, discouraging highly polluting investments.

Revolutionary Insurgents or Conservative Reactionaries? National Liberation Army’s Transnational Expansion in Colombia and Venezuela

The objective of this article is to document the spatial diffusion of the National Liberation Army (ELN) in Colombia and Venezuela. Building on a comprehensive database of more than 2100 violent incidents collected by the Armed Conflict Location & Event Database (ACLED) project and the Global Terrorism Database (GTD), the paper examines whether the ELN has expanded or relocated into Venezuela following the peace accords with the Revolutionary Armed Forces of Colombia (FARC) in 2016. We find that the vast majority of events and fatalities involving the ELN are located in Colombia. Recent years, however, have seen a significant increase in armed actions by the ELN on Venezuelan soil. This expansion was facilitated by the ultimate demobilization of the FARC and the ongoing social, political, and economic implosion that has wracked Venezuela since 2014. The ELN is a typical case of diffusion by escalation marked by a growth in violent events and fatalities in both the country of origin of armed groups and their new destination.

Spatio-Temporal Analysis of Boko Haram Attacks and its Effects on Education in Northern Nigeria (2009-2020)

The persistent attacks from Boko Haram have been issues of concern to the Nigerian government and its citizens. The study assessed spatio-temporal pattern of attacks by Boko-Haram insurgents and its effects on education in the Northern Region of Nigeria between 2009 and 2020 with specific objectives of ascertaining the pattern of the attacks, and appraising the trend of attacks’ fatalities over time, and reviewing the effects of the attacks on education. The study covered all three geopolitical zones (19 States with FCT) in the Northern Region of Nigeria. Data on Boko-Haram attacks were obtained from the Armed Conflict Location and Event Data Project (ACLED) database. Nearest Neighbourhood Analysis and Geographically Weighted Regression on ArcGIS 10.8 were analytical techniques adopted in this study. Results with a negative Z-value of -88.62 indicated the clustered pattern of Boko-Haram at an observed mean distance of 1213 metres from one attack to another. There were 28 792 reported fatalities due to Boko-Haram attacks from 2009 and 2020. Boko Haram has affected the education system of the Northern Region of Nigeria through attacks and abduction of students. The study revealed that geospatial technology has the potential to analyse and monitor insecurity issues, and it is thus recommended that geospatial technology need to be integrated into security surveillance and operation to curb the challenges of insecurity on education, not only in the northern region but the entire space of Nigeria, so that Sustainable Development Goal Four (quality education for all) can be achieved.

Geo-Locating and Identifying Wrong-Way Driving Entrance Points in Bexar County Highways by Implementing Mathematical Modeling and Land-Use Impact Assessment

Wrong-way driving (WWD) leads to high severity crashes and is a major concern for transportation managers. This study aims to identify WWD entry points of urban highway ramps and develop an analysis methodology using basic knowledge of WWD occurrences. The methodology examines the origin and driving behavior of impaired drivers by utilizing a land-use impact assessment (alcohol-serving establishments (ASE) proximity to exit ramps) and analyzing three distinct mathematical models: wrong-way driving events excluding 911 call analysis, wrong-way driving events including 911 call analysis, and 911 calls without wrong-way driving crashes. Data were collected and implemented from Google Maps, the 911 call database, wrong-way crash database, ASE location database, and a video camera database of a recent WWD study. Out of a total 543 exit ramps, 213 exit ramps are associated with approximately 98% of total WWD entries. The hotspots analysis of WWD entrance locations have found four major hotspots locations in Bexar County, Texas study area: 410 Loop near Culebra Road and Jackson Keller Road, 1604 Loop near US-281 highway, and IH-10 near the Medical Drive area. Outcomes of this study include a methodology for determining WWD entry locations of regional highways.

Drought years promote bark beetle outbreaks in Mexican forests of Abies religiosa and Pinus pseudostrobus

Climate change is an important risk factor for forest ecosystems through alteration of forest disturbance regimes such as bark beetle outbreaks, which in some places now are more successfully attacking host trees weakened by hotter drought events. In Mexico, ties between climate and amplified outbreaks of bark beetles have begun to be documented, although these relationships are not entirely clear. This project aims to identify the geographic patterning and relations between climate and bark beetle outbreaks in Abies religiosa and Pinus pseudostrobus populations located in the Trans-Mexican Volcanic Belt region. We used: 1) a database of phytosanitary logging authorizations and locations issued to enable salvage logging of trees infested or killed by bark beetle outbreaks from 2009 to 2018; 2) a previously developed gridded database of contemporary suitable climatic habitat of these tree species that will be lost by 2060 to determine if outbreaks occurred in sites with climatic habitat lost; and 3) elevation values to determine if outbreaks occur at xeric limits (drought limits) of tree hosts. Climate analysis was conducted with TerraClimate data using PDSI (Palmer’s Drought Severity Index) values. We find that bark beetle outbreaks do not necessarily occur at sites where models project that both species will lose suitable climatic habitat. For A. religiosa (Sacred Fir), of the 4091 ha treated by sanitary logging, 40% occurs between 3000 and 3100 m of elevation, where the pure and relatively better-preserved Sacred Fir stands are found. For P. pseudostrobus, more than 54% of the sanitary logging area (4664 ha) ranges from 2200 to 2400 m a.s.l., which coincides with the lower elevation limit (xeric limit) of the natural distribution of the species. Regarding relationships with PDSI, for A. religiosa there was a one-year lag between the year of mortality (expressed by the year of the phytosanitary logging authorization) and the year with the most negative (driest) PDSI anomaly (one year before), while for Pinus pseudostrobus, phytosanitary logging authorizations were concentrated in the driest year. An investigation that considers more variables, especially anthropogenic factors, is necessary to be able to better understand the dynamics of forest pests and prevent unusual outbreaks induced by expected climate-change-amplified droughts.

RAAS inhibitors for hypertensive patients during Covid-19 infection – a metanalysis

Inhibition of the Renin Angiotensin Aldosterone system (RAAS) is the leading form of anti-hypertensive treatment, through blockade of the angiotensin-converting enzyme 2 receptor (ACE2-r) by angiotensin enzyme inhibitors (ACE-is) and angiotensin receptor blockers (ARBs). However, the ACE-r has been found to be a functional receptor for the viral entry of SARS-CoV-2, and increased upregulation of ACE2 by inhibitors could increase the severity of infection. Previous to the SARS-CoV-2 outbreak, typical practice in patients with risk of infection would be to withhold ACE inhibitors due to evidence of renal failure. This thesis reviews the impact of RAAS inhibitor use in COVID-19 patients, and if withholding ACE inhibitors could affect the clinical outcome of patients. A total of 22 studies were located form database searching (PubMed, MedRxiv, Google scholar), 15,784 hypertensive COVID-19 patients were documented, with 8,035 taking ACE-is/ARBs treatment in comparison to 7,024 without. RAAS inhibitors were found to decrease likelihood of mortality (OR 0.68 [0.51, 0.90], p = 0.008), but there was no statistically significant difference between RAAS and Non-RAAS in severity (OR 0.91 [0.63, 1.31], p= 0.62). Following these results there would not be sufficient evidence to remove longstanding RAAS inhibitor medication in COVID-19 patients. Rapid data collected during observational trials can now be utilised in long term randomised controlled studies currently ongoing, such as BRACE-CORONA. This allows the opportunity for further analyses of the association between the RAAS system, new variants, genetic polymorphism and the maintenance of blood pressure.

Nest Distribution of Four Priority Raptor Species in Colorado

ABSTRACT Raptors face threats such as habitat modification, climate change, and environmental pollutants in many parts of the western USA, where rapid human population growth exacerbates such pressures. However, information about distribution of raptor nests at broad spatial scales that could inform conservation efforts is lacking. To provide a contemporary estimate of nest distribution of four raptor species of special conservation concern (Bald Eagle [Haliaeetus leucocephalus], Ferruginous Hawk [Buteo regalis], Golden Eagle [Aquila chrysaetos], and Prairie Falcon [Falco mexicanus]) throughout Colorado, we used a statewide database of raptor nesting locations to inform species distribution models for monitoring and management efforts. We used generalized linear models to identify the relationship between nest locations and explanatory covariates relating to land cover, temperature, topography, and prey distribution. We investigated the effect of different methods for selecting the sample of locations available to raptors, comparing four selection frames: sampling from the observed locations of the target-group (i.e., other raptor nests), sampling from within a spatial buffer around observed locations, sampling from outside of the same buffer, or complete random sampling of the background locations without respect to observations. Out-of-sample validation techniques indicated strong predictive accuracy of our models. Each raptor species was best represented by a different one of the four approaches to sample available locations, refuting our expectation that models accounting for bias would perform better than those that did not. Our findings were consistent with generally understood habitat associations of these species. These models can be used to identify hot spots with high relative probability of use by breeding raptors and to inform future monitoring practices that use a standardized, stratified sampling design.

Estimating the Total Number of Workplace and Public Electric Vehicle Chargers in California

Allocating charging infrastructure to support the plug-in electric vehicle (PEV) market transition is a major challenge for policy makers and planners seeking to promote PEV adoption. Most electric vehicle charging away from home takes place at or near the workplace, but existing research is focused on public charging. This study developed a method to combine charging location data from a variety of sources including users’ self-reports to produce a combined database of workplace and public charging locations. By matching survey responses to charging locations reported in other datasets using spatial clustering methods, we estimated the number of charging locations and chargers in California. This analysis found that charger location datasets from the Alternative Fuels Data Center and Plugshare included many of the same charging locations as each other but missed many chargers that serve commuters; as a result, existing datasets significantly underrepresent the total number of chargers in California. Some 28,175 Level 2 chargers and 7,350 direct current fast chargers by were identified by combining charger location data from the Alternative Fuels Data Center and Plugshare and at least 27,000 additional chargers near commute destinations by adding locations reported in a survey of electric vehicle drivers.

Retrospective IP Address Geolocation for Geography-Aware Internet Services.

The paper deals with the locations of IP addresses that were used in the past. This retrospective geolocation suffers from continuous changes in the Internet space and a limited availability of past IP location databases. I analyse the retrospective geolocation of IPv4 and IPv6 addresses over five years. An approach is also introduced to handle missing past IP geolocation databases. The results show that it is safe to retrospectively locate IP addresses by a couple of years, but there are differences between IPv4 and IPv6. The described parametric model of location lifetime allows us to estimate the time when the address location changed in the past. The retrospective geolocation of IP addresses has a broad range of applications, including social studies, system analyses, and security investigations. Two longitudinal use cases with the applied results are discussed. The first deals with geotargeted online content. The second deals with identity theft prevention in e-commerce.

A Comparative Analysis of Two Major Approaches for Mapping the Wildland-Urban Interface: A Case Study in California

The Wildland Urban Interface (WUI) is where human settlements border or intermingle with undeveloped land, often with multiple detrimental consequences. Therefore, mapping the WUI is required in order to identify areas-at-risk. There are two main WUI mapping methods, the point-based approach and the zonal approach. Both differ in data requirements and may produce considerably different maps, yet they were never compared before. My objective was to systematically compare the point-based and the zonal-based WUI maps of California, and to test the efficacy of a new database of building locations in the context of WUI mapping. I assessed the spatial accuracy of the building database, and then compared the spatial patterns of WUI maps by estimating the effect of multiple ancillary variables on the amount of agreement between maps. I found that the building database is highly accurate and is suitable for WUI mapping. The point-based approach estimated a consistently larger WUI area across California compared to the zonal approach. The spatial correspondence between maps was low-to-moderate, and was significantly affected by building numbers and by their spatial arrangement. The discrepancy between WUI maps suggests that they are not directly comparable within and across landscapes, and that each WUI map should serve a distinct practical purpose.

Validation of MODIS and AVHRR Fire Detections in Australia

The Sentinel Bushfire Monitoring System is an internet-based mapping tool which provides timely spatial information to fire agencies across Australia. The mapping system allows users to identify active fire locations that pose a potential risk to communities and property. Sentinel at Geoscience Australia currently provides hotspot information derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors on a continent-wide and daily basis enabling the fire community and general public to locate active fires. There has been little validation undertaken of the Sentinel since the system began operating in November 2002. Validation datasets have been collected for this work during the 2003-2007 fire seasons. Five study areas were selected to validate the detection capabilities of the MODIS and AVHRR hotspot product with fire activity that was mapped using high resolution earth observation imagery. The objective is to evaluate the reliability with which hotspots identified in MODIS and AVHRR thermal data can be used to identify fires. This consists of comparing the accuracy of AVHRR versus MODIS and quantifying the accuracy of both products. This objective is achieved by characterising errors through a stratified random sampling technique establishing a relationship between the ‘fire’ and ‘no fire’ condition, and error assessment using multi- source reference datasets over coincident MODIS and AVHRR pixels. The validation framework comprised two key approaches including validation of AVHRR hotspots in relation to MODIS hotspots and validation of both MODIS and AVHRR hotspots using multi-sensor earth observation imagery datasets. The study identified sources of errors associated with the Sentinel hotspots which could be used to improve the performance of hotspot algorithms and provide user-friendly information for the users. Statistical analysis revealed that overall commission errors of MODIS and AVHRR hotspots over the 5% sample data were 15% and 68% respectively, and overall omission errors of MODIS and AVHRR hotspots were 17% and 23% respectively. An important outcome of this study is the production of a database of fire locations derived from high-resolution imagery, which can serve as a resource for future validation efforts as detection algorithms evolve and sensors change.

Validation of MODIS and AVHRR Fire Detections in Australia

The Sentinel Bushfire Monitoring System is an internet-based mapping tool which provides timely spatial information to fire agencies across Australia. The mapping system allows users to identify active fire locations that pose a potential risk to communities and property. Sentinel at Geoscience Australia currently provides hotspot information derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors on a continent-wide and daily basis enabling the fire community and general public to locate active fires. There has been little validation undertaken of the Sentinel since the system began operating in November 2002. Validation datasets have been collected for this work during the 2003-2007 fire seasons. Five study areas were selected to validate the detection capabilities of the MODIS and AVHRR hotspot product with fire activity that was mapped using high resolution earth observation imagery. The objective is to evaluate the reliability with which hotspots identified in MODIS and AVHRR thermal data can be used to identify fires. This consists of comparing the accuracy of AVHRR versus MODIS and quantifying the accuracy of both products. This objective is achieved by characterising errors through a stratified random sampling technique establishing a relationship between the ‘fire’ and ‘no fire’ condition, and error assessment using multi- source reference datasets over coincident MODIS and AVHRR pixels. The validation framework comprised two key approaches including validation of AVHRR hotspots in relation to MODIS hotspots and validation of both MODIS and AVHRR hotspots using multi-sensor earth observation imagery datasets. The study identified sources of errors associated with the Sentinel hotspots which could be used to improve the performance of hotspot algorithms and provide user-friendly information for the users. Statistical analysis revealed that overall commission errors of MODIS and AVHRR hotspots over the 5% sample data were 15% and 68% respectively, and overall omission errors of MODIS and AVHRR hotspots were 17% and 23% respectively. An important outcome of this study is the production of a database of fire locations derived from high-resolution imagery, which can serve as a resource for future validation efforts as detection algorithms evolve and sensors change.

Design for the distributed data locator service for multi-site data repositories

The Remote Experimentation Centre (REC) in Japan has been preparing to replicate the full dataset of ITER over 10 000 km distance. In such a multi-site data repository environment, the data location informing service will be essential to find and retrieve the data efficiently. Considering the long latency time and the self sustainability of remote sites, the data location database should be running at each repository site. Multi-master asynchronous replication between cooperating databases will be essential to realize the remote experimental collaborations in fusion research. This study has investigated the functional differences of some relational databases and found that Postgres BDR has the expected database replication capabilities. Bi-directional replication (BDR) tests by using the LHD database and SNET revealed that the throughputs are sufficient for remote collaborations in fusion experiments.

Land-use impacts of Brazilian wind power expansion

While wind power is a low-carbon renewable energy technology with relatively little land footprint, the necessary infrastructure expansion still has land-related environmental impacts. Brazil has seen more than a ten-fold increase in wind power capacity in the last decade. However, little is known about these impacts of wind power generation in Brazil compared to other world regions, although Brazilian wind power infrastructure is concentrated in the least protected ecosystems that are prone to degradation, desertification and species extinction. This study focuses on land-use impacts of past wind power generation development in four Brazilian federal states, covering 80% of the country’s installed capacity. We assessed their spatial installation patterns, associated land-use and land cover change in the period before installation until 2018, and potential alternative installation locations, using a detailed wind turbine location database in combination with a high-resolution land-use and land cover map. In contrast to wind parks built in Europe, we found that 62% of the studied wind park area was covered by native vegetation and coastal sands. Overall, 3.2% of the total wind cluster area was converted from native vegetation to anthropogenic use. Wind parks installed mainly on native vegetation, on average, underwent higher land-use change compared to other wind parks. As Brazil intends to more than double its current wind power capacities by 2029, we explored possibilities to reduce environmental risks due to wind power expansion. We showed that this is feasible by integrating wind parks into human-altered areas, as sufficient wind resources there are available.

Travel Trajectory Frequent Pattern Mining Based on Differential Privacy Protection

Now, many application services based on location data have brought a lot of convenience to people’s daily life. However, publishing location data may divulge individual sensitive information. Because the location records about location data may be discrete in the database, some existing privacy protection schemes are difficult to protect location data in data mining. In this paper, we propose a travel trajectory data record privacy protection scheme (TMDP) based on differential privacy mechanism, which employs the structure of a trajectory graph model on location database and frequent subgraph mining based on weighted graph. Time series is introduced into the location data; the weighted trajectory model is designed to obtain the travel trajectory graph database. We upgrade the mining of location data to the mining of frequent trajectory graphs, which can discover the relationship of location data from the database and protect location data mined. In particular, to improve the identification efficiency of frequent trajectory graphs, we design a weighted trajectory graph support calculation algorithm based on canonical code and subgraph structure. Moreover, to improve the data utility under the premise of protecting user privacy, we propose double processes of adding noises to the subgraph mining process by the Laplace mechanism and selecting final data by the exponential mechanism. Through formal privacy analysis, we prove that our TMDP framework satisfies ε‐differential privacy. Compared with the other schemes, the experiments show that the data availability of the proposed scheme is higher and the privacy protection of the scheme is effective.

Enhanced Fingerprinting Based Indoor Positioning Using Machine Learning

Due to the inability of the Global Positioning System (GPS) signals to penetrate through surfaces like roofs, walls, and other objects in indoor environments, numerous alternative methods for user positioning have been presented. Amongst those, the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems (IPS) as the need for line-of-sight measurements is minimal, and it achieves better efficiency in even complex indoor environments. Offline and online are the two phases of the fingerprinting method. Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern. Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase. Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue. Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization, creating precise models to predict an indoor location. Large training sets are a key for obtaining better results in machine learning problems. Therefore, an existing WLAN fingerprinting-based multi-story building location database has been used with 21049 samples including 19938 training and 1111 testing samples. The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms (kNN, WkNN, FSkNN, and SVM) for estimating the location. The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84% as compared to all variants of the proposed method for 108703 m2 area.

Subcellular Proteomics as a Unified Approach of Experimental Localizations and Computed Prediction Data for Arabidopsis and Crop Plants.

In eukaryotic organisms, subcellular protein location is critical in defining protein function and understanding sub-functionalization of gene families. Some proteins have defined locations, whereas others have low specificity targeting and complex accumulation patterns. There is no single approach that can be considered entirely adequate for defining the in vivo location of all proteins. By combining evidence from different approaches, the strengths and weaknesses of different technologies can be estimated, and a location consensus can be built. The Subcellular Location of Proteins in Arabidopsis database ( http://suba.live/ ) combines experimental data sets that have been reported in the literature and is analyzing these data to provide useful tools for biologists to interpret their own data. Foremost among these tools is a consensus classifier (SUBAcon) that computes a proposed location for all proteins based on balancing the experimental evidence and predictions. Further tools analyze sets of proteins to define the abundance of cellular structures. Extending these types of resources to plant crop species has been complex due to polyploidy, gene family expansion and contraction, and the movement of pathways and processes within cells across the plant kingdom. The Crop Proteins of Annotated Location database ( http://crop-pal.org/ ) has developed a range of subcellular location resources including a species-specific voting consensus for 12 plant crop species that offers collated evidence and filters for current crop proteomes akin to SUBA. Comprehensive cross-species comparison of these data shows that the sub-cellular proteomes (subcellulomes) depend only to some degree on phylogenetic relationship and are more conserved in major biosynthesis than in metabolic pathways. Together SUBA and cropPAL created reference subcellulomes for plants as well as species-specific subcellulomes for cross-species data mining. These data collections are increasingly used by the research community to provide a subcellular protein location layer, inform models of compartmented cell function and protein-protein interaction network, guide future molecular crop breeding strategies, or simply answer a specific question-where is my protein of interest inside the cell?