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This study proposes new family-centered measures of access to early care and education (ECE) services with respect to quantity, cost, and quality and uses them to assess disparities in access across locations and socio-demographic groups in Minnesota. These measures are distance-based and use available geographic data to account for the fact that families can cross arbitrary administrative boundaries, such as census tract or ZIP code lines, and thus better reflect the real experiences of families than conventional area-based measures. Combining synthetic family locations simulated from Census demographic and geographic data and information on ECE provider locations, we calculate travel time between the locations of families with young children and ECE providers to measure families’ access to providers of different types. The results yield a map of areas with low and high relative ECE access. The average family in Minnesota lives in a location where there are nearly two children for every nearby slot of licensed capacity, however, access to ECE supply varies considerably at the local level. The supply measure can also serve as a weight useful in computing family-centered measures of ECE quality and access costs, incorporating both prices and travel costs, to further characterize the local ECE market from the perspective of families. Improving the measures of variation in families’ access to ECE quantity, cost, and quality is valuable as policymakers consider expansions to public supports for early learning and ECE entrepreneurs decide where to invest.

Past years have witnessed the rapid use of participatory mapping and the establishment of open-source mapping infrastructures. The collection of user-generated geographic content has proven to provide highly effective means of acquiring useful and detailed global environmental data. OpenStreetMap is perhaps the best-established and reliable example that makes use of crowdsourced maps, constantly and rapidly evolving. Since data is heterogeneous in nature, the position quality of the objects edited and mapped into OpenStreetMap varies. The current OpenStreetMap practice is that the last node location edit of a map-feature is presented in the map, possibly inferring that it is the most accurate representation. Accordingly, OpenStreetMap presents the recent version of the accumulated edited and mapped data, disregarding the earlier versions. This chapter investigates the positional quality of historical node versions, evaluating the current OpenStreetMap practice alongside alternative spatiotemporal data models for improved node location calculation. With the proper incremental use of the historical node versions, the more accurate location for close to 60% of all the analyzed nodes was automatically selected between the last version location and the location calculated by the best model. This statistic increases to 76% for nodes that show larger discrepancies when compared to the reference locations. These preliminary results challenge the current OpenStreetMap practice by suggesting an alternative data model that use historical versions. The outcome validates and extends the existing patchwork approach associated with the crowdsourcing of user-generated geographic data and information that are the basis of OpenStreetMap.

In this article, the authors present Lithuanian spatial information portal, its services and the availability to use in education process. Lithuanian spatial information portal created in 2008 year developing the project "Lithuanian infrastructure for geographical information, which was financed by the Republic of Lithuania and the European Union. The project is performed by the National Land Service under the Ministry of Agriculture and the State Enterprise Remote Sensing and Geoinformatics "GIS-Center". This article provides information about the LEI portal where you can find methodical and educational material, maps, spatial data sets, documents, help texts, geographic information terms, so teachers and students can use real data and create maps, or properly use geoportal.lt provided tools to deal with tasks.

Until DGI became widely used, commanders and staff got information about the area of activity first of all from paper models of landscape, i.e. from maps. International Cartographic Association (ICA) defines a map as (ICA in International Cartographic Association. Retrieved July 2014, from International Cartographic Association: http://icaci.org/mission/, 2014): “A map is a symbolized representation of geographic reality, representing selected features or characteristics, resulting from the creative effort of its author’s execution of choices, and is designed for use when spatial relationships are of primary relevance.” The efficiency of decision-making processes using maps has always been significantly influenced by their quality. Quality prepared and up-to-date maps enable to get detailed information about the area of activity that is necessary for making optimal decisions. For the needs of decision-making processes, there are maps published that correspond to the given needs, e.g. units of integrated rescue systems or armed forces, and they are usually produced by state organizations that guarantee its constant quality and regular updating.

UAVs are a disruptive technology bringing new geographic data and information to many application domains. UASs are similar to other geographic imagery systems so existing frameworks are applicable. But the diversity of UAVs as platforms along with the diversity of available sensors are presenting challenges in the processing and creation of geospatial products. Efficient processing and dissemination of the data is achieved using software and systems that implement open standards. The challenges identified point to the need for use of existing standards and extending standards. Results from the use of the OGC Sensor Web Enablement set of standards are presented. Next steps in the progress of UAVs and UASs may follow the path of open data, open source and open standards.

A protocol for the collection of vector data in Volunteered Geographic Information (VGI) projects is proposed. VGI is a source of crowdsourced geographic data and information which is comparable, and in some cases better, than equivalent data from National Mapping Agencies (NMAs) and Commercial Surveying Companies (CSC). However, there are many differences in how NMAs and CSC collect, analyse, manage and distribute geographic information to that of VGI projects. NMAs and CSC make use of robust and standardised data collection protocols whilst VGI projects often provide guidelines rather than rigorous data collection specifications. The proposed protocol addresses formalising the collection and creation of vector data in VGI projects in three principal ways: by manual vectorisation; field survey; and reuse of existing data sources. This protocol is intended to be generic rather than being linked to any specific VGI project. We believe that this is the first protocol for VGI vector data collection that has been formally described in the literature. Consequently, this paper shall serve as a starting point for on-going development and refinement of the protocol.

In 1995, the concept of 'naive geography' was formally introduced as an area of cognitive geographic information science representing 'the body of knowledge that people have about the surrounding geographic world' and reflecting 'the way people think and reason about geographic space and time, both consciously and subconsciously'. The need to incorporate such commonsense knowledge and reasoning into design of geospatial technologies was identified but faced challenges in formalizing these relationships and processes in software implementation. Ten years later, the Google Earth geobrowser was released, marking the beginning of a new era of open access to, and application of, geographic data and information in society. Fast-forward to today, and the opportunity presents itself to take stock of twenty years of naive geography and a decade of the ubiquitous virtual globe. This paper introduces an ongoing research effort to explore the integration of naive (or commonsense) geography concepts in the Google Earth geobrowser virtual globe and their possible impact on Google Earth's usability, utility, and usefulness. A multi-phase methodology is described, combining usability reviews and usability testing with use-case scenarios involving the U.S.-Canadian Yellowstone to Yukon Initiative. Initial progress on a usability review combining cognitive walkthroughs and heuristics evaluation is presented.

At the present time, spatial reference system is a bottleneck of geographic data and information sharing system. In this paper, a common spatial reference system sharing framework model for coordinate transformation WPS and geographic information sharing is designed and constructed based on the service-oriented architecture, using the modified seven-parameter Bursa coordinate transformation algorithm. The usefulness and effectiveness of this model is proved in the construction of Digital Taizhou geographical spatial framework.

Sixteen years ago, Franklin estimated that about 80% of data contain geo-referenced information. To date, the availability of geographic data and information is growing, together with the capacity of users to operate with IT tools and instruments. Spatial data infrastructures are growing and allow a wide number of users to rely on them. This growth has not been fully coupled to an increase of knowledge to support spatial decisions. Spatial analytical techniques, geographical analysis and modelling methods are therefore required to analyse data and to facilitate the decision process at all levels. Old geographical issues can find an answer thanks to new methods and instruments, while new issues are developing, challenging researchers towards new solutions. This volume aims to contribute to the development of new techniques and methods to improve the process of knowledge acquisition. The Geocomputational expression is related to the development and the application of new theories, methods and tools in order to provide better solutions to complex geographical problems. The geocomputational analysis discussed in this volume, could be classified according to three main domains of applications; the first one related to spatial decision support system and to spatial uncertainty, the second connected to artificial intelligence, the third based on all spatial statistics techniques.