Data about community Data about a community starts to tell something or real importance ... and it becomes possible to see what are the factors that have resulted in the state of the community. If something in the data is surprising ... data at the community level helps to pin-point what caused this and why and how this came about.
It is much easier at the community level to walk around and get to know what is going on ... especially the important things that affect the place ... and to see things that may be important but being ignored in the collection of data.
Getting little pieces of information about the community makes it possible to start to do an accounting using the TVM framework ... and with this it starts to be possible to have transparency and accountability.
Data about neighborhood or block. Some communities are too complex to be easy to understand ... in which case the neighborhood may be a better level for detailed data. Common sense applies.
In some cases it may be appropriate to get data at the block level. In high density urban settings, the block may still be quite a large population, and the economic activities quite complex.
State and Activities. TVM aims to put into the record everything that is important about the community. These data are of two types: (1) data that informs about the state of the community ... its resources and its constraints; and, (2) data that informs about the activities and the productivity of the community. These data are also of two characteristics: (1) data that are easy to obtain and at little cost; and, (2) data that are much more difficult to obtain and requiring considerable effort and cost. Some of these data are fairly stable over time, some change rapidly over time ... some data apply to all the area, some data relate to a very specific place within the broader area.
Information about activities is usually more difficult, and especially to get all the data that are needed.
The data usually have more value when they are part of a time series ... what was the equivalent data for a past period ... and what is the situation now, and what will it be in the future.
TVM uses averages to measure progress ... to measure change ... but not to understand the data. The key is to know what goes into making the average and to understand what can be done to eliminate what is bad and to enhance what is good. When this is done, the average changes ... but trying to change the average without understanding its components is a waste of resource and energy!