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REPRESENTING UNCERTAINTY IN VISUALISATIONS OF FUTURE LANDSCAPES

K. Appleton*, A. Lovett, T. Dockerty, G. Sünnenberg

Zuckerman Institute for Connective Environmental Research, School of Environmental Sciences,

University of East Anglia, Norwich, NR4 7TK, UK

+44 1603 591095 k.appleton@https://www.sodocs.net/doc/0015834578.html, https://www.sodocs.net/doc/0015834578.html,/zicer/ssevrel

KEY WORDS: GIS, visualization, understanding, landscape, decision support, photo-realism, agriculture

ABSTRACT:

The use of computer-based visualisations to depict landscape futures is increasingly common in environmental decision-making, particularly where information must be communicated to a wide and non-expert audience. Continuing technological development makes it possible to use real-world data as a base for such visualisations, with modifications made to the landscape data as appropriate depending on the scenario to be illustrated. The effects of factors such as the level of realism in the images produced and the capability for viewer interaction with the scene are beginning to be addressed by researchers, but relatively little attention has been given to the representation of uncertainty. The complexity of environmental systems means that uncertainty always exists to some degree when illustrating possible or proposed future states for any landscape, and it is essential that this be communicated to stakeholders, decision-makers and other audiences. Cartographic techniques exist and have been used to illustrate error and other quality issues relating to spatial data, but there has been no large-scale application to landscape visualisation. Options for communicating levels of uncertainty are being explored using a study area in Norfolk, in the eastern part of the UK. This work first considers sources of uncertainty; it then moves on to examine various illustration methods in terms of technical feasibility and data needs as well as audience reaction and perception. In helping visualisation producers to illustrate uncertainty in the clearest and most accessible way, the results of the study have the potential to enhance environmental decision-making processes. KURZFASSUNG:

Computerbasierende Visualisierungen von zukünftigen Landschaftszust?nden werden immer h?ufiger eingesetzt, um Umweltentscheidungen zu treffen, insbesondere, wenn Informationen mit einem breiten Laienpublikum ausgetauscht werden müssen. Die fortschreitende technologische Entwicklung macht es m?glich Real-World-Daten für solche Visualisierungen einzusetzen, wobei entsprechende Modifizierungen der Landschaftsdaten in Abh?ngigkeit von den darzustellenden Szenarien vorgenommen werden. Wissenschaftler beginnen damit Auswirkungen von Faktoren wie Realit?tsn?he der erzeugten Bilddaten und die F?higkeit der Interaktion der Betrachter mit diesen Bildern zu untersuchen. Wenig Aufmerksamkeit wurde dabei bisher der Darstellung von Unsicherheiten gewidmet. Aufgrund der Komplexit?t von Umweltsystemen bestehen bei der Darstellung von m?glichen oder vorgeschlagenen Landschaftszust?nden immer zu einem gewissen Grad Unsicherheiten und es ist unabdingbar, dass diese Interessenvertretern, Entscheidungstr?gern und anderen Zielgruppen verdeutlicht werden. Kartografische Verfahren existieren und werden genutzt, um Fehler oder andere Qualit?tsgesichtspunkte in Bezug auf r?umliche Daten darzustellen, aber es erfolgte bisher kein gro?er Einsatz in Bereich der Landschaftsvisualisierungen. M?glichkeiten zur Verdeutlichung der bestehenden Unsicherheiten werden bezogen auf einen Untersuchungsraum in Norfolk, im ?stlichen Teil von England, erforscht. Diese Arbeit berücksichtigt zun?chst die Quellen von Unsicherheiten; darauf aufbauend werden verschiedene Illustrationsmethoden in Abh?ngigkeit von technischer Umsetzbarkeit und erforderlichen Daten sowie die entsprechenden Reaktionen und Wahrnehmungen der Zielgruppe untersucht. Durch den Einsatz der Ergebnisse der Studie k?nnen Unsicherheiten in einer klaren und h?chst zug?nglichen Form dargestellt werden und so zu einer Verbesserung von Umweltentscheidungsprozessen führen.

1. INTRODUCTION

The environmental decision-making process increasingly involves the visualisation of spatial data in one form or another. In particular, landscape visualisation is recognised as a useful tool for communicating large amounts of environmental information, especially when working with the non-expert groups now beginning to participate in decision-making (Craig, 2002). Recent example applications of such work in the UK include the presentation of coastal management options (Jude et al, 2002) and the impacts of climate change on rural landscapes (Lovett et al, 2003).

However, as visualisation and display methods become more sophisticated, users have control over an increasing number of aspects of the output, such as lighting, the colour and texture of surfaces and objects, and the opportunity to add elements such as labels and overlays. Attention is therefore beginning to turn away from merely representing the visible landscape and towards adding value to that representation by including more subtle and complex factors such as uncertainty.

2. UNCERTAINTY IN ENVIRONMENTAL

DECISION-MAKING

Environmental decision-making processes naturally deal with predictions or proposals relating to future environmental states. Some level of ambiguity is inevitable in any future scenario due to the combination of various sources of uncertainty, including natural environmental variability, data uncertainty, knowledge uncertainty, and model uncertainty (Willows & Connell, 2003: 49). Our understanding of natural systems is imperfect; to this is added the impossibility of completely predicting human actions and reactions on either an individual or collective scale, and issues of measurement including accuracy and precision.

Multiplication of these error sources means that any visualised landscape can only ever be said to be a possible future, one of a great number of alternatives.

Although there is a desire to reduce uncertainty, stemming from a end-users' and decision-makers' demands for information that is certain (Foody & Atkinson, 2002), it is clear that it can never be eliminated. We must therefore find ways to communicate scientific uncertainty in a clear and honest way, particularly when dealing with the public, so that the most appropriate decisions are made. However, care must also be taken not to overwhelm the viewer by adding excessive or overly complicated information about uncertainty – the purpose of the visualisation is, after all, to increase the accessibility of environmental information. To date, little research has been done to address this issue.

Previous work with planning professionals (Appleton & Lovett, in press) has suggested that uncertainty should be carefully considered when visualising a scene for the environmental decision-making process, in order that viewers’ (and managers’) time is not wasted in commenting on uncertain or representative aspects of a landscape future as if they were certain and specific. For example, showing an visualisation of a future rural scene which includes wind turbines may elicit detailed comments on their size and location, when in fact they were only included as an indication of increased reliance of renewable energy in the scenario shown. Furthermore, futures which appear more certain than they really are may be accepted by the audience as a foregone conclusion rather than questioned, explored, and commented on.

One potential barrier to finding a simple and effective way to represent uncertainty is that uncertainty itself is very difficult to define. For example, we could attach particular level of uncertainty to a landscape as a whole, or to the various elements within that landscape; we could be trying to show that one outcome is simply more or less likely than another given certain conditions, or we may have more quantitative information on the overall probability of different futures. We may be asking for preferences or suggestions relating to choices about the landscape, or trying to communicate the consequences of different courses of action that the viewer could take. Exactly what is to be communicated will necessarily affect the suitability of the various methods detailed in the following section.

It may even be that the passive presentation of uncertainty is not enough for some uses. Couclelis (1992) urges that data quality information should grab the attention, forcing the viewer to assess quality, although such methods could prove confusing for the type of non-expert audiences that visualisation is usually intended to bring into the decision-making process.

It is worth noting that to some extent, anecdotally at least, where visualisation technology is capable of creating highly detailed images these capabilities are used without question in the belief that “more realistic” visualisations are “better”. However, it is arguable that the “best” visualisations are in fact those that give the viewer the truest picture of the future in question – scientific uncertainty included. It may be, therefore, that detail and visual realism should not be the overriding concerns, and careful attention should be paid to the overall message conveyed by a visualisation. One thing is clear – a visualisation cannot simply be presented as a fait accompli if the audience is to make meaningful decisions based upon it. Somehow, the idea that a future landscape is just one possibility must be communicated if more participatory environmental decision-making is to fulfil its potential.

3. PREVIOUS WORK

Perhaps the simplest ways of expressing uncertainty relate to numerical information, where it can be conveyed using error margins and ranges rather than single figures (although these too have their problems (Gigerenzer, 2002)). Such methods are, however, largely inappropriate for visual and/or spatial data where there is a large variation in uncertainty over the dataset. Research has therefore also examined ways of showing data quality and error when using 2D or 3D cartographic methods; indeed, uncertainty as a whole has been an important research topic in geographical information science for the past decade (Atkinson & Foody, 2002). There are broadly three strands of research relating to uncertainty in geographical information: reducing uncertainty, characterising sources of uncertainty, and providing information to users about the uncertainty of a particular dataset. It is the latter of these that provides information relevant to the work described here, but as stated by Hunter & Goodchild (1996), that investigation of the presentation of uncertainty could still be said to be sparse. This is particularly true for the field of landscape visualisation, due to its relative newness.

Existing literature mostly focuses on error in spatial databases, and usually includes separate maps of error (for example a colour gradation) to overlay or combine with the original data in some way. A common example is 3D draping, whereby a surface is generated to represent a variable over a certain space, with that surface then coloured according to the error at each location. Cartographic techniques lend themselves to variation in representation, e.g. point clouds, thick or braided lines, or fuzzy features to represent uncertainty of location (Hunter & Goodchild, 1996). As well as visual methods, there are suggestions of animation and sound-based solutions, but they seem to be little used in practice so far (Beard et al., 1991; Krygier, 1994). The applicability of these tools to current landscape visualisation methods is discussed in Section 5.

It is also important to note that there is still little guidance as to how a given degree of uncertainty might be inferred from the use of a particular technique. Visual techniques are generally indicative rather than quantitative, giving users information quickly, but not necessarily clearly (Hunter & Goodchild, 1996).

4. CURRENT TOOLS FOR VISUALISATION

The way in which uncertainty is represented in any landscape visualisation is governed to some extent by the choice of visualisation method and output type.

Still images offer the greatest levels of realism, as there is no limit on the time taken to draw a scene – it may take several hours to render, but once completed it can be shown many times. One example of software that produces this sort of output is Visual Nature Studio (3DNature, 2004) Interactive methods are still behind in terms of the detail that can be shown (although the gap is narrowing as available computing power increases), as their content needs to be rendered many times a second to keep up the illusion of movement through a scene. For instance, the terrain modelling software TerraVista (Terrex, 2004) can be used to produce models which can be explored in

realtime with a joystick or mouse. Animations may be derived from either method, by compiling a sequence of stills or by recording an interactive navigation.

Portability is another important issue for many visualisation methods. Simple still images can easily be reproduced and distributed in a variety of ways, but cannot be modified, for example to remove the uncertainty information. Interactive methods, and some animation techniques, sometimes involve the use of particular applications or plug-ins (e.g. Audition for OpenFlight realtime models, or Flash for multimedia content on the internet), which may allow customisation and thereby the addition of some degree of user control. Such methods, however, limit the distribution and use of visualisations to those with the appropriate computer software, hardware, and possibly network connections, which may go against the desire to open up decision-making to a wider audience.

5. POTENTIAL METHODS FOR REPRESENTING UNCERTAINTY IN LANDSCAPE VISUALISATION Many of the techniques in Section 3 do not immediately seem to be directly applicable to the issue of uncertainty in landscape visualisation, as they can conflict with the needs of the visualisation itself. That is, changes in colour, sharpness or position in an effort to show uncertainty may be interpreted as a change in real life rather than an attempt to illustrate non-visual information (Goodchild, 1991). For this reason, altering the appearance of the landscape or objects within it (e.g. changes in colour/size, or multiple instances of items) is potentially unhelpful in any decision-making process, since responses are usually requested based at least partially on the visual appearance of a landscape.

The suggestion that complications may arise from the use of highly detailed visualisations whereby the viewer equates visual detail with certainty of prediction was brought up by planning professionals in previous work (Appleton & Lovett, in press). There, respondents put forward the idea that different elements of the image could be visualised at different levels of detail depending on their certainty. On the other hand, the results of a different research project suggest that viewers find high levels of detail helpful in imagining a visualised landscape (Appleton & Lovett, 2003), implying that it could be unhelpful to alter the level of detail in an effort to express additional information. It was also evident that there may be a “lowest common denominator” effect whereby an image is only as realistic overall as its least detailed element, perhaps throwing doubt on the advisability of varying the detail level to convey uncertainty. This is an interesting disagreement which needs to be investigated in more depth, but it does not mean that visual modifications should be discounted as useful techniques. Methods whereby the user can control the visibility of uncertainty information (perhaps using a sliding scale of transparency or some other transition), or display it in a parallel window, are possible alternatives to the permanent alteration of visualisations, with animation techniques offering the option of similar displays where interactivity is not practical (e.g. with a large audience).

The techniques which would seem to be most suitable for representing uncertainty may be summarised as follows: ?Deliberately creating low-detail visualisations (or elements within them)

?Giving a range of possibilities, if appropriate ?Altering colour, either by adding false colour or desaturating (greying out)

?Blurring

?Written information, either on labels within the image or accompanying text

?Sound

Some of the above techniques may increase the time taken to draw or display a landscape image, by adding an extra dimension of information, which is relatively unimportant for single images, but has more impact on multi-frame animations and could well affect the perceived smoothness of interactive methods by reducing the frame rate. However, interactivity also brings an important extra dimension to most of the above options. Not only does it allow visual methods to be applied without permanently distorting the image, but it could be used to control the level of verbal information displayed, perhaps allowing expert and non-expert viewers to be catered for with the same visualisation. A range of visualisations could be presented in response to the viewer’s choice of scenario or changing of one or more given parameters. Sound-based techniques could respond to the pointing/navigation device used, either representing uncertainty on its own, or being used as an alert device to draw attention to the presence of other uncertainty information. It therefore seems likely that some sort of user interface would be helpful, to allow the viewer to choose the information to be displayed, showing multiple sources at a time either in separate windows or overlaid on one another.

It is important to consider non-interactive methods as well as interactive ones for the reasons mentioned in Section 4. If the decision-making process is to be widened to include a greater cross-section of society, the visual information intended to facilitate their participation must be made available in an accessible way. This does not preclude the use of sophisticated technologies but does mean that alternatives should be considered and investigated.

The audience may also affect the choice of methods in other ways. In reaching out to more non-experts, environmental decision-making must still be useful to expert participants, and their needs and perceptions must be considered. One interesting question is whether environmental professionals might have a greater inherent understanding of uncertainty when presented with an environmental future, and therefore there is less need to draw their attention to its existence and more need to explain its sources and magnitude. Such information may be overwhelming to a non-expert viewer, but could be presented on an on-demand basis as suggested above.

6. WORK SO FAR

Preliminary work is being carried out to assess the technical feasibility of the various options for presenting uncertainty information. This phase is ongoing.

The underlying image (Fig 1) was chosen from a previous project within the research group (Lovett et al, 2003) and shows a rural, agricultural English landscape in the south-east of the country. It is based on Geographical Information Systems (GIS) data, specifically UK Ordnance Survey elevation and land use data, with the land use information being greatly enhanced through fieldwork and other sources to reflect the varieties of crops grown in the area. The GIS information was used with Visual Nature Studio from 3DNature to create a landscape

model, in which various ‘cameras’ were placed; Figure 1 is the view from one such camera.

Figure 1. The base visualisation used.

The image illustrates the landscape as it may look in 20 years’ time under a particular climate change scenario which includes efforts to reduce CO2 emissions (wind turbines, solar panels), changes in crops to reflect differences in climate and water availability, a reduced water level in the stream, and subtle changes to the apparent health of non-crop vegetation (trees and hedges), as well as adaptation and mitigation measures. Uncertainty in this particular future state comes from numerous sources, including the nature and magnitude of climate change, the type and strength of its effects, and the financial, political and social factors driving policy change and the actions of farmers. As yet, no specific levels of uncertainty have been defined, and consequently the modified images are very much exploratory. As shown here, different elements of the landscape are given different levels of uncertainty, for example we may be fairly sure that the stream level will be lowered, but wind turbines are only a vague possibility due to the planning process, a lack of feasibility studies for that precise location, possible local opposition, and so on.

Figure 2. One way in which uncertainty may be shown using colours

Figures 2 and 3 are examples of colour and clarity modifications as applied to the image in Figure 1. Different colours as used in Figure 2 could represent different, and relatively easily distinguishable, levels of certainty, although determining those levels is a challenge in itself. A legend would need to be provided with such an image. It can be seen from Figure 3 that degrees of fuzziness are somewhat difficult to achieve, certainly more so than degrees of colour, and so this technique is probably more suited to expressing general uncertainty over a scenario rather than differentiating between specific parts of a scene. By its nature, blurring is more noticeable on elements which were previously well-defined, such as the wind turbines on the skyline, and less obvious on features which already have a degree of variation edges such as crops.

Figure 3. An example of how blurring may be used to show uncertainty

In terms of technical feasibility, the images were modified in PaintShop Pro v7 (Jasc, 2004) using, respectively, an airbrush tool used on a semi-transparent overlay layer, and a blur filter, and while they were not time-consuming to produce, it is evident that it could prove hard to apply such effects repeatedly with any great degree of consistency. The possibility of applying such effects automatically currently seems remote, although Visual Nature Studio does include a PostProcessing facility which may be of use with certain techniques. Such a capability would be almost essential if creating animations via the compilation route, where hundreds or thousands of frames need to be altered. Post-render colouring of areas of the image according to uncertainty (which could be specified in the underlying GIS database) does not seem to be possible at present, but this may change as the software develops.

Figure 4. A sample layout for an interactive “visualisation toolbox”

Figure 4 shows a purely artistic mock-up of a possible “visualisation toolbox” which could be given to viewers to allow them to toggle different representations on and off, change viewpoints, or otherwise interact with the display. While technology already exists to put together user-selectable displays of still images and other information (such as the web-browser based system being used in the K?nigslutter area in Germany (Warren-Kretzschmar & Van Haaren, 2004)), a working toolbox for interactive visualisations would need to be

programmed from scratch, which is beyond the current capabilities of the research group.

7. CONCLUSIONS AND FUTURE DIRECTIONS

It is clear that there are a number of areas ripe for research in this field, beginning with the definition of uncertainty as it relates to visualised landscapes, moving through the technical feasibility of the various options for representing it (which will need to be revisited as technology develops), to the perceptions and understanding of various groups of end-users when faced with such visualisations. The first of these is vital if subsequent work is to be carried out within a valid context, and to a large extent will influence the sorts of images produced for investigation.

It would seem sensible to begin with easily-modified still visualisations in order to keep processing time to a minimum as viewers’ perceptions are explored. By employing qualitative as well as quantitative research methods, further insight and ideas could be obtained from respondents; ideally such results would be used to inform and direct research into more complex animated or interactive methods. To these ends, the next steps of this project will be to carry out preliminary surveys of non-expert respondents to elicit their responses to these and other simple expressions of uncertainty in visualised landscapes.

The eventual aim of this research is to feed back into the visualisation process as it is used in real-life applications. In helping visualisation producers to illustrate uncertainty in the clearest and most accessible way, this work has the potential to improve the communication of landscape-related information and thereby enhance the environmental decision-making process.

REFERENCES

3DNature, 2004. Visual Nature Studio.

https://www.sodocs.net/doc/0015834578.html, (accessed 26th April 2004). Appleton, K., and Lovett, A., in press. Computer visualisation of planning proposals - comments from local authority planners. Computers, Environment and Urban Systems.

Appleton, K., and Lovett, A., 2003. GIS-based visualisation of rural landscapes: defining 'sufficient' realism for environmental decision-making. Landscape & Urban Planning, 65 (3), pp. 117-131.

Atkinson, P. M., Foody, G. M., 2002. Uncertainty in remote sensing and GIS: fundamentals. In: Foody, G. M., Atkinson, P. M., (Eds.) Uncertainty in Remote Sensing and GIS. Chichester, Wiley.

Craig, W. J., Harris, T. M. and Weiner, D., 2002. Community Participation and Geographic Information Systems. London, Taylor and Francis.

Couclelis, H., 1992. Geographic knowledge production through GIS: towards a model for quality monitoring.

https://www.sodocs.net/doc/0015834578.html,/Publications/Tech_Reports/92/92-12.PDF (accessed 26th April 2004).

Ehlschlaeger, C. R., Shortridge, A. M., and Goodchild, M. F., 1997. Visualizing spatial data uncertainty using animation. Computers & Geosciences, 23 (4), pp.387-395. Foody, G. M., Atkinson, P. M., 2002. Current status of uncertainty issues in remote sensing and GIS. In: Foody, G. M., Atkinson, P. M., (Eds.) Uncertainty in Remote Sensing and GIS. Chichester, Wiley.

Gigerenzer, G., 2002. Reckoning With Risk: Learning to Live with Uncertainty. London, Allen Lane.

Goodchild, M. F., 1991. Issues of quality and uncertainty. In Muller, J. C., (Ed.) Advances in Cartography. New York, Elsevier.

Hunter, G. J., 1991. New toold for handling spatial data quality: moving from academic concepts to practical reality. Presented at URISA ’99, Chicago, USA.

Hunter, G. J., and Goodchild, M. F., 1996. Communicating uncertainty in spatial databases. Transactions in GIS, 1 (1), pp. 13-25.

Jasc Software, 2004. PaintShop Pro. https://www.sodocs.net/doc/0015834578.html, (accessed 26th April 2004).

Jude, S., Jones, A.P., Andrews, J.E. and Bateman, I.J., 2002. Visualising Future Coastal Landscapes. Proceedings of the GIS Research UK 10th Annual Conference - GISRUK 2002, University of Sheffield, UK. pp.141-144.

Krygier, J., 1994. Sound and Cartographic Visualization. In: Taylor, D, and MacEachren, A., (Eds.) Visualization in Modern Cartography. Oxford, Pergamon.

Lovett, A., Dockerty, T., Appleton, K., Sünnenberg, G., 2003. GIS-based visualisation of potential climate change impacts on rural landscapes. Presented at Geocomputation 2003, University of Southampton, UK.

Terrex, 2004. TerraVista. https://www.sodocs.net/doc/0015834578.html, (accessed 26th April 2004).

Warren-Kretzschmar, B., van Haaren, C., 2004. Online Landscape Planning – What does it take? A case study in

K?nigslutter am Elm. Presented at Online Landscape Architecture: The 5th International Conference on Information Technologies in Landscape Architecture, Anhalt University of Applied Science, Dessau, Germany.

Willows, R. I., Connell, R. K., (Eds.) 2003. Climate adaptation: risk, uncertainty and decision-making. UKCIP Technical Report. Oxford, UKCIP

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Join In剑桥小学英语(改编版)入门阶段Unit 1Hello,hello第1单元嗨,嗨 and mime. 1 听,模仿 Stand up Say 'hello' Slap hands Sit down 站起来说"嗨" 拍手坐下来 Good. Let's do up Say 'hello' Slap hands Sit down 好. 我们来再做一遍.站起来说"嗨"拍手坐下来 the pictures. 2 再听一遍给图画编号. up "hello" hands down 1 站起来 2 说"嗨" 3 拍手 4 坐下来说 3. A ,what's yourname 3 一首歌嗨,你叫什么名字 Hello. , what's yourname Hello. Hello. 嗨. 嗨. 嗨, 你叫什么名字嗨,嗨. Hello, what's yourname I'm Toby. I'm Toby. Hello,hello,hello.嗨, 你叫什么名字我叫托比. 我叫托比 . 嗨,嗨,嗨. I'm Toby. I'm Toby. Hello,hello, let's go! 我是托比. 我是托比. 嗨,嗨, 我们一起! Hello. , what's yourname I'm 'm Toby. 嗨.嗨.嗨, 你叫什么名字我叫托比.我叫托比. Hello,hello,hello. I'm 'm Toby. Hello,hello,let's go! 嗨,嗨,嗨.我是托比. 我是托比. 嗨,嗨,我们一起! 4 Listen and stick 4 听和指 What's your name I'm Bob. 你叫什么名字我叫鲍勃. What's your name I'm Rita. What's your name I'm Nick. 你叫什么名字我叫丽塔. 你叫什么名字我叫尼克. What's your name I'm Lisa. 你叫什么名字我叫利萨. 5. A story-Pit'sskateboard. 5 一个故事-彼德的滑板. Pa:Hello,Pit. Pa:好,彼德. Pi:Hello,:What's this Pi:嗨,帕特.Pa:这是什么 Pi:My new :Look!Pi:Goodbye,Pat! Pi:这是我的新滑板.Pi:看!Pi:再见,帕特! Pa:Bye-bye,Pit!Pi:Help!Help!pi:Bye-bye,skateboard! Pa:再见,彼德!Pi:救命!救命!Pi:再见,滑板! Unit 16. Let's learnand act 第1单元6 我们来边学边表演.

Join In剑桥小学英语.doc

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公司注册登记流程(四证)

→客户提供：场所证明租赁协议身份证委托书三张一寸相片 →需准备材料：办理税务登记证时需要会计师资格证与财务人员劳动合同 →提交名称预审通知书→公司法定代表人签署的《公司设立登记申请书》→全体股东签署的《指定代表或者公共委托代理人的证明》（申请人填写股东姓名）→全体股东签署的公司章程（需得到工商局办事人员的认可）→股东身份证复印件→验资报告（需到计师事务所办理：需要材料有名称预审通知书复印件公司章程股东身份证复印件银行开具验资账户进账单原件银行开具询证函租赁合同及场所证明法人身份证原件公司开设临时存款账户的复印件）→任职文件（法人任职文件及股东董事会决议）→住所证明（房屋租赁合同）→工商局（办证大厅）提交所有材料→公司营业执照办理结束 →需带材料→公司营业执照正副本原件及复印件→法人身份证原件→代理人身份证→公章→办理人开具银行收据交款元工本费→填写申请书→组织机构代码证办

理结束 →需带材料→工商营业执照正副本复印件原件→组织机构正副本原件及复印件→公章→公司法定代表人签署的《公司设立登记申请书》→公司章程→股东注册资金情况表→验资报告书复印件→场所证明（租赁合同）→法人身份证复印件原件→会计师资格证（劳动合同）→税务登记证办理结束 →需带材料→工商营业执照正副本复印件原件→组织机构正副本原件及复印件→税务登记证原件及复印件→公章→法人身份证原件及复印件→代理人身份证原件及复印件→法人私章→公司验资账户→注以上复印件需四份→办理时间个工作日→办理结束 →需带材料→工商营业执照正副本复印件原件→组织机构正副本原件及复印件→公章→公司法定代表人签署的《公司设立登记申请书》→公司章程→股东注册资金情况表→验资报告书复印件→场所证明（租赁合同）→法人身份证复印件原件→会计师资格证（劳动合同）→会计制度→银行办理的开户许可证复印件→税务登记证备案办理结束

(完整版)剑桥小学英语Joinin六年级复习题(二)2-3单元.doc

2011-2012 学年度上学期六年级复习题（Unit2-Unit3 ）一、听力部分 1、听录音排序 ( ) () ()() () 2、听录音，找出与你所听到的单词属同一类的单词（） 1. A. spaceman B. pond C . tiger （） 2. A.mascots B. potato C . jeans （） 3. A. door B. behind C . golden （） 4. A. sometimes B. shop C . prince （） 5. A. chair B. who C . sell 3、听录音，将下面人物与他的梦连线 Sarah Tim Juliet Jenny Peter 4、听短文，请在属于Mr. Brown的物品下面打√ ( ) ( ) ( ) ( ) ( ) ( ) ( ) 5、听问句选答句（） 1. A. Yes, I am B. Yes, I have C . Yes, you do （） 2. A.Pink B. A friendship band C . Yes. （） 3. A. OK B. Bye-bye. C . Thanks, too. （） 4. A. Monday. B. Some juice. C . Kitty. （） 5. A. I ’ve got a shookbag. B. I ’m a student. C . It has got a round face. 6、听短文，选择正确答案（） 1. Where is Xiaoqing from? She is from . A.Hebei B. Hubei C . Hunan （） 2. She goes to school at . A.7:00 B.7:30 C . 7:15 （） 3. How many classes in the afternoon? classes. A. four B. three C . two （） 4. Where is Xiaoqing at twelve o ’clock? She is . A. at home B. at school C .in the park （） 5. What does she do from seven to half past eight? She . A.watches TV B. reads the book C. does homework

三年级下学期英语(Joinin剑桥英语)全册单元知识点归纳整理-

Starter Unit Good to see you again知识总结一. 短语 1. dance with me 和我一起跳舞 2. sing with me 和我一起唱歌 3. clap your hands 拍拍你的手 4. jump up high 高高跳起 5.shake your arms and your legs晃晃你的胳膊和腿 6. bend your knees 弯曲你的膝盖 7. touch your toes 触摸你的脚趾8. stand nose to nose鼻子贴鼻子站二. 句子 1. ---Good morning. 早上好。 ---Good morning, Mr Li. 早上好，李老师。 2. ---Good afternoon. 下午好。 ---Good afternoon, Mr Brown. 下午好，布朗先生。 3. ---Good evening，Lisa. 晚上好,丽莎。 ---Good evening, Bob. 晚上好，鲍勃。 4. ---Good night. 晚安。 ----Good night. 晚安。 5. ---What’s your name? 你叫什么名字？ ---I’m Bob./ My name is Bob. 我叫鲍勃。 6. ---Open the window, please. 请打开窗户。 ---Yes ,Miss. 好的，老师。 7. ---What colour is it? 它是什么颜色？它是蓝红白混合的。 ---It’s blue, red and white. 皮特的桌子上是什么？ 8. ---What’s on Pit’s table? ---A schoolbag, an eraser and two books. 一个书包，一个橡皮和两本书。 9. ---What time is it? 几点钟？两点钟。 ---It’s two. 10.---What’s this? 这是什么？ ---My guitar. 我的吉他。

JOININ英语三年级下册知识点

JOIN IN英语三年级下册 Start unit 1 Words morning afternoon evening night 2 Sentences Good morning !早上好Good afternoon ！下午好Good evening！晚上好 Good night！晚安 3 Phrases clap your hands 拍拍手 jump up high 往高跳 shake your arms and your legs 晃动你的胳膊和腿 bend your knees 弯曲你的膝盖 touch your toes 摸摸你的脚指 stand nose to nose 鼻子对鼻子站着 Unit 1 Pets 1 Words cat猫dog狗bird 鸟mouse老鼠fish鱼rabbit 兔子frog青蛙hamster仓鼠 budgie鹦鹉tiger老虎monkey 猴子panda熊猫giraffe 长颈鹿elephant 大象bear 熊run跑sit坐fly飞swim游泳roar吼叫eat吃 2 Grammar

★名词的复数：一般在词尾直接加s,不规则变化要牢记: fish-----fish mouse------mice 3 Sentences 1.Have you got a pet ? 你有宠物吗？ Yes ,I have. 是的,我有。/No, I haven’t. 不，我没有。 2.What have you got ? 你有什么宠物吗？I’ve got a dog . / A dog. 我有一只狗。 3.What colour is the cat ? 你的猫是什么颜色的？It’s black. 它是黑色的。 What is wizard’s pet? 巫师的宠物是什么？ 4.What is it ? 它是什么? It’s a rabbit .它是只兔子。 5.How many budgies /mice are there? 这里有多少只鹦鹉/老鼠？ There are + 数字budgies/mice. 这里有------只鹦鹉/老鼠。 6.Fly like a budgie. 像鹦鹉一样飞。Run like a rabbit. 像兔子一样跑。 Swim like a fish. 像鱼一样游泳。Eat like a hamster. 像仓鼠一样吃东西。 Sit like a dog. 像狗一样坐。Roar like a tiger. 像老虎一样吼叫。 7.What are in the pictures. 图片里面是什么？Animals. 动物。 8. What animals? 什么动物？ 9.How many pandas (elephants /bears/ giraffes/ monkeys/ budgies) are there?有多少.？ How many + 可数名词的复数形式 Unit 2 The days of the week

外研社剑桥小学英语Join_in四年级上册整体课时教案

外研社剑桥小学英语Join_in四年级上册整体课时教案Starter Unit Let's begin 第一学时: The pupils learn to understand: How are you today? I’m fine/OK. Goodbye. The pupils learn to use: What’s your name? I’m (Alice). How are you? I’m fine. I’m OK. Activities and skills: Decoding meaning from teacher input. Using phrases for interaction in class. Greeting each other. I Introduce oneself. Asking someone’s mane. Teaching process: Step 1: Warm—up. Sing a song. Hello, what’s your name? Step 2: Presentation. 1. What’s your name? I’m…. (1) The teacher introduces herself, saying: Hello, /Good morning, I’m Miss Sun. (2) Asks a volunteer’s name:

What’s your name? The teacher prompt the pupil by whispering, I’m (Alice). (3) Asks all the pupils the same question and help those who need it by whispering I’m…. 2. How are you today? I’m fine. I’m OK. (1) The teacher explains the meaning of How are you today? (2) Tell the class to ask the question all together and introduce two answers, I’m (not very) fine/I’m Ok. (3) Asks all the pupils the same question and make sure that all the students can reply. Step3: Speak English in class. 1. Tells the pupils to open their books at page3, look at the four photographs, and listen to the tape. 2. Asks the pupils to dramatise the situations depicted in the four photographs. A volunteer will play the part of the parts of the other pupils, answering as a group. 第二学时: The pupils learn to use these new words: Sandwich, hamburger, hot dog, puller, cowboy, jeans, cinema, walkman, snack bar, Taxi, clown, superstar.

剑桥英语JOININ三年级上册知识点(供参考)

Join in三年级上册知识点 1.见面打招呼 Hello! = Hi! 你好 Good morning! 早上好！ Good afternoon! 下午好！ Good evening! 晚上好！ (注意：Good night的意思是“晚安”) 2.询问身体健康状况 How are you? 你好吗？ I’m fine. = I’m OK.我很好。 3.临走分别 Goodbye. = Bye bye. 再见。 See you tomorrow. 明天见。 4.Miss小姐Mr先生 5.询问名字 -What’s your name? 你叫什么名字？ -I’m Toby. = My name is Toby. = Toby.我叫托比。 6.Let’s+动词原形如Let’s go! 我们走吧！ Let’s begin! 我们开始吧！ 7. Are you ready? 你准备好了吗？ I’m ready. 我准备好了。 8.十二个动词 look看listen听mime比划着表达speak讲read读write写draw画colour涂sing唱think想guess猜play玩9.询问物体 -What’s this? 这是什么？ -It’s my dog. / It’s a dog. / My dog. 这是我的狗。 10.my/your+名词如my apple我的苹果 your apple你的苹果 11.数字0-10 zero零one一two二three三four四five五six六seven七eight八nine九ten十12.询问电话号码 -What’s your phone number? 你的电话号码是多少？ -It’s . / . 13.guess sth 猜东西如Guess the number. 猜数字 14. What’s in the box? 盒子里有什么？ 15.询问数量 -How many? 多少？ -Nine. 九个。 16. Here is / are sth. 这是……

代理公司注册登记协议书简易版

It Is Necessary To Clarify The Rights And Obligations Of The Parties, To Restrict Parties, And To Supervise Both Parties To Keep Their Promises And To Restrain The Act Of Reckless Repentance. 编订：XXXXXXXX 20XX年XX月XX日代理公司注册登记协议书简易版

代理公司注册登记协议书简易版温馨提示：本协议文件应用在明确协议各方的权利与义务、并具有约束力和可作为凭证，且对当事人双方或者多方都有约制性，能实现监督双方信守诺言、约束轻率反悔的行为。文档下载完成后可以直接编辑，请根据自己的需求进行套用。代理公司注册登记协议书甲方：_________ 地址：_________ 电话：_________ 联系人：_________ 乙方：_________ 地址：_________ 电话：_________ 联系人：_________ 为了充分发挥_________的资源和信息服务优势，甲、乙双方经过友好协商，本着平等互利、友好合作的意愿达成本协议书，并郑重声

明共同遵守：一、甲方同意按照本协议的规定，授权乙方为其代办公司注册手续。二、乙方提供的代办咨询服务范围仅限如下： 1.为甲方代办工商营业执照、组织机构代码证、税务证、三章(法人章、公章、财务章); 2.约定的其他服务： _________。三、甲方的责任： 1.甲方应指定专人配合乙方完成新企业工商登记注册等事务，并提供齐全的证件和规范的法律文件资料。 2.甲方对提供的证件和法律文件资料的真实性、正确性、合法性承担全部责任。