Spatial Data Sources Research Articles

Spatializing inequality across residential built-up types: A relational geography of urban density in São Paulo, Brazil.

Overcrowding in informal housing is a core problem in international development debates, whereby slum upgrading is viewed as a dominant policy to integrate formal and informal areas. Conceptually, challenges to socio-spatial integration are associated with unbalanced development processes of urban de- and re-densification beyond the housing level. However, a lack of empirical understanding of these processes limits the case for redistributive land use policies to complement slum upgrading. To address this gap, our study adopts an exploratory approach, applying GIS-based techniques to population census and open data on land use in São Paulo, Brazil, in order to analyze the distribution of population densities across residential built-up types in informal and informal areas, although there are constrains related to small-scale spatial data sources on urban density. This shows that informal settlements are indeed the densest residential built-up type in the city while revealing the underlying spatial inequality between informal settlements and low-rise, high-standard residential areas. We suggest that more emphasis be placed on the design and implementation of redistributive policies to avoid spatialized forms of inequality associated with uneven urban development. This will ensure the spatial and social integration of urban areas.

European Land Use Spatial Data Sources and Their Role in Integrated Planning: Opportunities and Challenges for Poland

One of the 34 themes of the spatial datasets of Directive 2007/2/EC INSPIRE is ‘land use’, rightly described independently of ‘land cover’. Laws in most countries, apart from the Netherlands, do not consider the electronic form of plans as a legally binding document. As far as the elaboration step and the adoption step are concerned, the main land use requirement is related to the datasets that describe existing land use at present and in the past. Surveys and case studies concern Poland and were carried out in two stages, I in 2011–2013 and II in 2017–2019. Previous research on this subject concerned ‘planned land use’, especially attempts to standardize the classification of sub-local zoning plans and omit the creation of a metadata profile for existing land use. The main goal of the qualitative research is to assess the completeness of the available spatial datasets of existing and planned land use, conditioned by the needs of users. The author recommends the establishment in Poland of a new type of regulatory ‘Land Use Plan’ for the area of an entire municipality. As a summary, the author’s model of ‘The spatial planning system in the integrated development system of Poland’ was presented.

Pre‐dam valley reconstruction based on archival spatial data sources: Methods, accuracy, and 3D printing possibilities

AbstractArchival spatial data sources (maps and aerial photographs) allow reconstruction and 3D visualization of landscapes that have been altered by human activity. This article is dedicated to the reconstruction of the 300 km‐long pre‐dam valley of the Vltava River (Czech Republic, Europe) inundated by nine reservoirs built between 1930 and 1992. We used methods based on archival aerial photographs and old maps to reconstruct pre‐dam georelief, which is exceptional because the total area subject to the reconstruction is 1,670 km2. We found that old maps are preferred for terrain reconstruction. The map series “State Map Derived 1:5,000” released between 1950 and 1959 was chosen as the most suitable data source for the pre‐dam valley reconstruction because this map series covers the entire area of interest with elevation information. Based on the processed maps, more than 26,000 km of contour lines were derived by semi‐automatic vectorization. The resulting pre‐dam digital elevation model (DEM) was created using a combination of interpolation techniques. We demonstrated that the methods used for georelief reconstruction under standard conditions should be used carefully while working with such large areas. Geostatistical analysis was performed to verify the accuracy of the resulting DEM. The differences between the model and LiDAR surveyed data were analyzed from three points of view: spatial autocorrelation, normality, and elevation stability. We tested several 3D printing methods for producing a realistic 3D model with a texture applied. The computer numerical control (CNC) milling method was chosen to produce 3D models of the three reservoirs at scales of 1:8,000 and 1:6,000.

A Comparative Estimation of Maize Leaf Water Content Using Machine Learning Techniques and Unmanned Aerial Vehicle (UAV)-Based Proximal and Remotely Sensed Data

Determining maize water content variability is necessary for crop monitoring and in developing early warning systems to optimise agricultural production in smallholder farms. However, spatially explicit information on maize water content, particularly in Southern Africa, remains elementary due to the shortage of efficient and affordable primary sources of suitable spatial data at a local scale. Unmanned Aerial Vehicles (UAVs), equipped with light-weight multispectral sensors, provide spatially explicit, near-real-time information for determining the maize crop water status at farm scale. Therefore, this study evaluated the utility of UAV-derived multispectral imagery and machine learning techniques in estimating maize leaf water indicators: equivalent water thickness (EWT), fuel moisture content (FMC), and specific leaf area (SLA). The results illustrated that both NIR and red-edge derived spectral variables were critical in characterising the maize water indicators on smallholder farms. Furthermore, the best models for estimating EWT, FMC, and SLA were derived from the random forest regression (RFR) algorithm with an rRMSE of 3.13%, 1%, and 3.48%, respectively. Additionally, EWT and FMC yielded the highest predictive performance and were the most optimal indicators of maize leaf water content. The findings are critical towards developing a robust and spatially explicit monitoring framework of maize water status and serve as a proxy of crop health and the overall productivity of smallholder maize farms.

Evaluation of CMPAS precipitation products over Sichuan, China

High-quality and high-resolution precipitation data are the basis for mesoscale numerical weather forecasting, model verification, and hydrological monitoring, which play an important role in meteorological and hydrological disaster prevention and mitigation. In this study, high-density rain gauge data are used to evaluate the fusion accuracy of the China Meteorological Administration Multisource Precipitation Analysis System (CMPAS), and four CMPAS products with different spatial and temporal resolution and different data sources are compared, to derive the applicability of CMPAS. Results show that all the CMPAS products show high accuracy in the Sichuan Basin, followed by Panxi Area and the western Sichuan Plateau. The errors of the four products all rise with the increase in precipitation. CMPAS overestimates precipitation in summer and autumn and underestimates it in spring and winter. Overall, the applicability of these fused data in the Sichuan Basin is quite good. Due to the lack of observations and the influence of the terrain and meteorological conditions, the evaluation of CMPAS in the plateau area needs further analysis.摘要高质量和高分辨率的降水产品在天气预报, 数值模式模拟和气象防灾减灾方面起着重要的作用. 本文利用四川地区高密度的地面降水传感器观测数据, 比较CMPAS四种不同时空尺度的降水实况分析产品, 评估CMPAS的融合准确性与在四川地区的适用性. 研究表明:四种CMPAS降水产品都在四川盆地内精度较高, 攀西地区和川西高原次之. 随着降水量的增加, 产品的误差也随之增加. CMPAS在夏秋两季对降水量高估, 在春冬两季对降水量低估. 总而言之, CMPAS在四川盆地内与观测数据的融合准确性较高. 由于高原地区气象观测站点数量不足且观测数据受复杂地形和气候条件影响较大, CMPAS在高原地区的适用性检验仍需进一步研究.

The process of modelling the elevation surface of a coastal area using the fusion of spatial data from different sensors

Information regarding the depth distribution in a specific aquatic area is not also crucial for the safety of navigation, but also for modelling environmental processes, such as the quick establishment of marine-land boundaries or assessments of flood risk areas. Using elevation data from different available sources can be very convenient for individuals who wish to conduct quick analysis or need to obtain data covering a large area without the need for data collection and surveys. This study proposes a method of combining spatial data from different sources during surface modelling of a coastal area. The spatial data used for elevation surface modelling included hydrographic and topographic data, which are often collected separately for various purposes. Data are saved in different formats with various resolutions and accuracies; thus, a uniform surface model that will allow for easy and accurate analysis is currently lacking. The main aim of this study was to create a model of the surface of a coastal area using input data from various sources with the highest possible accuracy. This paper presents the available spatial data sources for coastal areas, along with the data pre-processing process. Furthermore, spatial data fusion is introduced, along with the results. The entire process of creating the uniform surface model consisted of several steps that are described in detail and visualised. The obtained model was visualised using a three-dimensional map.

Food Insecurity in Delaware: A Triangulation of Spatial Data Sources.

Preventing Chronic Disease (PCD) is a peer-reviewed electronic journal established by the National Center for Chronic Disease Prevention and Health Promotion. PCD provides an open exchange of information and knowledge among researchers, practitioners, policy makers, and others who strive to improve the health of the public through chronic disease prevention.

Using auxiliary data to rationalize smartphone-based pre-harvest forest mensuration

Abstract Accurate mensuration of forest stands for pre-harvest planning will pose high costs if carried out by a professional forester as an on-site evaluation. The costs could be reduced if a person with limited mensuration expertise could collect the required data using a smartphone-based system such as TRESTIMA® Forest Inventory System. Without prior information, the field sample with sufficient number of measurement points over the whole stand should be selected, so that the entire variation will be covered. We present and test a rational framework based on selecting the sampling locations according to auxiliary data. As auxiliary variables, we use various spatial data sources indicating forests’ structural or spectral variation, as well as previously predicted inventory variables. We construct two variants of sampling schemes based on the local pivotal method, weighted by the auxiliary data, and compare the results to simple random sampling (SRS) with corresponding sample sizes. According to our findings, the benefits of auxiliary data depend on the considered stand, species and timber assortment. The use of auxiliary data leads generally to improved results and up to three times higher efficiency (i.e. lower variance) as compared with SRS. We conclude that the framework of applying auxiliary data has high capabilities in rationalizing the sampling efforts with little drawbacks, consequently providing potential to improve the results with similar sample size and possibility to use of non-specialists for the pre-harvest inventory.

The Development of Urban Quality of Life Spatial Model using Geographic Information System (GIS) in Kuala Terengganu, Malaysia

The urbanisation process cause changes to the physical and non-physical environment to developing a town and Urban Quality of Life (UQoL). Kuala Terengganu is experiencing rapid urbanisation process over the past three decades. The main objective of this study to build the spatial model based on Geographic Information System (GIS) in the determination of UQoL in Kuala Terengganu, Malaysia. A total of 115 sampling location have chosen based on the current status of research UQoL in the study area. The method used includes the main stages of data analysis and processing of data mean score for each domain scores were analysed using GIS. The mean social domain, physical domain and access domain have been analysed through the technique of auto Inverse Distance Weighting (IDW-GIS). The correlation and regression methods were used to examine the relationship of urbanisation of research location and UQoL. The results showed the current status UQoL for the physical domain is in Class 2 with 78.92%, social domain is in Class 2, 77.61% and in Class 2 domain access, 95.01%. Based on the findings of these domains, a synthetic UQoL space model was produced. Overall, the current status of UQoL in the study area in Class 3 with 63.92%. The resulting space model based on spatial data sources from the year 2009 (land-use) and 2013 (satellite images) show that the area is in the south of Kuala Terengganu have a quality of life is better because of the proximity of the city centre and good infrastructure.

How will innovation-driven development policy affect sustainable urban land use: Evidence from 230 Chinese cities

Government policy has a significant effect on rural–urban land conversion, ultimately affecting sustainable urban land use. Unlike most studies that have centred on the effects of local land-use policies, such as regulations and taxes, the present study investigates the effect of the innovation-driven development policy initialised recently by China on urban land growth at the national level. Although the geography of innovation, high-tech agglomeration and sectoral restructuring are place-based activities, existing evidence shows several links between urban innovation and urban land growth. However, only a few empirical studies have examined their relationships to date. Thus, we aim to determine the effect of urban innovation on the size and the spatial distribution of urban land using a statistical dataset from 2007 to 2015 and a spatial data source at two-time points for 230 Chinese cities. We employ three fixed effects models to control for the possible correlation between independent variables and unobservable city-level effects. Our results indicate that urban innovation has indeed driven urban land growth but does not make the urban landscape more scattered than before. Further examinations suggest that the urban innovation effect varies across the whole country, and the innovation-urban land growth connection relies on the geographic location rather than the economy size.

Remote sensing, GIS and AHP techniques based investigation of groundwater potential zones in the Karumeniyar river basin, Tamil Nadu, southern India

A primary objective is to analyzing the multiple parameters for mapping of groundwater potential zones in the Karumeniyar river basin located in Southern India using integrated remote sensing, GIS and geophysical techniques. In this analysis, multiple geo-environmental parameters such as geology, geomorphology, soil, drainage density, lineament density, landuse/land cover (LULC), slope, rainfall, topographic wetness index (TWI), surface roughness, topographic curvature, elevation (relief), and land surface temperature (LST) are analyzed for identifying groundwater potentiality at site-specific scale using GIS based analytical hierarchy process (AHP) technique. Each thematic layer is derived from corresponding spatial data sources through systematic procedures, and assigned to appropriate weights and ratings based on degree of magnitude to recharge and permeability. The result reveals that the 10.7% of total area exhibits higher potential of groundwater sources, whereas, the 26.8% area is noted as good potential of groundwater along the streams and paleochannels. Significantly, the 33.6% of area fall under moderately groundwater potential due to monsoonal recharge fluctuations. However, the 28.9% of area occurred as lower groundwater potential because of sub-surface settings and lack of recharge or permeability. The groundwater potential zones are cross-verified at selected locations based on insitu VES geophysical survey (Vertical Electrical Sounding) and groundwater yield data. Among the six locations, the VES results indicate the aquifers underlying unconfined and semi-confined formations are noted as very-good to good groundwater potential than other category of aquifers. Moreover, the higher groundwater yield is estimated in good and very-good zones that indicates higher potentiality of groundwater sources. This study provides the geo-database of primary information for planning and management of groundwater resources in the non-perennial river basin environment.

Spatial factors influencing building age prediction and implications for urban residential energy modelling

Urban energy consumption is expected to continuously increase alongside rapid urbanization. The building sector represents a key area for curbing the consumption trend and reducing energy-related emissions by adopting energy efficiency strategies. Building age acts as a proxy for building insulation properties and is an important parameter for energy models that facilitate decision making. The present study explores the potential of predicting residential building age at a large geographical scale from open spatial data sources in eight municipalities in the German federal state of North-Rhine Westphalia. The proposed framework combines building attributes with street and block metrics as classification features in a Random Forest model. Results show that the addition of urban fabric metrics improves the accuracy of building age prediction in specific training scenarios. Furthermore, the findings highlight the way in which the spatial disposition of training and test samples influences classification accuracy. Additionally, the paper investigates the impact of age misclassification on residential building heat demand estimation. The age classification model leads to reasonable errors in energy estimates, in various scenarios of training, which suggests that the proposed method is a promising addition to the urban energy modelling toolkit.

Characteristics of land use change Tupu in Shandong province based on multi-source data

In recent years, with the rapid pace of urbanization in China and people’s increasingly high requirements for living environment, human activities on land demand are increasing. Based on the theory and method of Geo-information Tupu, we synthesize the land use change Tupu of Shandong province during the period 2010-2018 by taking the remote sensing interpretation data as the spatial data source, so as to analyze the spatial and temporal pattern of land use change in Shandong province in recent years. The results show that: (1) the main types of land use change in Shandong province are the mutual transformation of cropland and construction land and the transformation of cropland and construction land to water area. (2) in terms of space, “ construction land→ water area” and“ cropland→ construction land” have the most concentrated distribution. The former is significantly distributed in the coastal areas of Binzhou, Dongying and Weifang. (3) the transfer of cropland into related land use change types, with larger area, smaller spatial separation and more concentrated distribution; The transfer area of woodland is small, and the spatial separation degree is large. (4) in general, the cropland area is decreasing while the construction land area is increasing.

Distribution and extent of benthic habitats in Puck Bay (Gulf of Gdańsk, southern Baltic Sea)

The majority of the southern Baltic Sea seabed encompasses homogenous soft-bottom sediments of limited productivity and low biological diversity, but shallow productive areas in the coastal zone such as wetlands, vegetated lagoons and sheltered bays show a high variety of benthic habitat types offering favourable biotopic conditions for benthic fauna. Within Polish marine areas, semi-enclosed Puck Bay (the western part of the Gulf of Gdańsk) features an exceptionally diverse environment covering a range of benthic habitats which underscores its unique biological value and aesthetic quality and providing an impetus for conservation and ecosystem-based development. Full-coverages maps on benthic habitats in this area are therefore a necessary foundation for maritime spatial planning and implementation of strategies for sustainable management and protection of the coastal environment. This study presents the first comprehensive description and distribution of benthic habitats in Puck Bay which were categorised using the revised EUNIS 2019 classification system. Typological analyses were carried out based on inventory datasets from 1995 to 2019 including scientific publications, satellite images, open databases, topographic and geological maps, reports, theses, information available on websites and unpublished data shared willingly by individual researchers and administrative institutions. Collating various spatial data sources, that were first georeferenced and then visualized using techniques available in ArcMap 10.4.1 software (Esri), resulted in the mapping of benthic habitats and sites of important and protected plant species, which can contribute to the high confidence in environmental assessments and monitoring activities.

Cropland Abandonment in Slovakia: Analysis and Comparison of Different Data Sources

This study compares different nationwide multi-temporal spatial data sources and analyzes the cropland area, cropland abandonment rates and transformation of cropland to other land cover/land use categories in Slovakia. Four multi-temporal land cover/land use data sources were used: The Historic Land Dynamics Assessment (HILDA), the Carpathian Historical Land Use Dataset (CHLUD), CORINE Land Cover (CLC) data and Landsat images classification. We hypothesized that because of the different spatial, temporal and thematic resolution of the datasets, there would be differences in the resulting cropland abandonment rates. We validated the datasets, compared the differences, interpreted the results and combined the information from the different datasets to form an overall picture of long-term cropland abandonment in Slovakia. The cropland area increased until the Second World War, but then decreased after transition to the communist regime and sharply declined following the 1989 transition to an open market economy. A total of 49% of cropland area has been transformed to grassland, 34% to forest and 15% to urban areas. The Historical Carpathian dataset is the more reliable long-term dataset, and it records 19.65 km2/year average cropland abandonment for 1836–1937, 154.44 km2/year for 1938–1955 and 140.21 km2/year for 1956–2012. In comparison, the Landsat, as a recent data source, records 142.02 km2/year abandonment for 1985–2000 and 89.42 km2/year for 2000–2010. These rates, however, would be higher if the dataset contained urbanisation data and more precise information on afforestation. The CORINE Land Cover reflects changes larger than 5 ha, and therefore the reported cropland abandonment rates are lower.

Sources and sinks of greenhouse gases in the landscape: Approach for spatially explicit estimates

Climate change mitigation is a global response that requires actions at the local level. Quantifying local sources and sinks of greenhouse gases (GHG) facilitate evaluating mitigation options. We present an approach to collate spatially explicit estimated fluxes of GHGs (carbon dioxide, methane and nitrous oxide) for main land use sectors in the landscape, to aggregate, and to calculate the net emissions of an entire region. Our procedure was developed and tested in a large river basin in Finland, providing information from intensively studied eLTER research sites. To evaluate the full GHG balance, fluxes from natural ecosystems (lakes, rivers, and undrained mires) were included together with fluxes from anthropogenic activities, agriculture and forestry. We quantified the fluxes based on calculations with an anthropogenic emissions model (FRES) and a forest growth and carbon balance model (PREBAS), as well as on emission coefficients from the literature regarding emissions from lakes, rivers, undrained mires, peat extraction sites and cropland. Spatial data sources included CORINE land use data, soil map, lake and river shorelines, national forest inventory data, and statistical data on anthropogenic activities. Emission uncertainties were evaluated with Monte Carlo simulations. Artificial surfaces were the most emission intensive land-cover class. Lakes and rivers were about as emission intensive as arable land. Forests were the dominant land cover in the region (66%), and the C sink of the forests decreased the total emissions of the region by 72%. The region's net emissions amounted to 4.37 ± 1.43 Tg CO2-eq yr−1, corresponding to a net emission intensity 0.16 Gg CO2-eq km−2 yr−1, and estimated per capita net emissions of 5.6 Mg CO2-eq yr−1. Our landscape approach opens opportunities to examine the sensitivities of important GHG fluxes to changes in land use and climate, management actions, and mitigation of anthropogenic emissions.

ApeX-Vigne: experiences in monitoring vine water status from within-field to regional scales using crowdsourcing data from a free mobile phone application

Monitoring vine water status is a major issue for vineyard management because water constraints impact both the quality and the quantity of the harvest. Existing methods are often costly and complex to implement. ApeX-Vigne is a free mobile application developed to facilitate the collection and geolocation of 50 vine apex observations to characterise vine shoot growth and classify it into 3 growth categories. The application also provides the user with a simple estimate of vine water status based on shoot growth. This paper presents the results obtained over two seasons (2019 and 2020) after the launch of the Apex-Vigne application and its use over a large wine producing region in the south of France. An existing method was adapted for evaluating the interest of the application based on the number of installations and uninstallations. The results showed that the application had more than 1200 downloads and 6000 observations made in the 2020 season. Examples from the commercially collected data showed that ApeX-Vigne can be used as a tool for characterizing water stress at within-field and inter-field scales. Finally, it was also demonstrated that by enabling the massive and centralized collection of spatial field and within-field scale observations of shoot growth, the ApeX-Vigne data was able to characterise the spatial structure of vine water status at the regional scale. Access to this new source of information offers opportunities for the management of water resources at a regional scale as well as for site- and vineyard-specific management. These results also raised new research questions on the joint use of this new source of spatial data with other sources of high spatial resolution information.

Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping

Crowdsourcing is one of the spatial data sources, but due to its unstructured form, the quality of noisy crowd judgments is a challenge. In this study, we address the problem of detecting and removing crowdsourced data bias as a prerequisite for better-quality open-data output. This study aims to find the most robust data quality assurance system (QAs). To achieve this goal, we design logic-based QAs variants and test them on the air quality crowdsourcing database. By extending the paradigm of urban air pollution monitoring from particulate matter concentration levels to air-quality-related health symptom load, the study also builds a new perspective for citizen science (CS) air quality monitoring. The method includes the geospatial web (GeoWeb) platform as well as a QAs based on conditional statements. A four-month crowdsourcing campaign resulted in 1823 outdoor reports, with a rejection rate of up to 28%, depending on the applied. The focus of this study was not on digital sensors’ validation but on eliminating logically inconsistent surveys and technologically incorrect objects. As the QAs effectiveness may depend on the location and society structure, that opens up new cross-border opportunities for replication of the research in other geographical conditions.

Floral hosts of leaf-cutter bees (Megachilidae) in a biodiversity hotspot revealed by pollen DNA metabarcoding of historic specimens.

South Africa is a megadiverse country with three globally recognised biodiversity hotspots within its borders. Bees in particular show high diversity and endemism in the western part of the country. Not much is currently known about the floral host preferences of indigenous bees in South Africa, with data only available from observational studies. Pollen metabarcoding provides provenance information by utilising DNA analyses instead of floral visitation and traditional microscopic identification to identify pollinator food plants, which can be time consuming and imprecise. In this study, we sampled pollen from leaf-cutter bees (Megachilidae) specimens maintained in a historic insect collection (National Collection of Insects, South Africa) that were originally collected from two florally important areas in South Africa (Succulent Karoo and Savanna) and used metabarcoding to determine pollen provenance. We also sampled pollen from leafcutter bee species with wider distributions, that extend across many different biomes, to determine if these 'generalist' species show relaxed floral host specificity in some biomes. Metabarcoding involved sequencing of the nuclear internal transcribed spacer 2 (ITS2) region. Amplicons were compared to a sequence reference database to assign taxonomic classifications to family level. Sequence reads were also clustered to OTUs based on 97% sequence similarity to estimate numbers of plant species visited. We found no significant difference in the mean number of plant taxa visited in the Succulent Karoo and Savanna regions, but the widespread group visited significantly more floral hosts. Bees from the widespread group were also characterised by a significantly different composition in pollen assemblage. The time since specimens were collected did not have an effect on the mean number of taxa visited by any of the bee species studied. This study highlights national history collections as valuable sources of temporal and spatial biodiversity data.