GIS Monthly Maps
Map of the Month October
Cartographer: David Meredith (PhD) and Jesko Zimmermann (PhD)
The data: The map uses data produced by the CSO from the 2016 Census of Population, associated with ‘workplace zones’. Whilst most Census of Population data and maps summarise the profile of the people living in a particular area, workplace zones report the profile of the jobs available in an area. This provides important insights into the relative geographic importance of different sectors of the economy. The CSO has identified 7,219 such zones. The basic building block of a zone is the Small Area which nests inside an electoral division. All zones to have at least 100 workers, three businesses or enterprises, and no more than 90% of employees in any one zone can work in a single organisation.
The map: The map shows the percentage of all workers in each workplace zone working in the Agriculture, Forestry and Fishing sector. In total, the CSO identified 88,536 persons, 4.5% of all people in employment, as working in this sector following the 2016 Census of population. The map clearly demonstrates the spatial variation in the relative level of employment in the sector. Some workplace zones, mainly associated with towns and cities, record very small percentages of their total number of jobs associated with this sector. In contrast, there are many workplace zones where one in five, or more, jobs are associated with this sector.
One of the obvious features of the map is the extensive areas where agricultural employment is particularly important (dark blue). It is also evident that road infrastructure plays an important role in shaping both the zones and the range of employment opportunities in them. Zones traversed by a major road generally, though not always, record a lower percentage of all jobs associated with the Agriculture, Forestry and Fishing sector. This highlights the dispersion of employment opportunities outside of the urban centres. Another interesting feature of the map is the relatively small percentages of workers employed in the sector in some remote western coastal areas. This points to the importance of other sectors of the economy in these zones, e.g. tourism.
Map of the Month September
Cartographer: Stuart Green
Map; TOPEX (PDF)
Topographic Exposure or TOPEX is a concept from forestry designed to help in planning forest locations where wind may be a problem. However measures of shelter and exposure to wind have lots of uses in land management from locating wind turbines to selecting sites free from frost for crops. TOPEX is usually a measurement made in the field as an index showing how exposed a site is. At the site the observer looks to the directions, north , south, east and west and measures the angle from the horizontal to the horizon.
A positive angle means the landscape in that direction is higher than the site and so offers some protection from wind, if the angle is negative, then the site is exposed in that direction. To calculate the total topographic shelter the four measures are summed and the lower the index is the more exposed the site is.
Here we have recreated these measurements across the country using a Digital Elevation Model and a piece of code to automatically make the TOPEX calculation every 100m. Blue areas are very sheltered whilst red areas are exposed (ridges in mountain landscapes show up very clearly in this map)
Map of the Month August
Cartographer: Jesko Zimmermann
One of the most important factors shaping agriculture, both current and historical, is the landscape in which it is happening. The term landscape describes the physical geography of a location. It includes, among other climate, the shape of the land, the hydrology, the soils as well as the living biology of a place. These factors themselves are interwoven in a complex network of interacting drivers that function both in space and time. Still, despite this complexity, the connection between landscape and farming can be observed even in its single components.
This month’s map showcases the interaction between elevation (and the shape of the land) and the stocking rate in Ireland. Elevation is a major aspect of landscape. It directly influences factors such as climate, hydrology and biology, but also human factors such as accessibility of land. Stocking rate is a good proxy for farming intensity, especially in Ireland were the majority of agricultural land is utilised for livestock farming. Therefore comparing these two factors provides a great visualisation of how agriculture and landscape interact.
The elevation map was created by NASA as part of the Shuttle Radar Topography Mission, which is now freely available. To better highlight the elevation, hillshades where calculated in ArcGIS Pro. The stocking density map was created by researchers from Teagasc and University College Cork using remote sensing data in conjunction with a spring growth model (see map for reference). The stocking rate is measured in Livestock Units (LSU) per hectare and should therefore not be equated to animal numbers as 1 LSU is equivalent to a dairy cow, or for example six sheep or goats.
Still map clearly shows the impact of elevation on stocking rates with uplands generally showing low stocking rates, while areas of high stoking rates are concentrated in the lowlands. The map however also highlights that a landscape single factor (i.e. elevation) is not sufficient to explain all differences in agriculture, as there are large lowland areas in the southeast, midlands and northwest still show low stocking rates. This can have multiple reasons such as a higher prevalence of arable farming, or overlap with urban fabric (such as the Greater Dublin Area).
Map of the Month July
Cartographer: Stuart Green
This month we show the River Shannon and the farm lands that are directly on the Shannon bank. We show this information in two ways, as a map and as a histogram that shows the totalaccumulated land area as we move south form the source of the Shannon to the mouth. There are 14,000 ha of farm land parcels on the banks of the Shannon (this is of course a very small percentage of the land in the shannon catchment).
Interestingly, whilst the length of the Shannon is quoted as 360km, this is the "as the crow flies" distance form the source to the mouth.The total length of the river banks is much higher, over four times longer at 1600km.
Map of the Month June
Cartographer: Jesko Zimmermann
Spatial objects such as countries, counties or cities are defined by their boundary. These boundaries, however, are not always static. They may depend on the administrative body defining the spatial object, or they may change over time. Taking such dynamics into account is crucial for the interpretation of any results derived from spatial data.
To highlight these differences, this month’s map looks at three land cover/land use classes in the city of Cork, (1) agricultural land, (2) other green space, and (3) built environment. Understanding land use/land cover is an important part of planning decisions, but also necessary for the understanding of landscape. The question of share of each of these classes on the total area of the city of Cork is strongly dependant on the definition of the city. Cork City is an especially interesting example as it showcases the effects of both different definitions, and temporal change.
The three definitions for urban area used for this month’s map are (a) the old Cork City Council boundary, which is the historic boundary of Cork City, and is also used in the CSO 2016 census to define Cork City; (b) the settlement area of Cork which includes the suburbs and is considered part of urban Cork in the CSO 2016 census; and (c) the new Cork City Council boundary in effect since the 31st May 2019.
The map clearly shows the effects of the definition in the share of area of each land cover/land use class in the urban area of Cork City, with the old Cork City Council area showing a majority built space, about a third green space, and very little agricultural area. The settlement area, defined by the CSO, shows a slight majority of agricultural area, followed by built space, and green space. Finally, the new Cork City Council boundary is mainly agricultural land, accounting for almost half of the area, while built space and other green space cover the remaining area in roughly equal shares.
In this case, a change in demarcation of the outline of Cork City leads to very different picture, with the old City Council outline showing a dominance of built environment, while at the same time ignoring substantial parts of the continuous urban fabric to the south. The other definitions show a much greener picture of Cork City.
Two datasets where used to derive the land cover/land use classes. The Ordnance Survey of Ireland Prime2 spatial reference dataset was used to determine built environment and non-agricultural green spaces, and the Land Parcel Identification System for 2016 was used to identify agriculturally used areas.
Map of the Month May
Cartographers: Jesko Zimmermann & David Meredith
Social isolation is an important issue in rural Ireland, and has been shown to have detrimental effects on both physical and mental health. A range of factors, including individual and socio-economic group characteristics and spatial dimensions influence whether a person may suffer from isolation.
Rural isolation is not equally distributed across Ireland, but is subject to geographic factors. To showcase the risk of rural isolation amongst farm households in Ireland, we have used the share of single person household farms of all farm households in a set of predefined hexagons as a proxy for isolation. The data was derived from the Central Statistics Office POWSCAR 2011 census of anonymised records.
The map has two layers: the colour depicts the share of single household farms, and the height of the hexagons represents the total number of farms in the area calculated using the GeoDirectory. These two datasets do not directly correlate. This form of visualisation ensures that small groups of households cannot be identified, which is a prerequisite of using the POWSCAR data.
The results show some clear geographic patterns with the majority of areas with high shares of single household farms occurring in the west of Ireland. In particular, Counties Donegal, Leitrim, Sligo, Galway and Kerry, but also County Wicklow in the east, show high shares of single household farms. The map also shows that this form of isolation is not limited to areas with a small number of households either; in fact there is no significant relationship between the number of households and the share of single person households.
Generally the geographic patters correspond well with areas that are regarded as remote and poorly accessible, such are mountain ranges, and islands. It is important to note that while geography is a small but important aspect of isolation, other factors are also important, e.g. quality of rural transportation services, the cohesion of the community and individual characteristics.
Map of the Month April
Cartographer: Stuart Green
The General Soil Map, published in 1980, is national resource based on soil survey data from AFT. Even though the map itself has low resolution (you can't use it to identify soils on your own farm for example) it has a huge amount of information in the accompanying bulletin.
The map itself is an association map - each area is a collection of soil series that generally co-occur. Within the bulletin you can find information on the properties of the soils series and the associations and also an assessment of the principal use of each soil in farming. For example Association 13 (an acid brown earth) has:
“a wide use range and are very suitable for both tillage and grass production. Because of their sandy loam texture, free drainage and good structure, they are easy to cultivate and can produce a wide range of crops, including malting barley and sugar beet. The climatic advantages of their southern location increase both crop and pasture yields. They also have a high reputation for apple production where they have been devoted to orchards, as in south Kilkenny and Waterford.”
Whereas Association 18 (a podzol):
“are generally not suited to tillage. They are moderately suitable for grassland, but, because of the weak structure and high organic matter content of the surface horizon, they require careful management, even in pasture”
Here as, part of the EPA funded SOLUM project, we've classified the soil map based on the published assessment of the grass growing potential of each soil association. Bear in mind these judgements were made 40 years ago and grassland and soil management technology and methods have progressed significantly and each association, as a range of soil series, can have a wide range of grass use potential (not to mention other land use options such as tillage).
Map of the Month March
Cartographer: Stuart Green
This month’s map isn’t really a map, it’s a cartogram. A cartogram is another way of showing information about places. In a cartogram we start off with a conventional map, in this case a map of the counties of Ireland and then we re-shape each county (or element) according to the relative size of the statistic we are looking at; if a county is at the high end of the statistical range it gets inflated and if it’s a low range it gets deflated and the method then adjusts the shape so that relative positions are kept (so counties that are adjacent remain adjacent).
So we have created two cartograms in this month’s map to show the relative importance of agriculture and public administration (a part of the civil service) as an employment sector for each county’s working population. We chose to compare full time agriculture employment and full time public administration as the total numbers in the republic are roughly similar. If a county is bigger than normal in the cartogram, then the importance the employment sector is large, compared to the national average, and is shrunk then the opposite is true.
Map of the Month February
Cartographer: Kazeem Abiodun Ishola
The availability of water is an important component for assessing the impact of climate change on agriculture. In 2018, the two extreme weather events resulted in poor cop performance and yield, and consequently impacted farm incomes in Ireland. However, quantitative knowledge of magnitudes and
Vegetation indices and temperature serve as indirect parameters that can be used to derive the Water Deficit Index (WDI), which serves as an indicator for crop water status. In this case a remote sensing approach was used to calculate the index for the Republic of Ireland. Monthly NASA MODIS surface temperature (MOD11B3), enhanced vegetation index (MOD13A3) and surface albedo (MCD19A3) were obtained, and resampled to 1km cell size. The data span from December 2017 to November 2018. At pixel level WDI was derived by dividing the difference between the surface temperature and minimum surface temperature at cold pixels, by the difference between maximum surface temperature at hot pixels and minimum surface temperature for each month.
The WDI ranged from 0 to 1, indicating no water stress to severe crop water stress, respectively. The early period of winter shows moderate to severe water deficits in the west and south-west (WDI > 0.5) and no water stress in the north. In late winter, the entire country severe water deficits which prolonged till the end of spring. This was the result of the prolonged cold and wet conditions, where actual evapotranspiration greatly exceeded the potential evapotranspiration, and consequently leading to poor crop performance. We further observe peak WDI spreading from the east and south of Ireland northwards in summer and autumn as a result of the prolongued drought period.
Map of the Month January
Cartographers: Dr Jesko Zimmermann
The first map of 2019 illustrates the more pleasant side of 2018s weather.
Optical sensors mounted on satellites do not have the capacity to penetrate cloud cover. Especially in Ireland this can cause issues for people working in remote sensing as much of the recorded imagery will be partially or fully obstructed by clouds.
As 2018 was a year of quite extreme weather, we wondered if the particularly warm summer led to a less cloudy year, or if this effect was negated by the wet spring.
In order to help users, many providers of satellite imagery supplement their products with a cloud mask which help identify cloudy pixels. In the NASA MODIS surface reflectance product (MOD9AGA), this mask is provided on a 1 km2 cell grid. In this map we use the cloud mask of this product, which is available on an almost daily basis and generally covers the whole of the island of Ireland, as an indicator for cloudy days. To assess the cloudiness of 2018, we took the sum of cloudy days for each pixel in 2018 and compared it to the average number of cloudy days per year in the decade prior (2008 to 2017).
The map clearly illustrates that Ireland was for most parts slightly less cloudy in 2018, with some notable exceptions in the midlands and on the Ards Peninsula. The spot with lowest relative cloud cover (36 cloudy days less than average) was recorded on the Iveragh Peninsula close to Killorglin; other particularly sunny spots were on the Cork/Limerick Border and south of Wicklow town.
About Map of the Month
In addition to undertaking geographical analyses and producing maps for research projects, the spatial analysis lab responds to ad hoc requests for contributions. The latter may be for in-house purposes or to inform policy submissions. While dissemination is a key objective of research projects, maps produced in response to such requests rarely get a wider audience. We’ve decided that we’ll take the most interesting map we have produced in each month and to present it here to hopefully find a wider audience and promote discussion and debate on both the contribution of spatial analysis to Irish agriculture and food and on the specific maps produced.
Whilst this map can be shared please check with us before reproducing it in a publication. Many of the data sets we use are under licence with conditions attached.
For general enquiries contact Stuart Green or the author above for information on this month’s map.